[{"file":[{"file_size":1515446,"checksum":"5eeec69a51e192dcd94b955d84423836","date_created":"2023-05-02T07:17:05Z","file_name":"2023_ChemialScience_Chen.pdf","content_type":"application/pdf","date_updated":"2023-05-02T07:17:05Z","success":1,"relation":"main_file","access_level":"open_access","creator":"dernst","file_id":"12883"}],"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","status":"public","publication_identifier":{"issn":["2041-6520"],"eissn":["2041-6539"]},"oa":1,"tmp":{"name":"Creative Commons Attribution 3.0 Unported (CC BY 3.0)","image":"/images/cc_by.png","short":"CC BY (3.0)","legal_code_url":"https://creativecommons.org/licenses/by/3.0/legalcode"},"type":"journal_article","date_published":"2023-04-10T00:00:00Z","language":[{"iso":"eng"}],"oa_version":"Published Version","month":"04","has_accepted_license":"1","publication":"Chemical Science","acknowledgement":"KC acknowledges funding from the China Scholarship Council. KC is grateful for the TUM graduate school finance support to visit Bingqing Cheng's group in IST for two months. We also thankfully acknowledge computational resources provided by the MPCDF Supercomputing Centre.","ddc":["000","540"],"day":"10","doi":"10.1039/d3sc00841j","abstract":[{"text":"Machine learning (ML) has been widely applied to chemical property prediction, most prominently for the energies and forces in molecules and materials. The strong interest in predicting energies in particular has led to a ‘local energy’-based paradigm for modern atomistic ML models, which ensures size-extensivity and a linear scaling of computational cost with system size. However, many electronic properties (such as excitation energies or ionization energies) do not necessarily scale linearly with system size and may even be spatially localized. Using size-extensive models in these cases can lead to large errors. In this work, we explore different strategies for learning intensive and localized properties, using HOMO energies in organic molecules as a representative test case. In particular, we analyze the pooling functions that atomistic neural networks use to predict molecular properties, and suggest an orbital weighted average (OWA) approach that enables the accurate prediction of orbital energies and locations.","lang":"eng"}],"year":"2023","citation":{"chicago":"Chen, Ke, Christian Kunkel, Bingqing Cheng, Karsten Reuter, and Johannes T. Margraf. “Physics-Inspired Machine Learning of Localized Intensive Properties.” <i>Chemical Science</i>. Royal Society of Chemistry, 2023. <a href=\"https://doi.org/10.1039/d3sc00841j\">https://doi.org/10.1039/d3sc00841j</a>.","ieee":"K. Chen, C. Kunkel, B. Cheng, K. Reuter, and J. T. Margraf, “Physics-inspired machine learning of localized intensive properties,” <i>Chemical Science</i>. Royal Society of Chemistry, 2023.","apa":"Chen, K., Kunkel, C., Cheng, B., Reuter, K., &#38; Margraf, J. T. (2023). Physics-inspired machine learning of localized intensive properties. <i>Chemical Science</i>. Royal Society of Chemistry. <a href=\"https://doi.org/10.1039/d3sc00841j\">https://doi.org/10.1039/d3sc00841j</a>","ama":"Chen K, Kunkel C, Cheng B, Reuter K, Margraf JT. Physics-inspired machine learning of localized intensive properties. <i>Chemical Science</i>. 2023. doi:<a href=\"https://doi.org/10.1039/d3sc00841j\">10.1039/d3sc00841j</a>","ista":"Chen K, Kunkel C, Cheng B, Reuter K, Margraf JT. 2023. Physics-inspired machine learning of localized intensive properties. Chemical Science.","mla":"Chen, Ke, et al. “Physics-Inspired Machine Learning of Localized Intensive Properties.” <i>Chemical Science</i>, Royal Society of Chemistry, 2023, doi:<a href=\"https://doi.org/10.1039/d3sc00841j\">10.1039/d3sc00841j</a>.","short":"K. Chen, C. Kunkel, B. Cheng, K. Reuter, J.T. Margraf, Chemical Science (2023)."