[{"file":[{"file_name":"2020_CambridgeUniversityPress_Ferber.pdf","date_updated":"2021-06-22T09:23:59Z","success":1,"access_level":"open_access","content_type":"application/pdf","checksum":"5553c596bb4db0f38226a56bee9c87a1","file_size":601516,"creator":"asandaue","relation":"main_file","date_created":"2021-06-22T09:23:59Z","file_id":"9584"}],"tmp":{"short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png"},"author":[{"last_name":"Ferber","full_name":"Ferber, Asaf","first_name":"Asaf"},{"id":"5fca0887-a1db-11eb-95d1-ca9d5e0453b3","first_name":"Matthew Alan","last_name":"Kwan","orcid":"0000-0002-4003-7567","full_name":"Kwan, Matthew Alan"}],"oa_version":"Published Version","external_id":{"pmid":["1907.06744"]},"month":"11","_id":"9583","extern":"1","date_published":"2020-11-03T00:00:00Z","doi":"10.1017/fms.2020.29","intvolume":"         8","publication":"Forum of Mathematics","oa":1,"publication_status":"published","quality_controlled":"1","type":"journal_article","file_date_updated":"2021-06-22T09:23:59Z","publication_identifier":{"eissn":["2050-5094"]},"language":[{"iso":"eng"}],"article_processing_charge":"No","title":"Almost all Steiner triple systems are almost resolvable","pmid":1,"article_number":"e39","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","publisher":"Cambridge University Press","date_updated":"2023-02-23T14:01:48Z","ddc":["510"],"year":"2020","date_created":"2021-06-22T09:12:23Z","volume":8,"status":"public","article_type":"original","scopus_import":"1","has_accepted_license":"1","citation":{"apa":"Ferber, A., &#38; Kwan, M. A. (2020). Almost all Steiner triple systems are almost resolvable. <i>Forum of Mathematics</i>. Cambridge University Press. <a href=\"https://doi.org/10.1017/fms.2020.29\">https://doi.org/10.1017/fms.2020.29</a>","chicago":"Ferber, Asaf, and Matthew Alan Kwan. “Almost All Steiner Triple Systems Are Almost Resolvable.” <i>Forum of Mathematics</i>. Cambridge University Press, 2020. <a href=\"https://doi.org/10.1017/fms.2020.29\">https://doi.org/10.1017/fms.2020.29</a>.","ama":"Ferber A, Kwan MA. Almost all Steiner triple systems are almost resolvable. <i>Forum of Mathematics</i>. 2020;8. doi:<a href=\"https://doi.org/10.1017/fms.2020.29\">10.1017/fms.2020.29</a>","ista":"Ferber A, Kwan MA. 2020. Almost all Steiner triple systems are almost resolvable. Forum of Mathematics. 8, e39.","short":"A. Ferber, M.A. Kwan, Forum of Mathematics 8 (2020).","ieee":"A. Ferber and M. A. Kwan, “Almost all Steiner triple systems are almost resolvable,” <i>Forum of Mathematics</i>, vol. 8. Cambridge University Press, 2020.","mla":"Ferber, Asaf, and Matthew Alan Kwan. “Almost All Steiner Triple Systems Are Almost Resolvable.” <i>Forum of Mathematics</i>, vol. 8, e39, Cambridge University Press, 2020, doi:<a href=\"https://doi.org/10.1017/fms.2020.29\">10.1017/fms.2020.29</a>."},"day":"03","abstract":[{"text":"We show that for any n divisible by 3, almost all order-n Steiner triple systems admit a decomposition of almost all their triples into disjoint perfect matchings (that is, almost all Steiner triple systems are almost resolvable).","lang":"eng"}],"license":"https://creativecommons.org/licenses/by/4.0/"},{"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/3.0/legalcode","image":"/images/cc_by.png","short":"CC BY (3.0)","name":"Creative Commons Attribution 3.0 Unported (CC BY 3.0)"},"file":[{"file_name":"2020_JournalOfComputationalGeometry_Edelsbrunner.pdf","date_updated":"2021-08-11T11:55:11Z","checksum":"f02d0b2b3838e7891a6c417fc34ffdcd","access_level":"open_access","content_type":"application/pdf","success":1,"creator":"asandaue","file_size":1449234,"file_id":"9882","relation":"main_file","date_created":"2021-08-11T11:55:11Z"}],"department":[{"_id":"HeEd"}],"oa_version":"Published Version","author":[{"orcid":"0000-0002-9823-6833","full_name":"Edelsbrunner, Herbert","last_name":"Edelsbrunner","first_name":"Herbert","id":"3FB178DA-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Virk","full_name":"Virk, Ziga","id":"2E36B656-F248-11E8-B48F-1D18A9856A87","first_name":"Ziga"},{"full_name":"Wagner, Hubert","last_name":"Wagner","first_name":"Hubert","id":"379CA8B8-F248-11E8-B48F-1D18A9856A87"}],"publication_status":"published","quality_controlled":"1","publication":"Journal of Computational Geometry","intvolume":"        11","oa":1,"_id":"9630","date_published":"2020-12-14T00:00:00Z","doi":"10.20382/jocg.v11i2a7","month":"12","file_date_updated":"2021-08-11T11:55:11Z","language":[{"iso":"eng"}],"publication_identifier":{"eissn":["1920180X"]},"issue":"2","type":"journal_article","ddc":["510","000"],"date_updated":"2021-08-11T12:26:34Z","publisher":"Carleton University","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","article_processing_charge":"Yes","title":"Topological data analysis in information space","page":"162-182","status":"public","project":[{"_id":"0aa4bc98-070f-11eb-9043-e6fff9c6a316","grant_number":"I4887","name":"Discretization in Geometry and Dynamics"}],"article_type":"original","acknowledgement":"This research is partially supported by the Office of Naval Research, through grant no. N62909-18-1-2038, and the DFG Collaborative Research Center TRR 109, ‘Discretization in Geometry and Dynamics’, through grant no. I02979-N35 of the Austrian Science Fund (FWF).","volume":11,"year":"2020","date_created":"2021-07-04T22:01:26Z","has_accepted_license":"1","scopus_import":"1","license":"https://creativecommons.org/licenses/by/3.0/","abstract":[{"text":"Various kinds of data are routinely represented as discrete probability distributions. Examples include text documents summarized by histograms of word occurrences and images represented as histograms of oriented gradients. Viewing a discrete probability distribution as a point in the standard simplex of the appropriate dimension, we can understand collections of such objects in geometric and topological terms.  Importantly, instead of using the standard Euclidean distance, we look into dissimilarity measures with information-theoretic justification, and we develop the theory needed for applying topological data analysis in this setting. In doing so, we emphasize constructions that enable the usage of existing computational topology software in this context.","lang":"eng"}],"day":"14","citation":{"ista":"Edelsbrunner H, Virk Z, Wagner H. 2020. Topological data analysis in information space. Journal of Computational Geometry. 11(2), 162–182.","ama":"Edelsbrunner H, Virk Z, Wagner H. Topological data analysis in information space. <i>Journal of Computational Geometry</i>. 2020;11(2):162-182. doi:<a href=\"https://doi.org/10.20382/jocg.v11i2a7\">10.20382/jocg.v11i2a7</a>","chicago":"Edelsbrunner, Herbert, Ziga Virk, and Hubert Wagner. “Topological Data Analysis in Information Space.” <i>Journal of Computational Geometry</i>. Carleton University, 2020. <a href=\"https://doi.org/10.20382/jocg.v11i2a7\">https://doi.org/10.20382/jocg.v11i2a7</a>.","apa":"Edelsbrunner, H., Virk, Z., &#38; Wagner, H. (2020). Topological data analysis in information space. <i>Journal of Computational Geometry</i>. Carleton University. <a href=\"https://doi.org/10.20382/jocg.v11i2a7\">https://doi.org/10.20382/jocg.v11i2a7</a>","short":"H. Edelsbrunner, Z. Virk, H. Wagner, Journal of Computational Geometry 11 (2020) 162–182.","ieee":"H. Edelsbrunner, Z. Virk, and H. Wagner, “Topological data analysis in information space,” <i>Journal of Computational Geometry</i>, vol. 11, no. 2. Carleton University, pp. 162–182, 2020.","mla":"Edelsbrunner, Herbert, et al. “Topological Data Analysis in Information Space.” <i>Journal of Computational Geometry</i>, vol. 11, no. 2, Carleton University, 2020, pp. 162–82, doi:<a href=\"https://doi.org/10.20382/jocg.v11i2a7\">10.20382/jocg.v11i2a7</a>."}},{"oa_version":"Published Version","author":[{"last_name":"Aksenov","full_name":"Aksenov, Vitaly","first_name":"Vitaly"},{"first_name":"Dan-Adrian","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-3650-940X","full_name":"Alistarh, Dan-Adrian","last_name":"Alistarh"},{"full_name":"Korhonen, Janne","last_name":"Korhonen","first_name":"Janne","id":"C5402D42-15BC-11E9-A202-CA2BE6697425"}],"ec_funded":1,"department":[{"_id":"DaAl"}],"publication_identifier":{"issn":["10495258"],"isbn":["9781713829546"]},"language":[{"iso":"eng"}],"type":"conference","publication_status":"published","quality_controlled":"1","oa":1,"publication":"Advances in Neural Information Processing Systems","intvolume":"        33","_id":"9631","date_published":"2020-12-06T00:00:00Z","conference":{"location":"Vancouver, Canada","end_date":"2020-12-12","name":"NeurIPS: Conference on Neural Information Processing Systems","start_date":"2020-12-06"},"arxiv":1,"external_id":{"arxiv":["2002.11505"]},"month":"12","page":"22361-22372","status":"public","project":[{"call_identifier":"H2020","name":"Elastic Coordination for Scalable Machine Learning","grant_number":"805223","_id":"268A44D6-B435-11E9-9278-68D0E5697425"}],"main_file_link":[{"url":"https://proceedings.neurips.cc/paper/2020/hash/fdb2c3bab9d0701c4a050a4d8d782c7f-Abstract.html","open_access":"1"}],"acknowledgement":"We thank Marco Mondelli for discussions related to LDPC decoding, and Giorgi Nadiradze for discussions on analysis of relaxed schedulers. This project has received funding from the European Research Council (ERC) under the European\r\nUnion’s Horizon 2020 research and innovation programme (grant agreement No 805223 ScaleML).","volume":33,"year":"2020","date_created":"2021-07-04T22:01:26Z","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","date_updated":"2023-02-23T14:03:03Z","publisher":"Curran Associates","article_processing_charge":"No","title":"Scalable belief propagation via relaxed scheduling","abstract":[{"text":"The ability to leverage large-scale hardware parallelism has been one of the key enablers of the accelerated recent progress in machine learning. Consequently, there has been considerable effort invested into developing efficient parallel variants of classic machine learning algorithms. However, despite the wealth of knowledge on parallelization, some classic machine learning algorithms often prove hard to parallelize efficiently while maintaining convergence. In this paper, we focus on efficient parallel algorithms for the key machine learning task of inference on graphical models, in particular on the fundamental belief propagation algorithm. We address the challenge of efficiently parallelizing this classic paradigm by showing how to leverage scalable relaxed schedulers in this context. We present an extensive empirical study, showing that our approach outperforms previous parallel belief propagation implementations both in terms of scalability and in terms of wall-clock convergence time, on a range of practical applications.","lang":"eng"}],"day":"06","citation":{"short":"V. Aksenov, D.-A. Alistarh, J. Korhonen, in:, Advances in Neural Information Processing Systems, Curran Associates, 2020, pp. 22361–22372.","ista":"Aksenov V, Alistarh D-A, Korhonen J. 2020. Scalable belief propagation via relaxed scheduling. Advances in Neural Information Processing Systems. NeurIPS: Conference on Neural Information Processing Systems vol. 33, 22361–22372.","chicago":"Aksenov, Vitaly, Dan-Adrian Alistarh, and Janne Korhonen. “Scalable Belief Propagation via Relaxed Scheduling.” In <i>Advances in Neural Information Processing Systems</i>, 33:22361–72. Curran Associates, 2020.","apa":"Aksenov, V., Alistarh, D.-A., &#38; Korhonen, J. (2020). Scalable belief propagation via relaxed scheduling. In <i>Advances in Neural Information Processing Systems</i> (Vol. 33, pp. 22361–22372). Vancouver, Canada: Curran Associates.","ama":"Aksenov V, Alistarh D-A, Korhonen J. Scalable belief propagation via relaxed scheduling. In: <i>Advances in Neural Information Processing Systems</i>. Vol 33. Curran Associates; 2020:22361-22372.","mla":"Aksenov, Vitaly, et al. “Scalable Belief Propagation via Relaxed Scheduling.” <i>Advances in Neural Information Processing Systems</i>, vol. 33, Curran Associates, 2020, pp. 22361–72.","ieee":"V. Aksenov, D.-A. Alistarh, and J. Korhonen, “Scalable belief propagation via relaxed scheduling,” in <i>Advances in Neural Information Processing Systems</i>, Vancouver, Canada, 2020, vol. 33, pp. 22361–22372."},"scopus_import":"1"},{"publication_status":"published","quality_controlled":"1","publication":"Advances in Neural Information Processing Systems","intvolume":"        33","oa":1,"_id":"9632","date_published":"2020-12-06T00:00:00Z","external_id":{"arxiv":["2004.14340"]},"conference":{"location":"Vancouver, Canada","end_date":"2020-12-12","start_date":"2020-12-06","name":"NeurIPS: Conference on Neural Information Processing Systems"},"arxiv":1,"month":"12","publication_identifier":{"isbn":["9781713829546"],"issn":["10495258"]},"language":[{"iso":"eng"}],"type":"conference","ec_funded":1,"department":[{"_id":"DaAl"},{"_id":"ToHe"}],"author":[{"first_name":"Sidak Pal","id":"DD138E24-D89D-11E9-9DC0-DEF6E5697425","full_name":"Singh, Sidak Pal","last_name":"Singh"},{"first_name":"Dan-Adrian","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","full_name":"Alistarh, Dan-Adrian","orcid":"0000-0003-3650-940X","last_name":"Alistarh"}],"oa_version":"Published Version","scopus_import":"1","abstract":[{"lang":"eng","text":"Second-order information, in the form of Hessian- or Inverse-Hessian-vector products, is a fundamental tool for solving optimization problems. Recently, there has been significant interest in utilizing this information in the context of deep\r\nneural networks; however, relatively little is known about the quality of existing approximations in this context. Our work examines this question, identifies issues with existing approaches, and proposes a method called WoodFisher to compute a faithful and efficient estimate of the inverse Hessian. Our main application is to neural network compression, where we build on the classic Optimal Brain Damage/Surgeon framework. We demonstrate that WoodFisher significantly outperforms popular state-of-the-art methods for oneshot pruning. Further, even when iterative, gradual pruning is allowed, our method results in a gain in test accuracy over the state-of-the-art approaches, for standard image classification datasets such as ImageNet ILSVRC. We examine how our method can be extended to take into account first-order information, as well as\r\nillustrate its ability to automatically set layer-wise pruning thresholds and perform compression in the limited-data regime. The code is available at the following link, https://github.com/IST-DASLab/WoodFisher."}],"day":"06","citation":{"ieee":"S. P. Singh and D.-A. Alistarh, “WoodFisher: Efficient second-order approximation for neural network compression,” in <i>Advances in Neural Information Processing Systems</i>, Vancouver, Canada, 2020, vol. 33, pp. 18098–18109.","mla":"Singh, Sidak Pal, and Dan-Adrian Alistarh. “WoodFisher: Efficient Second-Order Approximation for Neural Network Compression.” <i>Advances in Neural Information Processing Systems</i>, vol. 33, Curran Associates, 2020, pp. 18098–109.","apa":"Singh, S. P., &#38; Alistarh, D.-A. (2020). WoodFisher: Efficient second-order approximation for neural network compression. In <i>Advances in Neural Information Processing Systems</i> (Vol. 33, pp. 18098–18109). Vancouver, Canada: Curran Associates.","ista":"Singh SP, Alistarh D-A. 2020. WoodFisher: Efficient second-order approximation for neural network compression. Advances in Neural Information Processing Systems. NeurIPS: Conference on Neural Information Processing Systems vol. 33, 18098–18109.","ama":"Singh SP, Alistarh D-A. WoodFisher: Efficient second-order approximation for neural network compression. In: <i>Advances in Neural Information Processing Systems</i>. Vol 33. Curran Associates; 2020:18098-18109.","chicago":"Singh, Sidak Pal, and Dan-Adrian Alistarh. “WoodFisher: Efficient Second-Order Approximation for Neural Network Compression.” In <i>Advances in Neural Information Processing Systems</i>, 33:18098–109. Curran Associates, 2020.","short":"S.P. Singh, D.-A. Alistarh, in:, Advances in Neural Information Processing Systems, Curran Associates, 2020, pp. 18098–18109."},"publisher":"Curran Associates","date_updated":"2023-02-23T14:03:06Z","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","article_processing_charge":"No","title":"WoodFisher: Efficient second-order approximation for neural network compression","page":"18098-18109","project":[{"call_identifier":"H2020","name":"Elastic Coordination for Scalable Machine Learning","grant_number":"805223","_id":"268A44D6-B435-11E9-9278-68D0E5697425"}],"status":"public","main_file_link":[{"open_access":"1","url":"https://proceedings.neurips.cc/paper/2020/hash/d1ff1ec86b62cd5f3903ff19c3a326b2-Abstract.html"}],"acknowledgement":"This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 805223 ScaleML). Also, we would like to thank Alexander Shevchenko, Alexandra Peste, and other members of the group for fruitful discussions.","volume":33,"year":"2020","date_created":"2021-07-04T22:01:26Z"},{"type":"conference","language":[{"iso":"eng"}],"publication_identifier":{"issn":["1049-5258"]},"conference":{"start_date":"2020-12-06","name":"NeurIPS: Conference on Neural Information Processing Systems","end_date":"2020-12-12","location":"Vancouver, Canada"},"month":"12","_id":"9633","date_published":"2020-12-06T00:00:00Z","publication":"Advances in Neural Information Processing Systems","oa":1,"intvolume":"        33","publication_status":"published","quality_controlled":"1","oa_version":"Published Version","author":[{"full_name":"Confavreux, Basile J","last_name":"Confavreux","first_name":"Basile J","id":"C7610134-B532-11EA-BD9F-F5753DDC885E"},{"first_name":"Friedemann","last_name":"Zenke","full_name":"Zenke, Friedemann"},{"last_name":"Agnes","full_name":"Agnes, Everton J.","first_name":"Everton J."},{"last_name":"Lillicrap","full_name":"Lillicrap, Timothy","first_name":"Timothy"},{"last_name":"Vogels","orcid":"0000-0003-3295-6181","full_name":"Vogels, Tim P","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","first_name":"Tim P"}],"ec_funded":1,"department":[{"_id":"TiVo"}],"citation":{"short":"B.J. Confavreux, F. Zenke, E.J. Agnes, T. Lillicrap, T.P. Vogels, in:, Advances in Neural Information Processing Systems, 2020, pp. 16398–16408.","apa":"Confavreux, B. J., Zenke, F., Agnes, E. J., Lillicrap, T., &#38; Vogels, T. P. (2020). A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network. In <i>Advances in Neural Information Processing Systems</i> (Vol. 33, pp. 16398–16408). Vancouver, Canada.","ista":"Confavreux BJ, Zenke F, Agnes EJ, Lillicrap T, Vogels TP. 2020. A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network. Advances in Neural Information Processing Systems. NeurIPS: Conference on Neural Information Processing Systems vol. 33, 16398–16408.","chicago":"Confavreux, Basile J, Friedemann Zenke, Everton J. Agnes, Timothy Lillicrap, and Tim P Vogels. “A Meta-Learning Approach to (Re)Discover Plasticity Rules That Carve a Desired Function into a Neural Network.” In <i>Advances in Neural Information Processing Systems</i>, 33:16398–408, 2020.","ama":"Confavreux BJ, Zenke F, Agnes EJ, Lillicrap T, Vogels TP. A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network. In: <i>Advances in Neural Information Processing Systems</i>. Vol 33. ; 2020:16398-16408.","mla":"Confavreux, Basile J., et al. “A Meta-Learning Approach to (Re)Discover Plasticity Rules That Carve a Desired Function into a Neural Network.” <i>Advances in Neural Information Processing Systems</i>, vol. 33, 2020, pp. 16398–408.","ieee":"B. J. Confavreux, F. Zenke, E. J. Agnes, T. Lillicrap, and T. P. Vogels, “A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network,” in <i>Advances in Neural Information Processing Systems</i>, Vancouver, Canada, 2020, vol. 33, pp. 16398–16408."