},"date_updated":"2023-08-01T14:18:10Z","external_id":{"isi":["000971508100001"]},"isi":1,"publisher":"Royal Society of Chemistry","article_type":"original","quality_controlled":"1","file_date_updated":"2023-05-02T07:17:05Z","date_created":"2023-04-30T22:01:06Z","department":[{"_id":"BiCh"}],"article_processing_charge":"No","publication_status":"published","title":"Physics-inspired machine learning of localized intensive properties","license":"https://creativecommons.org/licenses/by/3.0/","scopus_import":"1","_id":"12879","author":[{"full_name":"Chen, Ke","first_name":"Ke","last_name":"Chen","id":"c636c5ca-e8b8-11ed-b2d4-cc2c37613a8d"},{"full_name":"Kunkel, Christian","first_name":"Christian","last_name":"Kunkel"},{"id":"cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9","first_name":"Bingqing","last_name":"Cheng","orcid":"0000-0002-3584-9632","full_name":"Cheng, Bingqing"},{"first_name":"Karsten","last_name":"Reuter","full_name":"Reuter, Karsten"},{"last_name":"Margraf","first_name":"Johannes T.","full_name":"Margraf, Johannes T."}]},{"issue":"24","author":[{"last_name":"Dear","first_name":"Alexander J.","full_name":"Dear, Alexander J."},{"full_name":"Meisl, Georg","first_name":"Georg","last_name":"Meisl"},{"id":"bf63d406-f056-11eb-b41d-f263a6566d8b","full_name":"Šarić, Anđela","orcid":"0000-0002-7854-2139","last_name":"Šarić","first_name":"Anđela"},{"full_name":"Michaels, Thomas C. T.","first_name":"Thomas C. T.","last_name":"Michaels"},{"full_name":"Kjaergaard, Magnus","last_name":"Kjaergaard","first_name":"Magnus"},{"first_name":"Sara","last_name":"Linse","full_name":"Linse, Sara"},{"full_name":"Knowles, Tuomas P. J.","first_name":"Tuomas P. J.","last_name":"Knowles"}],"scopus_import":"1","license":"https://creativecommons.org/licenses/by-nc/3.0/","pmid":1,"_id":"10350","intvolume":"        11","title":"Identification of on- and off-pathway oligomers in amyloid fibril formation","article_processing_charge":"No","date_created":"2021-11-26T09:08:19Z","publication_status":"published","quality_controlled":"1","page":"6236-6247","article_type":"original","publisher":"Royal Society of Chemistry","external_id":{"pmid":["32953019"]},"year":"2020","citation":{"chicago":"Dear, Alexander J., Georg Meisl, Anđela Šarić, Thomas C. T. Michaels, Magnus Kjaergaard, Sara Linse, and Tuomas P. J. Knowles. “Identification of On- and off-Pathway Oligomers in Amyloid Fibril Formation.” <i>Chemical Science</i>. Royal Society of Chemistry, 2020. <a href=\"https://doi.org/10.1039/c9sc06501f\">https://doi.org/10.1039/c9sc06501f</a>.","ieee":"A. J. Dear <i>et al.</i>, “Identification of on- and off-pathway oligomers in amyloid fibril formation,” <i>Chemical Science</i>, vol. 11, no. 24. Royal Society of Chemistry, pp. 6236–6247, 2020.","apa":"Dear, A. J., Meisl, G., Šarić, A., Michaels, T. C. T., Kjaergaard, M., Linse, S., &#38; Knowles, T. P. J. (2020). Identification of on- and off-pathway oligomers in amyloid fibril formation. <i>Chemical Science</i>. Royal Society of Chemistry. <a href=\"https://doi.org/10.1039/c9sc06501f\">https://doi.org/10.1039/c9sc06501f</a>","ama":"Dear AJ, Meisl G, Šarić A, et al. Identification of on- and off-pathway oligomers in amyloid fibril formation. <i>Chemical Science</i>. 2020;11(24):6236-6247. doi:<a href=\"https://doi.org/10.1039/c9sc06501f\">10.1039/c9sc06501f</a>","ista":"Dear AJ, Meisl G, Šarić A, Michaels TCT, Kjaergaard M, Linse S, Knowles TPJ. 2020. Identification of on- and off-pathway oligomers in amyloid fibril formation. Chemical Science. 11(24), 6236–6247.","short":"A.J. Dear, G. Meisl, A. Šarić, T.C.T. Michaels, M. Kjaergaard, S. Linse, T.P.J. Knowles, Chemical Science 11 (2020) 6236–6247.","mla":"Dear, Alexander J., et al. “Identification of On- and off-Pathway Oligomers in Amyloid Fibril Formation.” <i>Chemical Science</i>, vol. 11, no. 24, Royal Society of Chemistry, 2020, pp. 6236–47, doi:<a href=\"https://doi.org/10.1039/c9sc06501f\">10.1039/c9sc06501f</a>."