},"day":"06","abstract":[{"lang":"eng","text":"The search for biologically faithful synaptic plasticity rules has resulted in a large body of models. They are usually inspired by – and fitted to – experimental data, but they rarely produce neural dynamics that serve complex functions. These failures suggest that current plasticity models are still under-constrained by existing data. Here, we present an alternative approach that uses meta-learning to discover plausible synaptic plasticity rules. Instead of experimental data, the rules are constrained by the functions they implement and the structure they are meant to produce. Briefly, we parameterize synaptic plasticity rules by a Volterra expansion and then use supervised learning methods (gradient descent or evolutionary strategies) to minimize a problem-dependent loss function that quantifies how effectively a candidate plasticity rule transforms an initially random network into one with the desired function. We first validate our approach by re-discovering previously described plasticity rules, starting at the single-neuron level and “Oja’s rule”, a simple Hebbian plasticity rule that captures the direction of most variability of inputs to a neuron (i.e., the first principal component). We expand the problem to the network level and ask the framework to find Oja’s rule together with an anti-Hebbian rule such that an initially random two-layer firing-rate network will recover several principal components of the input space after learning. Next, we move to networks of integrate-and-fire neurons with plastic inhibitory afferents. We train for rules that achieve a target firing rate by countering tuned excitation. Our algorithm discovers a specific subset of the manifold of rules that can solve this task. Our work is a proof of principle of an automated and unbiased approach to unveil synaptic plasticity rules that obey biological constraints and can solve complex functions."}],"scopus_import":"1","related_material":{"link":[{"url":"https://doi.org/10.1101/2020.10.24.353409","relation":"is_continued_by"}],"record":[{"status":"public","id":"14422","relation":"dissertation_contains"}]},"year":"2020","date_created":"2021-07-04T22:01:27Z","acknowledgement":"We would like to thank Chaitanya Chintaluri, Georgia Christodoulou, Bill Podlaski and Merima Šabanovic for useful discussions and comments. This work was supported by a Wellcome Trust ´ Senior Research Fellowship (214316/Z/18/Z), a BBSRC grant (BB/N019512/1), an ERC consolidator Grant (SYNAPSEEK), a Leverhulme Trust Project Grant (RPG-2016-446), and funding from École Polytechnique, Paris.","volume":33,"project":[{"grant_number":"214316/Z/18/Z","_id":"c084a126-5a5b-11eb-8a69-d75314a70a87","name":"What’s in a memory? Spatiotemporal dynamics in strongly coupled recurrent neuronal networks."},{"call_identifier":"H2020","name":"Learning the shape of synaptic plasticity rules for neuronal architectures and function through machine learning.","grant_number":"819603","_id":"0aacfa84-070f-11eb-9043-d7eb2c709234"}],"status":"public","main_file_link":[{"open_access":"1","url":"https://proceedings.neurips.cc/paper/2020/hash/bdbd5ebfde4934142c8a88e7a3796cd5-Abstract.html"}],"page":"16398-16408","article_processing_charge":"No","title":"A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network","date_updated":"2023-10-18T09:20:55Z","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf"},{"quality_controlled":"1","publication_status":"published","oa":1,"intvolume":"       152","publication":"The Journal of Chemical Physics","date_published":"2020-01-31T00:00:00Z","doi":"10.1063/1.5134461","extern":"1","_id":"9658","month":"01","arxiv":1,"external_id":{"pmid":["32007057"],"arxiv":["1910.13481"]},"language":[{"iso":"eng"}],"publication_identifier":{"issn":["0021-9606"],"eissn":["1089-7690"]},"type":"journal_article","issue":"4","author":[{"orcid":"0000-0002-3584-9632","full_name":"Cheng, Bingqing","last_name":"Cheng","first_name":"Bingqing","id":"cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9"},{"last_name":"Ceriotti","full_name":"Ceriotti, Michele","first_name":"Michele"},{"first_name":"Gareth A.","full_name":"Tribello, Gareth A.","last_name":"Tribello"}],"oa_version":"Submitted Version","scopus_import":"1","abstract":[{"text":"Macroscopic models of nucleation provide powerful tools for understanding activated phase transition processes. These models do not provide atomistic insights and can thus sometimes lack material-specific descriptions. Here, we provide a comprehensive framework for constructing a continuum picture from an atomistic simulation of homogeneous nucleation. We use this framework to determine the equilibrium shape of the solid nucleus that forms inside bulk liquid for a Lennard-Jones potential. From this shape, we then extract the anisotropy of the solid-liquid interfacial free energy, by performing a reverse Wulff construction in the space of spherical harmonic expansions. We find that the shape of the nucleus is nearly spherical and that its anisotropy can be perfectly described using classical models.","lang":"eng"}],"day":"31","citation":{"short":"B. Cheng, M. Ceriotti, G.A. Tribello, The Journal of Chemical Physics 152 (2020).","ista":"Cheng B, Ceriotti M, Tribello GA. 2020. Classical nucleation theory predicts the shape of the nucleus in homogeneous solidification. The Journal of Chemical Physics. 152(4), 044103.","ama":"Cheng B, Ceriotti M, Tribello GA. Classical nucleation theory predicts the shape of the nucleus in homogeneous solidification. <i>The Journal of Chemical Physics</i>. 2020;152(4). doi:<a href=\"https://doi.org/10.1063/1.5134461\">10.1063/1.5134461</a>","chicago":"Cheng, Bingqing, Michele Ceriotti, and Gareth A. Tribello. “Classical Nucleation Theory Predicts the Shape of the Nucleus in Homogeneous Solidification.” <i>The Journal of Chemical Physics</i>. AIP Publishing, 2020. <a href=\"https://doi.org/10.1063/1.5134461\">https://doi.org/10.1063/1.5134461</a>.","apa":"Cheng, B., Ceriotti, M., &#38; Tribello, G. A. (2020). Classical nucleation theory predicts the shape of the nucleus in homogeneous solidification. <i>The Journal of Chemical Physics</i>. AIP Publishing. <a href=\"https://doi.org/10.1063/1.5134461\">https://doi.org/10.1063/1.5134461</a>","mla":"Cheng, Bingqing, et al. “Classical Nucleation Theory Predicts the Shape of the Nucleus in Homogeneous Solidification.” <i>The Journal of Chemical Physics</i>, vol. 152, no. 4, 044103, AIP Publishing, 2020, doi:<a href=\"https://doi.org/10.1063/1.5134461\">10.1063/1.5134461</a>.","ieee":"B. Cheng, M. Ceriotti, and G. A. Tribello, “Classical nucleation theory predicts the shape of the nucleus in homogeneous solidification,” <i>The Journal of Chemical Physics</i>, vol. 152, no. 4. AIP Publishing, 2020."},"date_updated":"2023-02-23T14:03:55Z","publisher":"AIP Publishing","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","article_number":"044103","pmid":1,"title":"Classical nucleation theory predicts the shape of the nucleus in homogeneous solidification","article_processing_charge":"No","main_file_link":[{"open_access":"1","url":"https://pure.qub.ac.uk/en/publications/classical-nucleation-theory-predicts-the-shape-of-the-nucleus-in-homogeneous-solidification(56af848b-eee8-4e9b-93cf-667373e4a49b).html"}],"article_type":"original","status":"public","volume":152,"date_created":"2021-07-15T07:22:24Z","year":"2020"},{"extern":"1","doi":"10.1103/physrevlett.125.130602","date_published":"2020-09-25T00:00:00Z","_id":"9664","month":"09","external_id":{"arxiv":["2005.07562"],"pmid":["33034481"]},"arxiv":1,"quality_controlled":"1","publication_status":"published","oa":1,"publication":"Physical Review Letters","intvolume":"       125","language":[{"iso":"eng"}],"publication_identifier":{"eissn":["1079-7114"],"issn":["0031-9007"]},"type":"journal_article","issue":"13","oa_version":"Preprint","author":[{"last_name":"Cheng","full_name":"Cheng, Bingqing","orcid":"0000-0002-3584-9632","id":"cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9","first_name":"Bingqing"},{"first_name":"Daan","full_name":"Frenkel, Daan","last_name":"Frenkel"}],"scopus_import":"1","day":"25","citation":{"ama":"Cheng B, Frenkel D. Computing the heat conductivity of fluids from density fluctuations. <i>Physical Review Letters</i>. 2020;125(13). doi:<a href=\"https://doi.org/10.1103/physrevlett.125.130602\">10.1103/physrevlett.125.130602</a>","apa":"Cheng, B., &#38; Frenkel, D. (2020). Computing the heat conductivity of fluids from density fluctuations. <i>Physical Review Letters</i>. American Physical Society. <a href=\"https://doi.org/10.1103/physrevlett.125.130602\">https://doi.org/10.1103/physrevlett.125.130602</a>","chicago":"Cheng, Bingqing, and Daan Frenkel. “Computing the Heat Conductivity of Fluids from Density Fluctuations.” <i>Physical Review Letters</i>. American Physical Society, 2020. <a href=\"https://doi.org/10.1103/physrevlett.125.130602\">https://doi.org/10.1103/physrevlett.125.130602</a>.","ista":"Cheng B, Frenkel D. 2020. Computing the heat conductivity of fluids from density fluctuations. Physical Review Letters. 125(13), 130602.","short":"B. Cheng, D. Frenkel, Physical Review Letters 125 (2020).","ieee":"B. Cheng and D. Frenkel, “Computing the heat conductivity of fluids from density fluctuations,” <i>Physical Review Letters</i>, vol. 125, no. 13. American Physical Society, 2020.","mla":"Cheng, Bingqing, and Daan Frenkel. “Computing the Heat Conductivity of Fluids from Density Fluctuations.” <i>Physical Review Letters</i>, vol. 125, no. 13, 130602, American Physical Society, 2020, doi:<a href=\"https://doi.org/10.1103/physrevlett.125.130602\">10.1103/physrevlett.125.130602</a>."},"abstract":[{"text":"Equilibrium molecular dynamics simulations, in combination with the Green-Kubo (GK) method, have been extensively used to compute the thermal conductivity of liquids. However, the GK method relies on an ambiguous definition of the microscopic heat flux, which depends on how one chooses to distribute energies over atoms. This ambiguity makes it problematic to employ the GK method for systems with nonpairwise interactions. In this work, we show that the hydrodynamic description of thermally driven density fluctuations can be used to obtain the thermal conductivity of a bulk fluid unambiguously, thereby bypassing the need to define the heat flux. We verify that, for a model fluid with only pairwise interactions, our method yields estimates of thermal conductivity consistent with the GK approach. We apply our approach to compute the thermal conductivity of a nonpairwise additive water model at supercritical conditions, and of a liquid hydrogen system described by a machine-learning interatomic potential, at 33 GPa and 2000 K.","lang":"eng"}],"article_number":"130602","pmid":1,"title":"Computing the heat conductivity of fluids from density fluctuations","article_processing_charge":"No","publisher":"American Physical Society","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","date_updated":"2021-08-09T12:35:58Z","volume":125,"date_created":"2021-07-15T12:15:14Z","year":"2020","article_type":"original","main_file_link":[{"url":"https://arxiv.org/abs/2005.07562","open_access":"1"}],"status":"public"},{"arxiv":1,"external_id":{"arxiv":["1909.08934"],"pmid":["32459228"]},"month":"06","_id":"9666","date_published":"2020-06-14T00:00:00Z","doi":"10.1039/d0cp02513e","extern":"1","publication":"Physical Chemistry Chemical Physics","oa":1,"intvolume":"        22","publication_status":"published","quality_controlled":"1","issue":"22","type":"journal_article","file_date_updated":"2021-07-15T12:43:51Z","publication_identifier":{"issn":["1463-9076"],"eissn":["1463-9084"]},"language":[{"iso":"eng"}],"file":[{"file_size":3151206,"creator":"asandaue","file_id":"9667","date_created":"2021-07-15T12:43:51Z","relation":"main_file","file_name":"202_PhysicalChemistryChemicalPhysics_Reinhardt.pdf","date_updated":"2021-07-15T12:43:51Z","success":1,"access_level":"open_access","checksum":"0a6872972b1b2e60f9095d39b01753fa","content_type":"application/pdf"}],"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/3.0/legalcode","image":"/images/cc_by.png","short":"CC BY (3.0)","name":"Creative Commons Attribution 3.0 Unported (CC BY 3.0)"},"oa_version":"Published Version","author":[{"full_name":"Reinhardt, Aleks","last_name":"Reinhardt","first_name":"Aleks"},{"first_name":"Chris J.","full_name":"Pickard, Chris J.","last_name":"Pickard"},{"orcid":"0000-0002-3584-9632","full_name":"Cheng, Bingqing","last_name":"Cheng","first_name":"Bingqing","id":"cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9"}],"scopus_import":"1","has_accepted_license":"1","citation":{"apa":"Reinhardt, A., Pickard, C. J., &#38; Cheng, B. (2020). Predicting the phase diagram of titanium dioxide with random search and pattern recognition. <i>Physical Chemistry Chemical Physics</i>. Royal Society of Chemistry. <a href=\"https://doi.org/10.1039/d0cp02513e\">https://doi.org/10.1039/d0cp02513e</a>","chicago":"Reinhardt, Aleks, Chris J. Pickard, and Bingqing Cheng. “Predicting the Phase Diagram of Titanium Dioxide with Random Search and Pattern Recognition.” <i>Physical Chemistry Chemical Physics</i>. Royal Society of Chemistry, 2020. <a href=\"https://doi.org/10.1039/d0cp02513e\">https://doi.org/10.1039/d0cp02513e</a>.","ista":"Reinhardt A, Pickard CJ, Cheng B. 2020. Predicting the phase diagram of titanium dioxide with random search and pattern recognition. Physical Chemistry Chemical Physics. 22(22), 12697–12705.","ama":"Reinhardt A, Pickard CJ, Cheng B. Predicting the phase diagram of titanium dioxide with random search and pattern recognition. <i>Physical Chemistry Chemical Physics</i>. 2020;22(22):12697-12705. doi:<a href=\"https://doi.org/10.1039/d0cp02513e\">10.1039/d0cp02513e</a>","short":"A. Reinhardt, C.J. Pickard, B. Cheng, Physical Chemistry Chemical Physics 22 (2020) 12697–12705.","ieee":"A. Reinhardt, C. J. Pickard, and B. Cheng, “Predicting the phase diagram of titanium dioxide with random search and pattern recognition,” <i>Physical Chemistry Chemical Physics</i>, vol. 22, no. 22. Royal Society of Chemistry, pp. 12697–12705, 2020.","mla":"Reinhardt, Aleks, et al. “Predicting the Phase Diagram of Titanium Dioxide with Random Search and Pattern Recognition.” <i>Physical Chemistry Chemical Physics</i>, vol. 22, no. 22, Royal Society of Chemistry, 2020, pp. 12697–705, doi:<a href=\"https://doi.org/10.1039/d0cp02513e\">10.1039/d0cp02513e</a>."},"day":"14","abstract":[{"text":"Predicting phase stabilities of crystal polymorphs is central to computational materials science and chemistry. Such predictions are challenging because they first require searching for potential energy minima and then performing arduous free-energy calculations to account for entropic effects at finite temperatures. Here, we develop a framework that facilitates such predictions by exploiting all the information obtained from random searches of crystal structures. This framework combines automated clustering, classification and visualisation of crystal structures with machine-learning estimation of their enthalpy and entropy. We demonstrate the framework on the technologically important system of TiO2, which has many polymorphs, without relying on prior knowledge of known phases. We find a number of new phases and predict the phase diagram and metastabilities of crystal polymorphs at 1600 K, benchmarking the results against full free-energy calculations.","lang":"eng"}],"article_processing_charge":"No","title":"Predicting the phase diagram of titanium dioxide with random search and pattern recognition","pmid":1,"user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","publisher":"Royal Society of Chemistry","date_updated":"2023-02-23T14:04:16Z","ddc":["530"],"year":"2020","date_created":"2021-07-15T12:37:27Z","volume":22,"status":"public","article_type":"original","page":"12697-12705"},{"article_processing_charge":"No","title":"Liquid water contains the building blocks of diverse ice phases","article_number":"5757","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","date_updated":"2023-02-23T14:04:25Z","publisher":"Springer Nature","ddc":["530","540"],"year":"2020","date_created":"2021-07-15T14:01:35Z","volume":11,"status":"public","article_type":"original","scopus_import":"1","has_accepted_license":"1","citation":{"chicago":"Monserrat, Bartomeu, Jan Gerit Brandenburg, Edgar A. Engel, and Bingqing Cheng. “Liquid Water Contains the Building Blocks of Diverse Ice Phases.” <i>Nature Communications</i>. Springer Nature, 2020. <a href=\"https://doi.org/10.1038/s41467-020-19606-y\">https://doi.org/10.1038/s41467-020-19606-y</a>.","ista":"Monserrat B, Brandenburg JG, Engel EA, Cheng B. 2020. Liquid water contains the building blocks of diverse ice phases. Nature Communications. 11(1), 5757.","ama":"Monserrat B, Brandenburg JG, Engel EA, Cheng B. Liquid water contains the building blocks of diverse ice phases. <i>Nature Communications</i>. 2020;11(1). doi:<a href=\"https://doi.org/10.1038/s41467-020-19606-y\">10.1038/s41467-020-19606-y</a>","apa":"Monserrat, B., Brandenburg, J. G., Engel, E. A., &#38; Cheng, B. (2020). Liquid water contains the building blocks of diverse ice phases. <i>Nature Communications</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s41467-020-19606-y\">https://doi.org/10.1038/s41467-020-19606-y</a>","short":"B. Monserrat, J.G. Brandenburg, E.A. Engel, B. Cheng, Nature Communications 11 (2020).","ieee":"B. Monserrat, J. G. Brandenburg, E. A. Engel, and B. Cheng, “Liquid water contains the building blocks of diverse ice phases,” <i>Nature Communications</i>, vol. 11, no. 1. Springer Nature, 2020.","mla":"Monserrat, Bartomeu, et al. “Liquid Water Contains the Building Blocks of Diverse Ice Phases.” <i>Nature Communications</i>, vol. 11, no. 1, 5757, Springer Nature, 2020, doi:<a href=\"https://doi.org/10.1038/s41467-020-19606-y\">10.1038/s41467-020-19606-y</a>."