},"date_updated":"2021-11-26T11:21:20Z","abstract":[{"text":"The misfolding and aberrant aggregation of proteins into fibrillar structures is a key factor in some of the most prevalent human diseases, including diabetes and dementia. Low molecular weight oligomers are thought to be a central factor in the pathology of these diseases, as well as critical intermediates in the fibril formation process, and as such have received much recent attention. Moreover, on-pathway oligomeric intermediates are potential targets for therapeutic strategies aimed at interrupting the fibril formation process. However, a consistent framework for distinguishing on-pathway from off-pathway oligomers has hitherto been lacking and, in particular, no consensus definition of on- and off-pathway oligomers is available. In this paper, we argue that a non-binary definition of oligomers' contribution to fibril-forming pathways may be more informative and we suggest a quantitative framework, in which each oligomeric species is assigned a value between 0 and 1 describing its relative contribution to the formation of fibrils. First, we clarify the distinction between oligomers and fibrils, and then we use the formalism of reaction networks to develop a general definition for on-pathway oligomers, that yields meaningful classifications in the context of amyloid formation. By applying these concepts to Monte Carlo simulations of a minimal aggregating system, and by revisiting several previous studies of amyloid oligomers in light of our new framework, we demonstrate how to perform these classifications in practice. For each oligomeric species we obtain the degree to which it is on-pathway, highlighting the most effective pharmaceutical targets for the inhibition of amyloid fibril formation.","lang":"eng"}],"day":"08","doi":"10.1039/c9sc06501f","extern":"1","volume":11,"acknowledgement":"We are grateful to the Schiff Foundation (AJD), Peterhouse, Cambridge (TCTM), the Swiss National Science foundation (TCTM), Ramon Jenkins Fellowship, Sidney Sussex, Cambridge (GM), the Royal Society (AŠ), the Academy of Medical Sciences and Wellcome Trust (AŠ), the Danish Research Council (MK), the Lundbeck Foundation (MK), the Swedish Research Council (SL), the Wellcome Trust (TPJK), the Cambridge Centre for Misfolding Diseases (TPJK), the BBSRC (TPJK), the Frances and Augustus Newman Foundation (TPJK) for financial support. The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP7/2007-2013) through the ERC grants PhysProt (agreement no. 337969), MAMBA (agreement no. 340890) and NovoNordiskFonden (SL).","publication":"Chemical Science","month":"06","oa_version":"Published Version","keyword":["general chemistry"],"language":[{"iso":"eng"}],"type":"journal_article","date_published":"2020-06-08T00:00:00Z","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by-nc/3.0/legalcode","image":"/images/cc_by_nc.png","name":"Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)","short":"CC BY-NC (3.0)"},"oa":1,"publication_identifier":{"issn":["2041-6520"],"eissn":["2041-6539"]},"status":"public","user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","main_file_link":[{"open_access":"1","url":"https://pubs.rsc.org/en/content/articlehtml/2020/sc/c9sc06501f"}]},{"language":[{"iso":"eng"}],"has_accepted_license":"1","publication":"Chemical Science","month":"07","oa_version":"Published Version","status":"public","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","file":[{"relation":"main_file","access_level":"open_access","file_id":"7363","creator":"dernst","date_created":"2020-01-26T15:04:44Z","checksum":"70c7c2ce5430b6e8605ccbf0275f1e80","file_size":992106,"date_updated":"2020-07-14T12:47:55Z","file_name":"2017_ChemicalScience_Mahne.pdf","content_type":"application/pdf"}],"type":"journal_article","date_published":"2017-07-31T00:00:00Z","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"oa":1,"publication_identifier":{"eissn":["2041-6539"],"issn":["2041-6520"]},"file_date_updated":"2020-07-14T12:47:55Z","quality_controlled":"1","page":"6716-6729","article_type":"original","publisher":"RSC","issue":"10","author":[{"last_name":"Mahne","first_name":"Nika","full_name":"Mahne, Nika"},{"full_name":"Fontaine, Olivier","first_name":"Olivier","last_name":"Fontaine"},{"full_name":"Thotiyl, Musthafa