},"day":"13","abstract":[{"lang":"eng","text":"Water molecules can arrange into a liquid with complex hydrogen-bond networks and at least 17 experimentally confirmed ice phases with enormous structural diversity. It remains a puzzle how or whether this multitude of arrangements in different phases of water are related. Here we investigate the structural similarities between liquid water and a comprehensive set of 54 ice phases in simulations, by directly comparing their local environments using general atomic descriptors, and also by demonstrating that a machine-learning potential trained on liquid water alone can predict the densities, lattice energies, and vibrational properties of the ices. The finding that the local environments characterising the different ice phases are found in water sheds light on the phase behavior of water, and rationalizes the transferability of water models between different phases."}],"file":[{"file_name":"2020_NatureCommunications_Monserrat.pdf","date_updated":"2021-07-15T14:05:45Z","checksum":"1edd9b6d8fa791f8094d87bd6453955b","content_type":"application/pdf","access_level":"open_access","success":1,"file_size":1385954,"creator":"asandaue","date_created":"2021-07-15T14:05:45Z","relation":"main_file","file_id":"9672"}],"tmp":{"short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png"},"oa_version":"Published Version","author":[{"first_name":"Bartomeu","last_name":"Monserrat","full_name":"Monserrat, Bartomeu"},{"first_name":"Jan Gerit","last_name":"Brandenburg","full_name":"Brandenburg, Jan Gerit"},{"first_name":"Edgar A.","full_name":"Engel, Edgar A.","last_name":"Engel"},{"id":"cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9","first_name":"Bingqing","last_name":"Cheng","orcid":"0000-0002-3584-9632","full_name":"Cheng, Bingqing"}],"month":"11","_id":"9671","doi":"10.1038/s41467-020-19606-y","date_published":"2020-11-13T00:00:00Z","extern":"1","publication":"Nature Communications","intvolume":"        11","oa":1,"publication_status":"published","quality_controlled":"1","issue":"1","type":"journal_article","language":[{"iso":"eng"}],"file_date_updated":"2021-07-15T14:05:45Z","publication_identifier":{"eissn":["2041-1723"]}},{"author":[{"first_name":"Bingqing","id":"cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9","orcid":"0000-0002-3584-9632","full_name":"Cheng, Bingqing","last_name":"Cheng"},{"full_name":"Griffiths, Ryan-Rhys","last_name":"Griffiths","first_name":"Ryan-Rhys"},{"first_name":"Simon","last_name":"Wengert","full_name":"Wengert, Simon"},{"last_name":"Kunkel","full_name":"Kunkel, Christian","first_name":"Christian"},{"full_name":"Stenczel, Tamas","last_name":"Stenczel","first_name":"Tamas"},{"last_name":"Zhu","full_name":"Zhu, Bonan","first_name":"Bonan"},{"first_name":"Volker L.","last_name":"Deringer","full_name":"Deringer, Volker L."},{"first_name":"Noam","full_name":"Bernstein, Noam","last_name":"Bernstein"},{"last_name":"Margraf","full_name":"Margraf, Johannes T.","first_name":"Johannes T."},{"last_name":"Reuter","full_name":"Reuter, Karsten","first_name":"Karsten"},{"first_name":"Gabor","last_name":"Csanyi","full_name":"Csanyi, Gabor"}],"oa_version":"None","publication_identifier":{"issn":["0001-4842"],"eissn":["1520-4898"]},"language":[{"iso":"eng"}],"type":"journal_article","issue":"9","quality_controlled":"1","publication_status":"published","intvolume":"        53","publication":"Accounts of Chemical Research","doi":"10.1021/acs.accounts.0c00403","extern":"1","date_published":"2020-08-14T00:00:00Z","_id":"9675","month":"08","external_id":{"pmid":["32794697"]},"page":"1981-1991","article_type":"original","status":"public","volume":53,"date_created":"2021-07-16T06:25:53Z","year":"2020","user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","date_updated":"2021-11-24T15:54:41Z","publisher":"American Chemical Society","pmid":1,"title":"Mapping materials and molecules","article_processing_charge":"No","abstract":[{"lang":"eng","text":"The visualization of data is indispensable in scientific research, from the early stages when human insight forms to the final step of communicating results. In computational physics, chemistry and materials science, it can be as simple as making a scatter plot or as straightforward as looking through the snapshots of atomic positions manually. However, as a result of the \"big data\" revolution, these conventional approaches are often inadequate. The widespread adoption of high-throughput computation for materials discovery and the associated community-wide repositories have given rise to data sets that contain an enormous number of compounds and atomic configurations. A typical data set contains thousands to millions of atomic structures, along with a diverse range of properties such as formation energies, band gaps, or bioactivities.It would thus be desirable to have a data-driven and automated framework for visualizing and analyzing such structural data sets. The key idea is to construct a low-dimensional representation of the data, which facilitates navigation, reveals underlying patterns, and helps to identify data points with unusual attributes. Such data-intensive maps, often employing machine learning methods, are appearing more and more frequently in the literature. However, to the wider community, it is not always transparent how these maps are made and how they should be interpreted. Furthermore, while these maps undoubtedly serve a decorative purpose in academic publications, it is not always apparent what extra information can be garnered from reading or making them.This Account attempts to answer such questions. We start with a concise summary of the theory of representing chemical environments, followed by the introduction of a simple yet practical conceptual approach for generating structure maps in a generic and automated manner. Such analysis and mapping is made nearly effortless by employing the newly developed software tool ASAP. To showcase the applicability to a wide variety of systems in chemistry and materials science, we provide several illustrative examples, including crystalline and amorphous materials, interfaces, and organic molecules. In these examples, the maps not only help to sift through large data sets but also reveal hidden patterns that could be easily missed using conventional analyses.The explosion in the amount of computed information in chemistry and materials science has made visualization into a science in itself. Not only have we benefited from exploiting these visualization methods in previous works, we also believe that the automated mapping of data sets will in turn stimulate further creativity and exploration, as well as ultimately feed back into future advances in the respective fields."}],"day":"14","citation":{"ieee":"B. Cheng <i>et al.</i>, “Mapping materials and molecules,” <i>Accounts of Chemical Research</i>, vol. 53, no. 9. American Chemical Society, pp. 1981–1991, 2020.","mla":"Cheng, Bingqing, et al. “Mapping Materials and Molecules.” <i>Accounts of Chemical Research</i>, vol. 53, no. 9, American Chemical Society, 2020, pp. 1981–91, doi:<a href=\"https://doi.org/10.1021/acs.accounts.0c00403\">10.1021/acs.accounts.0c00403</a>.","ista":"Cheng B, Griffiths R-R, Wengert S, Kunkel C, Stenczel T, Zhu B, Deringer VL, Bernstein N, Margraf JT, Reuter K, Csanyi G. 2020. Mapping materials and molecules. Accounts of Chemical Research. 53(9), 1981–1991.","chicago":"Cheng, Bingqing, Ryan-Rhys Griffiths, Simon Wengert, Christian Kunkel, Tamas Stenczel, Bonan Zhu, Volker L. Deringer, et al. “Mapping Materials and Molecules.” <i>Accounts of Chemical Research</i>. American Chemical Society, 2020. <a href=\"https://doi.org/10.1021/acs.accounts.0c00403\">https://doi.org/10.1021/acs.accounts.0c00403</a>.","ama":"Cheng B, Griffiths R-R, Wengert S, et al. Mapping materials and molecules. <i>Accounts of Chemical Research</i>. 2020;53(9):1981-1991. doi:<a href=\"https://doi.org/10.1021/acs.accounts.0c00403\">10.1021/acs.accounts.0c00403</a>","apa":"Cheng, B., Griffiths, R.-R., Wengert, S., Kunkel, C., Stenczel, T., Zhu, B., … Csanyi, G. (2020). Mapping materials and molecules. <i>Accounts of Chemical Research</i>. American Chemical Society. <a href=\"https://doi.org/10.1021/acs.accounts.0c00403\">https://doi.org/10.1021/acs.accounts.0c00403</a>","short":"B. Cheng, R.-R. Griffiths, S. Wengert, C. Kunkel, T. Stenczel, B. Zhu, V.L. Deringer, N. Bernstein, J.T. Margraf, K. Reuter, G. Csanyi, Accounts of Chemical Research 53 (2020) 1981–1991."},"scopus_import":"1"},{"oa_version":"Preprint","author":[{"id":"cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9","first_name":"Bingqing","last_name":"Cheng","full_name":"Cheng, Bingqing","orcid":"0000-0002-3584-9632"},{"full_name":"Mazzola, Guglielmo","last_name":"Mazzola","first_name":"Guglielmo"},{"first_name":"Chris J.","last_name":"Pickard","full_name":"Pickard, Chris J."},{"first_name":"Michele","full_name":"Ceriotti, Michele","last_name":"Ceriotti"}],"publication":"Nature","oa":1,"intvolume":"       585","publication_status":"published","quality_controlled":"1","arxiv":1,"external_id":{"pmid":["32908269"],"arxiv":["1906.03341"]},"month":"09","_id":"9685","doi":"10.1038/s41586-020-2677-y","extern":"1","date_published":"2020-09-10T00:00:00Z","issue":"7824","type":"journal_article","publication_identifier":{"eissn":["1476-4687"],"issn":["0028-0836"]},"language":[{"iso":"eng"}],"user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","date_updated":"2021-08-09T12:38:01Z","publisher":"Springer Nature","article_processing_charge":"No","title":"Evidence for supercritical behaviour of high-pressure liquid hydrogen","pmid":1,"status":"public","main_file_link":[{"url":"https://arxiv.org/abs/1906.03341","open_access":"1"}],"article_type":"original","page":"217-220","year":"2020","date_created":"2021-07-19T09:17:49Z","volume":585,"scopus_import":"1","abstract":[{"text":"Hydrogen, the simplest and most abundant element in the Universe, develops a remarkably complex behaviour upon compression^1. Since Wigner predicted the dissociation and metallization of solid hydrogen at megabar pressures almost a century ago^2, several efforts have been made to explain the many unusual properties of dense hydrogen, including a rich and poorly understood solid polymorphism^1,3-5, an anomalous melting line6 and the possible transition to a superconducting state^7. Experiments at such extreme conditions are challenging and often lead to hard-to-interpret and controversial observations, whereas theoretical investigations are constrained by the huge computational cost of sufficiently accurate quantum mechanical calculations. Here we present a theoretical study of the phase diagram of dense hydrogen that uses machine learning to 'learn' potential-energy surfaces and interatomic forces from reference calculations and then predict them at low computational cost, overcoming length- and timescale limitations. We reproduce both the re-entrant melting behaviour and the polymorphism of the solid phase. Simulations using our machine-learning-based potentials provide evidence for a continuous molecular-to-atomic transition in the liquid, with no first-order transition observed above the melting line. This suggests a smooth transition between insulating and metallic layers in giant gas planets, and reconciles existing discrepancies between experiments as a manifestation of supercritical behaviour.","lang":"eng"}],"citation":{"mla":"Cheng, Bingqing, et al. “Evidence for Supercritical Behaviour of High-Pressure Liquid Hydrogen.” <i>Nature</i>, vol. 585, no. 7824, Springer Nature, 2020, pp. 217–20, doi:<a href=\"https://doi.org/10.1038/s41586-020-2677-y\">10.1038/s41586-020-2677-y</a>.","ieee":"B. Cheng, G. Mazzola, C. J. Pickard, and M. Ceriotti, “Evidence for supercritical behaviour of high-pressure liquid hydrogen,” <i>Nature</i>, vol. 585, no. 7824. Springer Nature, pp. 217–220, 2020.","short":"B. Cheng, G. Mazzola, C.J. Pickard, M. Ceriotti, Nature 585 (2020) 217–220.","apa":"Cheng, B., Mazzola, G., Pickard, C. J., &#38; Ceriotti, M. (2020). Evidence for supercritical behaviour of high-pressure liquid hydrogen. <i>Nature</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s41586-020-2677-y\">https://doi.org/10.1038/s41586-020-2677-y</a>","ama":"Cheng B, Mazzola G, Pickard CJ, Ceriotti M. Evidence for supercritical behaviour of high-pressure liquid hydrogen. <i>Nature</i>. 2020;585(7824):217-220. doi:<a href=\"https://doi.org/10.1038/s41586-020-2677-y\">10.1038/s41586-020-2677-y</a>","chicago":"Cheng, Bingqing, Guglielmo Mazzola, Chris J. Pickard, and Michele Ceriotti. “Evidence for Supercritical Behaviour of High-Pressure Liquid Hydrogen.” <i>Nature</i>. Springer Nature, 2020. <a href=\"https://doi.org/10.1038/s41586-020-2677-y\">https://doi.org/10.1038/s41586-020-2677-y</a>.","ista":"Cheng B, Mazzola G, Pickard CJ, Ceriotti M. 2020. Evidence for supercritical behaviour of high-pressure liquid hydrogen. Nature. 585(7824), 217–220."},"day":"10"},{"status":"public","author":[{"first_name":"Bartomeu","last_name":"Monserrat","full_name":"Monserrat, Bartomeu"},{"first_name":"Jan Gerit","last_name":"Brandenburg","full_name":"Brandenburg, Jan Gerit"},{"full_name":"Engel, Edgar A.","last_name":"Engel","first_name":"Edgar A."},{"first_name":"Bingqing","id":"cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9","full_name":"Cheng, Bingqing","orcid":"0000-0002-3584-9632","last_name":"Cheng"}],"main_file_link":[{"url":"https://arxiv.org/abs/2006.13316","open_access":"1"}],"oa_version":"Submitted Version","year":"2020","date_created":"2021-07-20T11:25:15Z","date_updated":"2023-05-10T10:17:48Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","article_processing_charge":"No","title":"Extracting ice phases from liquid water: Why a machine-learning water model generalizes so well","article_number":"2006.13316","abstract":[{"text":"We investigate the structural similarities between liquid water and 53 ices, including 20 known crystalline phases. We base such similarity comparison on the local environments that consist of atoms within a certain cutoff radius of a central atom. We reveal that liquid water explores the local environments of the diverse ice phases, by directly comparing the environments in these phases using general atomic descriptors, and also by demonstrating that a machine-learning potential trained on liquid water alone can predict the densities, the lattice energies, and vibrational properties of the\r\nices. The finding that the local environments characterising the different ice phases are found in water sheds light on water phase behaviors, and rationalizes the transferability of water models between different phases.","lang":"eng"}],"type":"preprint","language":[{"iso":"eng"}],"citation":{"chicago":"Monserrat, Bartomeu, Jan Gerit Brandenburg, Edgar A. Engel, and Bingqing Cheng. “Extracting Ice Phases from Liquid Water: Why a Machine-Learning Water Model Generalizes so Well.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/arXiv.2006.13316\">https://doi.org/10.48550/arXiv.2006.13316</a>.","apa":"Monserrat, B., Brandenburg, J. G., Engel, E. A., &#38; Cheng, B. (n.d.). Extracting ice phases from liquid water: Why a machine-learning water model generalizes so well. <i>arXiv</i>. <a href=\"https://doi.org/10.48550/arXiv.2006.13316\">https://doi.org/10.48550/arXiv.2006.13316</a>","ama":"Monserrat B, Brandenburg JG, Engel EA, Cheng B. Extracting ice phases from liquid water: Why a machine-learning water model generalizes so well. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/arXiv.2006.13316\">10.48550/arXiv.2006.13316</a>","ista":"Monserrat B, Brandenburg JG, Engel EA, Cheng B. Extracting ice phases from liquid water: Why a machine-learning water model generalizes so well. arXiv, 2006.13316.","short":"B. Monserrat, J.G. Brandenburg, E.A. Engel, B. Cheng, ArXiv (n.d.).","ieee":"B. Monserrat, J. G. Brandenburg, E. A. Engel, and B. Cheng, “Extracting ice phases from liquid water: Why a machine-learning water model generalizes so well,” <i>arXiv</i>. .","mla":"Monserrat, Bartomeu, et al. “Extracting Ice Phases from Liquid Water: Why a Machine-Learning Water Model Generalizes so Well.” <i>ArXiv</i>, 2006.13316, doi:<a href=\"https://doi.org/10.48550/arXiv.2006.13316\">10.48550/arXiv.2006.13316</a>."},"day":"23","publication":"arXiv","oa":1,"publication_status":"submitted","external_id":{"arxiv":["2006.13316"]},"arxiv":1,"month":"06","_id":"9699","date_published":"2020-06-23T00:00:00Z","doi":"10.48550/arXiv.2006.13316","extern":"1"},{"day":"09","citation":{"chicago":"Hillary, Robert F., Daniel Trejo-Banos, Athanasios Kousathanas, Daniel L. McCartney, Sarah E. Harris, Anna J. Stevenson, Marion Patxot, et al. “Additional File 2 of Multi-Method Genome- and Epigenome-Wide Studies of Inflammatory Protein Levels in Healthy Older Adults.” Springer Nature, 2020. <a href=\"https://doi.org/10.6084/m9.figshare.12629697.v1\">https://doi.org/10.6084/m9.figshare.12629697.v1</a>.","apa":"Hillary, R. F., Trejo-Banos, D., Kousathanas, A., McCartney, D. L., Harris, S. E., Stevenson, A. J., … Marioni, R. E. (2020). Additional file 2 of multi-method genome- and epigenome-wide studies of inflammatory protein levels in healthy older adults. Springer Nature. <a href=\"https://doi.org/10.6084/m9.figshare.12629697.v1\">https://doi.org/10.6084/m9.figshare.12629697.v1</a>","ama":"Hillary RF, Trejo-Banos D, Kousathanas A, et al. Additional file 2 of multi-method genome- and epigenome-wide studies of inflammatory protein levels in healthy older adults. 2020. doi:<a href=\"https://doi.org/10.6084/m9.figshare.12629697.v1\">10.6084/m9.figshare.12629697.v1</a>","ista":"Hillary RF, Trejo-Banos D, Kousathanas A, McCartney DL, Harris SE, Stevenson AJ, Patxot M, Ojavee SE, Zhang Q, Liewald DC, Ritchie CW, Evans KL, Tucker-Drob EM, Wray NR, McRae AF, Visscher PM, Deary IJ, Robinson MR, Marioni RE. 2020. Additional file 2 of multi-method genome- and epigenome-wide studies of inflammatory protein levels in healthy older adults, Springer Nature, <a href=\"https://doi.org/10.6084/m9.figshare.12629697.v1\">10.6084/m9.figshare.12629697.v1</a>.","short":"R.F. Hillary, D. Trejo-Banos, A. Kousathanas, D.L. McCartney, S.E. Harris, A.J. Stevenson, M. Patxot, S.E. Ojavee, Q. Zhang, D.C. Liewald, C.W. Ritchie, K.L. Evans, E.M. Tucker-Drob, N.R. Wray, A.F. McRae, P.M. Visscher, I.J. Deary, M.R. Robinson, R.E. Marioni, (2020).","ieee":"R. F. Hillary <i>et al.</i>, “Additional file 2 of multi-method genome- and epigenome-wide studies of inflammatory protein levels in healthy older adults.” Springer Nature, 2020.","mla":"Hillary, Robert F., et al. <i>Additional File 2 of Multi-Method Genome- and Epigenome-Wide Studies of Inflammatory Protein Levels in Healthy Older Adults</i>. Springer Nature, 2020, doi:<a href=\"https://doi.org/10.6084/m9.figshare.12629697.v1\">10.6084/m9.figshare.12629697.v1</a>."