Ottakam","first_name":"Musthafa Ottakam","last_name":"Thotiyl"},{"first_name":"Martin","last_name":"Wilkening","full_name":"Wilkening, Martin"},{"id":"A8CA28E6-CE23-11E9-AD2D-EC27E6697425","last_name":"Freunberger","first_name":"Stefan Alexander","full_name":"Freunberger, Stefan Alexander","orcid":"0000-0003-2902-5319"}],"_id":"7292","intvolume":"         8","title":"Mechanism and performance of lithium–oxygen batteries – a perspective","date_created":"2020-01-15T12:15:42Z","article_processing_charge":"No","publication_status":"published","ddc":["540"],"extern":"1","volume":8,"year":"2017","citation":{"ista":"Mahne N, Fontaine O, Thotiyl MO, Wilkening M, Freunberger SA. 2017. Mechanism and performance of lithium–oxygen batteries – a perspective. Chemical Science. 8(10), 6716–6729.","mla":"Mahne, Nika, et al. “Mechanism and Performance of Lithium–Oxygen Batteries – a Perspective.” <i>Chemical Science</i>, vol. 8, no. 10, RSC, 2017, pp. 6716–29, doi:<a href=\"https://doi.org/10.1039/c7sc02519j\">10.1039/c7sc02519j</a>.","short":"N. Mahne, O. Fontaine, M.O. Thotiyl, M. Wilkening, S.A. Freunberger, Chemical Science 8 (2017) 6716–6729.","ieee":"N. Mahne, O. Fontaine, M. O. Thotiyl, M. Wilkening, and S. A. Freunberger, “Mechanism and performance of lithium–oxygen batteries – a perspective,” <i>Chemical Science</i>, vol. 8, no. 10. RSC, pp. 6716–6729, 2017.","chicago":"Mahne, Nika, Olivier Fontaine, Musthafa Ottakam Thotiyl, Martin Wilkening, and Stefan Alexander Freunberger. “Mechanism and Performance of Lithium–Oxygen Batteries – a Perspective.” <i>Chemical Science</i>. RSC, 2017. <a href=\"https://doi.org/10.1039/c7sc02519j\">https://doi.org/10.1039/c7sc02519j</a>.","ama":"Mahne N, Fontaine O, Thotiyl MO, Wilkening M, Freunberger SA. Mechanism and performance of lithium–oxygen batteries – a perspective. <i>Chemical Science</i>. 2017;8(10):6716-6729. doi:<a href=\"https://doi.org/10.1039/c7sc02519j\">10.1039/c7sc02519j</a>","apa":"Mahne, N., Fontaine, O., Thotiyl, M. O., Wilkening, M., &#38; Freunberger, S. A. (2017). Mechanism and performance of lithium–oxygen batteries – a perspective. <i>Chemical Science</i>. RSC. <a href=\"https://doi.org/10.1039/c7sc02519j\">https://doi.org/10.1039/c7sc02519j</a>"},"date_updated":"2021-01-12T08:12:49Z","abstract":[{"lang":"eng","text":"Rechargeable Li–O2 batteries have amongst the highest formal energy and could store significantly more energy than other rechargeable batteries in practice if at least a large part of their promise could be realized. Realization, however, still faces many challenges than can only be overcome by fundamental understanding of the processes taking place. Here, we review recent advances in understanding the chemistry of the Li–O2 cathode and provide a perspective on dominant research needs. We put particular emphasis on issues that are often grossly misunderstood: realistic performance metrics and their reporting as well as identifying reversibility and quantitative measures to do so. Parasitic reactions are the prime obstacle for reversible cell operation and have recently been identified to be predominantly caused by singlet oxygen and not by reduced oxygen species as thought before. We discuss the far reaching implications of this finding on electrolyte and cathode stability, electrocatalysis, and future research needs."