},"other_data_license":"CC0 + CC BY (4.0)","abstract":[{"text":"Additional file 2: Supplementary Tables. The association of pre-adjusted protein levels with biological and technical covariates. Protein levels were adjusted for age, sex, array plate and four genetic principal components (population structure) prior to analyses. Significant associations are emboldened. (Table S1). pQTLs associated with inflammatory biomarker levels from Bayesian penalised regression model (Posterior Inclusion Probability > 95%). (Table S2). All pQTLs associated with inflammatory biomarker levels from ordinary least squares regression model (P < 7.14 × 10− 10). (Table S3). Summary of lambda values relating to ordinary least squares GWAS and EWAS performed on inflammatory protein levels (n = 70) in Lothian Birth Cohort 1936 study. (Table S4). Conditionally significant pQTLs associated with inflammatory biomarker levels from ordinary least squares regression model (P < 7.14 × 10− 10). (Table S5). Comparison of variance explained by ordinary least squares and Bayesian penalised regression models for concordantly identified SNPs. (Table S6). Estimate of heritability for blood protein levels as well as proportion of variance explained attributable to different prior mixtures. (Table S7). Comparison of heritability estimates from Ahsan et al. (maximum likelihood) and Hillary et al. (Bayesian penalised regression). (Table S8). List of concordant SNPs identified by linear model and Bayesian penalised regression and whether they have been previously identified as eQTLs. (Table S9). Bayesian tests of colocalisation for cis pQTLs and cis eQTLs. (Table S10). Sherlock algorithm: Genes whose expression are putatively associated with circulating inflammatory proteins that harbour pQTLs. (Table S11). CpGs associated with inflammatory protein biomarkers as identified by Bayesian model (Bayesian model; Posterior Inclusion Probability > 95%). (Table S12). CpGs associated with inflammatory protein biomarkers as identified by linear model (limma) at P < 5.14 × 10− 10. (Table S13). CpGs associated with inflammatory protein biomarkers as identified by mixed linear model (OSCA) at P < 5.14 × 10− 10. (Table S14). Estimate of variance explained for blood protein levels by DNA methylation as well as proportion of explained attributable to different prior mixtures - BayesR+. (Table S15). Comparison of variance in protein levels explained by genome-wide DNA methylation data by mixed linear model (OSCA) and Bayesian penalised regression model (BayesR+). (Table S16). Variance in circulating inflammatory protein biomarker levels explained by common genetic and methylation data (joint and conditional estimates from BayesR+). Ordered by combined variance explained by genetic and epigenetic data - smallest to largest. Significant results from t-tests comparing distributions for variance explained by methylation or genetics alone versus combined estimate are emboldened. (Table S17). Genetic and epigenetic factors identified by BayesR+ when conditioning on all SNPs and CpGs together. (Table S18). Mendelian Randomisation analyses to assess whether proteins with concordantly identified genetic signals are causally associated with Alzheimer’s disease risk. (Table S19).","lang":"eng"}],"type":"research_data_reference","_id":"9706","has_accepted_license":"1","doi":"10.6084/m9.figshare.12629697.v1","related_material":{"record":[{"status":"public","id":"8133","relation":"used_in_publication"}]},"date_published":"2020-07-09T00:00:00Z","month":"07","oa":1,"year":"2020","date_created":"2021-07-23T08:59:15Z","status":"public","main_file_link":[{"url":"https://doi.org/10.6084/m9.figshare.12629697.v1","open_access":"1"}],"author":[{"first_name":"Robert F.","last_name":"Hillary","full_name":"Hillary, Robert F."},{"first_name":"Daniel","last_name":"Trejo-Banos","full_name":"Trejo-Banos, Daniel"},{"last_name":"Kousathanas","full_name":"Kousathanas, Athanasios","first_name":"Athanasios"},{"first_name":"Daniel L.","full_name":"McCartney, Daniel L.","last_name":"McCartney"},{"last_name":"Harris","full_name":"Harris, Sarah E.","first_name":"Sarah E."},{"first_name":"Anna J.","full_name":"Stevenson, Anna J.","last_name":"Stevenson"},{"full_name":"Patxot, Marion","last_name":"Patxot","first_name":"Marion"},{"first_name":"Sven Erik","last_name":"Ojavee","full_name":"Ojavee, Sven Erik"},{"last_name":"Zhang","full_name":"Zhang, Qian","first_name":"Qian"},{"full_name":"Liewald, David C.","last_name":"Liewald","first_name":"David C."},{"last_name":"Ritchie","full_name":"Ritchie, Craig W.","first_name":"Craig W."},{"last_name":"Evans","full_name":"Evans, Kathryn L.","first_name":"Kathryn L."},{"last_name":"Tucker-Drob","full_name":"Tucker-Drob, Elliot M.","first_name":"Elliot M."},{"full_name":"Wray, Naomi R.","last_name":"Wray","first_name":"Naomi R."},{"first_name":"Allan F. ","last_name":"McRae","full_name":"McRae, Allan F. "},{"full_name":"Visscher, Peter M.","last_name":"Visscher","first_name":"Peter M."},{"first_name":"Ian J.","last_name":"Deary","full_name":"Deary, Ian J."},{"full_name":"Robinson, Matthew Richard","orcid":"0000-0001-8982-8813","last_name":"Robinson","first_name":"Matthew Richard","id":"E5D42276-F5DA-11E9-8E24-6303E6697425"},{"first_name":"Riccardo E. ","last_name":"Marioni","full_name":"Marioni, Riccardo E. "}],"oa_version":"Published Version","article_processing_charge":"No","department":[{"_id":"MaRo"}],"title":"Additional file 2 of multi-method genome- and epigenome-wide studies of inflammatory protein levels in healthy older adults","tmp":{"short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png"},"publisher":"Springer Nature","date_updated":"2023-08-22T07:55:36Z","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf"},{"date_created":"2021-07-23T10:00:35Z","year":"2020","author":[{"last_name":"Hartstein","full_name":"Hartstein, Mate","first_name":"Mate"},{"first_name":"Yu-Te","full_name":"Hsu, Yu-Te","last_name":"Hsu"},{"orcid":"0000-0001-9760-3147","full_name":"Modic, Kimberly A","last_name":"Modic","first_name":"Kimberly A","id":"13C26AC0-EB69-11E9-87C6-5F3BE6697425"},{"last_name":"Porras","full_name":"Porras, Juan","first_name":"Juan"},{"first_name":"Toshinao","last_name":"Loew","full_name":"Loew, Toshinao"},{"first_name":"Matthieu","last_name":"Le Tacon","full_name":"Le Tacon, Matthieu"},{"first_name":"Huakun","last_name":"Zuo","full_name":"Zuo, Huakun"},{"last_name":"Wang","full_name":"Wang, Jinhua","first_name":"Jinhua"},{"full_name":"Zhu, Zengwei","last_name":"Zhu","first_name":"Zengwei"},{"first_name":"Mun","last_name":"Chan","full_name":"Chan, Mun"},{"last_name":"McDonald","full_name":"McDonald, Ross","first_name":"Ross"},{"full_name":"Lonzarich, Gilbert","last_name":"Lonzarich","first_name":"Gilbert"},{"last_name":"Keimer","full_name":"Keimer, Bernhard","first_name":"Bernhard"},{"full_name":"Sebastian, Suchitra","last_name":"Sebastian","first_name":"Suchitra"},{"full_name":"Harrison, Neil","last_name":"Harrison","first_name":"Neil"}],"oa_version":"Published Version","main_file_link":[{"url":"https://doi.org/10.17863/CAM.50169","open_access":"1"}],"status":"public","title":"Accompanying dataset for 'Hard antinodal gap revealed by quantum oscillations in the pseudogap regime of underdoped high-Tc superconductors'","department":[{"_id":"KiMo"}],"article_processing_charge":"No","tmp":{"short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png"},"date_updated":"2023-08-21T07:06:48Z","publisher":"Apollo - University of Cambridge","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","day":"29","citation":{"ista":"Hartstein M, Hsu Y-T, Modic KA, Porras J, Loew T, Le Tacon M, Zuo H, Wang J, Zhu Z, Chan M, McDonald R, Lonzarich G, Keimer B, Sebastian S, Harrison N. 2020. Accompanying dataset for ‘Hard antinodal gap revealed by quantum oscillations in the pseudogap regime of underdoped high-Tc superconductors’, Apollo - University of Cambridge, <a href=\"https://doi.org/10.17863/cam.50169\">10.17863/cam.50169</a>.","ama":"Hartstein M, Hsu Y-T, Modic KA, et al. Accompanying dataset for “Hard antinodal gap revealed by quantum oscillations in the pseudogap regime of underdoped high-Tc superconductors.” 2020. doi:<a href=\"https://doi.org/10.17863/cam.50169\">10.17863/cam.50169</a>","chicago":"Hartstein, Mate, Yu-Te Hsu, Kimberly A Modic, Juan Porras, Toshinao Loew, Matthieu Le Tacon, Huakun Zuo, et al. “Accompanying Dataset for ‘Hard Antinodal Gap Revealed by Quantum Oscillations in the Pseudogap Regime of Underdoped High-Tc Superconductors.’” Apollo - University of Cambridge, 2020. <a href=\"https://doi.org/10.17863/cam.50169\">https://doi.org/10.17863/cam.50169</a>.","apa":"Hartstein, M., Hsu, Y.-T., Modic, K. A., Porras, J., Loew, T., Le Tacon, M., … Harrison, N. (2020). Accompanying dataset for “Hard antinodal gap revealed by quantum oscillations in the pseudogap regime of underdoped high-Tc superconductors.” Apollo - University of Cambridge. <a href=\"https://doi.org/10.17863/cam.50169\">https://doi.org/10.17863/cam.50169</a>","short":"M. Hartstein, Y.-T. Hsu, K.A. Modic, J. Porras, T. Loew, M. Le Tacon, H. Zuo, J. Wang, Z. Zhu, M. Chan, R. McDonald, G. Lonzarich, B. Keimer, S. Sebastian, N. Harrison, (2020).","ieee":"M. Hartstein <i>et al.</i>, “Accompanying dataset for ‘Hard antinodal gap revealed by quantum oscillations in the pseudogap regime of underdoped high-Tc superconductors.’” Apollo - University of Cambridge, 2020.","mla":"Hartstein, Mate, et al. <i>Accompanying Dataset for “Hard Antinodal Gap Revealed by Quantum Oscillations in the Pseudogap Regime of Underdoped High-Tc Superconductors.”</i> Apollo - University of Cambridge, 2020, doi:<a href=\"https://doi.org/10.17863/cam.50169\">10.17863/cam.50169</a>."},"type":"research_data_reference","abstract":[{"lang":"eng","text":"This research data supports 'Hard antinodal gap revealed by quantum oscillations in the pseudogap regime of underdoped high-Tc superconductors'. A Readme file for plotting each figure is provided."}],"related_material":{"record":[{"status":"public","relation":"used_in_publication","id":"7942"}]},"doi":"10.17863/cam.50169","date_published":"2020-05-29T00:00:00Z","has_accepted_license":"1","_id":"9708","month":"05","oa":1},{"department":[{"_id":"LeSa"}],"title":"Supporting information","article_processing_charge":"No","publisher":"American Chemical Society ","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","date_updated":"2023-08-22T07:49:38Z","date_created":"2021-07-23T12:02:39Z","year":"2020","oa_version":"Published Version","author":[{"last_name":"Gupta","full_name":"Gupta, Chitrak","first_name":"Chitrak"},{"first_name":"Umesh","full_name":"Khaniya, Umesh","last_name":"Khaniya"},{"full_name":"Chan, Chun Kit","last_name":"Chan","first_name":"Chun Kit"},{"full_name":"Dehez, Francois","last_name":"Dehez","first_name":"Francois"},{"first_name":"Mrinal","full_name":"Shekhar, Mrinal","last_name":"Shekhar"},{"first_name":"M.R.","full_name":"Gunner, M.R.","last_name":"Gunner"},{"first_name":"Leonid A","id":"338D39FE-F248-11E8-B48F-1D18A9856A87","full_name":"Sazanov, Leonid A","orcid":"0000-0002-0977-7989","last_name":"Sazanov"},{"first_name":"Christophe","full_name":"Chipot, Christophe","last_name":"Chipot"},{"first_name":"Abhishek","last_name":"Singharoy","full_name":"Singharoy, Abhishek"}],"status":"public","date_published":"2020-05-20T00:00:00Z","related_material":{"record":[{"relation":"used_in_publication","id":"8040","status":"public"}]},"doi":"10.1021/jacs.9b13450.s001","_id":"9713","month":"05","day":"20","citation":{"apa":"Gupta, C., Khaniya, U., Chan, C. K., Dehez, F., Shekhar, M., Gunner, M. R., … Singharoy, A. (2020). Supporting information. American Chemical Society . <a href=\"https://doi.org/10.1021/jacs.9b13450.s001\">https://doi.org/10.1021/jacs.9b13450.s001</a>","ama":"Gupta C, Khaniya U, Chan CK, et al. Supporting information. 2020. doi:<a href=\"https://doi.org/10.1021/jacs.9b13450.s001\">10.1021/jacs.9b13450.s001</a>","ista":"Gupta C, Khaniya U, Chan CK, Dehez F, Shekhar M, Gunner MR, Sazanov LA, Chipot C, Singharoy A. 2020. Supporting information, American Chemical Society , <a href=\"https://doi.org/10.1021/jacs.9b13450.s001\">10.1021/jacs.9b13450.s001</a>.","chicago":"Gupta, Chitrak, Umesh Khaniya, Chun Kit Chan, Francois Dehez, Mrinal Shekhar, M.R. Gunner, Leonid A Sazanov, Christophe Chipot, and Abhishek Singharoy. “Supporting Information.” American Chemical Society , 2020. <a href=\"https://doi.org/10.1021/jacs.9b13450.s001\">https://doi.org/10.1021/jacs.9b13450.s001</a>.","short":"C. Gupta, U. Khaniya, C.K. Chan, F. Dehez, M. Shekhar, M.R. Gunner, L.A. Sazanov, C. Chipot, A. Singharoy, (2020).","ieee":"C. Gupta <i>et al.</i>, “Supporting information.” American Chemical Society , 2020.","mla":"Gupta, Chitrak, et al. <i>Supporting Information</i>. American Chemical Society , 2020, doi:<a href=\"https://doi.org/10.1021/jacs.9b13450.s001\">10.1021/jacs.9b13450.s001</a>."},"type":"research_data_reference","abstract":[{"text":"Additional analyses of the trajectories","lang":"eng"}]},{"oa_version":"Preprint","author":[{"id":"30F3F2F0-F248-11E8-B48F-1D18A9856A87","first_name":"Jana","last_name":"Slovakova","full_name":"Slovakova, Jana"},{"id":"2F74BCDE-F248-11E8-B48F-1D18A9856A87","first_name":"Mateusz K","last_name":"Sikora","full_name":"Sikora, Mateusz K"},{"id":"2F1E1758-F248-11E8-B48F-1D18A9856A87","first_name":"Silvia","last_name":"Caballero Mancebo","orcid":"0000-0002-5223-3346","full_name":"Caballero Mancebo, Silvia"},{"first_name":"Gabriel","id":"2B819732-F248-11E8-B48F-1D18A9856A87","full_name":"Krens, Gabriel","orcid":"0000-0003-4761-5996","last_name":"Krens"},{"last_name":"Kaufmann","full_name":"Kaufmann, Walter","orcid":"0000-0001-9735-5315","id":"3F99E422-F248-11E8-B48F-1D18A9856A87","first_name":"Walter"},{"id":"44C6F6A6-F248-11E8-B48F-1D18A9856A87","first_name":"Karla","last_name":"Huljev","full_name":"Huljev, Karla"},{"first_name":"Carl-Philipp J","id":"39427864-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-0912-4566","full_name":"Heisenberg, Carl-Philipp J","last_name":"Heisenberg"}],"main_file_link":[{"open_access":"1","url":"https://doi.org/10.1101/2020.11.20.391284"}],"status":"public","project":[{"call_identifier":"FP7","name":"International IST Postdoc Fellowship Programme","grant_number":"291734","_id":"25681D80-B435-11E9-9278-68D0E5697425"},{"_id":"260F1432-B435-11E9-9278-68D0E5697425","grant_number":"742573","name":"Interaction and feedback between cell mechanics and fate specification in vertebrate gastrulation","call_identifier":"H2020"},{"grant_number":"187-2013","_id":"2521E28E-B435-11E9-9278-68D0E5697425","name":"Modulation of adhesion function in cell-cell contact formation by cortical tension"}],"page":"41","date_created":"2021-07-29T11:29:50Z","year":"2020","acknowledgement":"We would like to thank Edouard Hannezo for discussions, Shayan Shami Pour and Daniel Capek for help with data analysis, Vanessa Barone and other members of the Heisenberg laboratory for thoughtful discussions and comments on the manuscript. We also thank Jack Merrin for preparing the microwells, and the Scientific Service Units at IST Austria, specifically Bioimaging and Electron Microscopy, and the Zebrafish Facility for continuous support. We acknowledge Hitoshi Morita for the kind gift of VinculinB-GFP plasmid. This research was supported by an ERC Advanced Grant (MECSPEC) to C.-P.H, EMBO Long Term grant (ALTF 187-2013) to M.S and IST Fellow Marie-Curie COFUND No. P_IST_EU01 to J.S.","date_updated":"2024-03-25T23:30:10Z","publisher":"Cold Spring Harbor Laboratory","user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","department":[{"_id":"CaHe"},{"_id":"EM-Fac"},{"_id":"Bio"}],"title":"Tension-dependent stabilization of E-cadherin limits cell-cell contact expansion","article_processing_charge":"No","ec_funded":1,"type":"preprint","abstract":[{"text":"Tension of the actomyosin cell cortex plays a key role in determining cell-cell contact growth and size. The level of cortical tension outside of the cell-cell contact, when pulling at the contact edge, scales with the total size to which a cell-cell contact can grow1,2. Here we show in zebrafish primary germ layer progenitor cells that this monotonic relationship only applies to a narrow range of cortical tension increase, and that above a critical threshold, contact size inversely scales with cortical tension. This switch from cortical tension increasing to decreasing progenitor cell-cell contact size is caused by cortical tension promoting E-cadherin anchoring to the actomyosin cytoskeleton, thereby increasing clustering and stability of E-cadherin at the contact. Once tension-mediated E-cadherin stabilization at the contact exceeds a critical threshold level, the rate by which the contact expands in response to pulling forces from the cortex sharply drops, leading to smaller contacts at physiologically relevant timescales of contact formation. Thus, the activity of cortical tension in expanding cell-cell contact size is limited by tension stabilizing E-cadherin-actin complexes at the contact.","lang":"eng"}],"language":[{"iso":"eng"}],"citation":{"ieee":"J. Slovakova <i>et al.</i>, “Tension-dependent stabilization of E-cadherin limits cell-cell contact expansion,” <i>bioRxiv</i>. Cold Spring Harbor Laboratory, 2020.","mla":"Slovakova, Jana, et al. “Tension-Dependent Stabilization of E-Cadherin Limits Cell-Cell Contact Expansion.” <i>BioRxiv</i>, Cold Spring Harbor Laboratory, 2020, doi:<a href=\"https://doi.org/10.1101/2020.11.20.391284\">10.1101/2020.11.20.391284</a>.","ista":"Slovakova J, Sikora MK, Caballero Mancebo S, Krens G, Kaufmann W, Huljev K, Heisenberg C-PJ. 2020. Tension-dependent stabilization of E-cadherin limits cell-cell contact expansion. bioRxiv, <a href=\"https://doi.org/10.1101/2020.11.20.391284\">10.1101/2020.11.20.391284</a>.","ama":"Slovakova J, Sikora MK, Caballero Mancebo S, et al. Tension-dependent stabilization of E-cadherin limits cell-cell contact expansion. <i>bioRxiv</i>. 2020. doi:<a href=\"https://doi.org/10.1101/2020.11.20.391284\">10.1101/2020.11.20.391284</a>","chicago":"Slovakova, Jana, Mateusz K Sikora, Silvia Caballero Mancebo, Gabriel Krens, Walter Kaufmann, Karla Huljev, and Carl-Philipp J Heisenberg. “Tension-Dependent Stabilization of E-Cadherin Limits Cell-Cell Contact Expansion.” <i>BioRxiv</i>. Cold Spring Harbor Laboratory, 2020. <a href=\"https://doi.org/10.1101/2020.11.20.391284\">https://doi.org/10.1101/2020.11.20.391284</a>.","apa":"Slovakova, J., Sikora, M. K., Caballero Mancebo, S., Krens, G., Kaufmann, W., Huljev, K., &#38; Heisenberg, C.-P. J. (2020). Tension-dependent stabilization of E-cadherin limits cell-cell contact expansion. <i>bioRxiv</i>. Cold Spring Harbor Laboratory. <a href=\"https://doi.org/10.1101/2020.11.20.391284\">https://doi.org/10.1101/2020.11.20.391284</a>","short":"J. Slovakova, M.K. Sikora, S. Caballero Mancebo, G. Krens, W. Kaufmann, K. Huljev, C.-P.J. 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