}],"day":"31","doi":"10.1039/c7sc02519j"},{"oa_version":"Published Version","month":"08","publication":"Chemical Science","keyword":["general chemistry"],"language":[{"iso":"eng"}],"publication_identifier":{"issn":["2041-6520"],"eissn":["2041-6539"]},"oa":1,"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by-nc/3.0/legalcode","image":"/images/cc_by_nc.png","name":"Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)","short":"CC BY-NC (3.0)"},"type":"journal_article","date_published":"2017-08-31T00:00:00Z","main_file_link":[{"open_access":"1","url":"https://pubs.rsc.org/en/content/articlelanding/2017/SC/C7SC01965C"}],"user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","status":"public","date_created":"2021-11-29T09:29:31Z","article_processing_charge":"No","publication_status":"published","intvolume":"         8","title":"Scaling behaviour and rate-determining steps in filamentous self-assembly","scopus_import":"1","_id":"10374","pmid":1,"issue":"10","author":[{"full_name":"Meisl, Georg","last_name":"Meisl","first_name":"Georg"},{"first_name":"Luke","last_name":"Rajah","full_name":"Rajah, Luke"},{"first_name":"Samuel A. I.","last_name":"Cohen","full_name":"Cohen, Samuel A. I."},{"last_name":"Pfammatter","first_name":"Manuela","full_name":"Pfammatter, Manuela"},{"first_name":"Anđela","last_name":"Šarić","orcid":"0000-0002-7854-2139","full_name":"Šarić, Anđela","id":"bf63d406-f056-11eb-b41d-f263a6566d8b"},{"last_name":"Hellstrand","first_name":"Erik","full_name":"Hellstrand, Erik"},{"first_name":"Alexander K.","last_name":"Buell","full_name":"Buell, Alexander K."},{"full_name":"Aguzzi, Adriano","last_name":"Aguzzi","first_name":"Adriano"},{"first_name":"Sara","last_name":"Linse","full_name":"Linse, Sara"},{"first_name":"Michele","last_name":"Vendruscolo","full_name":"Vendruscolo, Michele"},{"full_name":"Dobson, Christopher M.","first_name":"Christopher M.","last_name":"Dobson"},{"full_name":"Knowles, Tuomas P. J.","last_name":"Knowles","first_name":"Tuomas P. J."}],"publisher":"Royal Society of Chemistry","article_type":"original","quality_controlled":"1","page":"7087-7097","day":"31","doi":"10.1039/c7sc01965c","abstract":[{"text":"The formation of filaments from naturally occurring protein molecules is a process at the core of a range of functional and aberrant biological phenomena, such as the assembly of the cytoskeleton or the appearance of aggregates in Alzheimer's disease. The macroscopic behaviour associated with such processes is remarkably diverse, ranging from simple nucleated growth to highly cooperative processes with a well-defined lagtime. Thus, conventionally, different molecular mechanisms have been used to explain the self-assembly of different proteins. Here we show that this range of behaviour can be quantitatively captured by a single unifying Petri net that describes filamentous growth in terms of aggregate number and aggregate mass concentrations. By considering general features associated with a particular network connectivity, we are able to establish directly the rate-determining steps of the overall aggregation reaction from the system's scaling behaviour. We illustrate the power of this framework on a range of different experimental and simulated aggregating systems. The approach is general and will be applicable to any future extensions of the reaction network of filamentous self-assembly.","lang":"eng"}],"year":"2017","citation":{"short":"G. Meisl, L. Rajah, S.A.I. Cohen, M. Pfammatter, A. Šarić, E. Hellstrand, A.K. Buell, A. Aguzzi, S. Linse, M. Vendruscolo, C.M. Dobson, T.P.J. Knowles, Chemical Science 8 (2017) 7087–7097.","mla":"Meisl, Georg, et al. “Scaling Behaviour and Rate-Determining Steps in Filamentous Self-Assembly.” <i>Chemical Science</i>, vol. 8, no. 10, Royal Society of Chemistry, 2017, pp. 7087–97, doi:<a href=\"https://doi.org/10.1039/c7sc01965c\">10.1039/c7sc01965c</a>.","ista":"Meisl G, Rajah L, Cohen SAI, Pfammatter M, Šarić A, Hellstrand E, Buell AK, Aguzzi A, Linse S, Vendruscolo M, Dobson CM, Knowles TPJ. 2017. Scaling behaviour and rate-determining steps in filamentous self-assembly. Chemical Science. 8(10), 7087–7097.","apa":"Meisl, G., Rajah, L., Cohen, S. A. I., Pfammatter, M., Šarić, A., Hellstrand, E., … Knowles, T. P. J. (2017). Scaling behaviour and rate-determining steps in filamentous self-assembly. <i>Chemical Science</i>. Royal Society of Chemistry. <a href=\"https://doi.org/10.1039/c7sc01965c\">https://doi.org/10.1039/c7sc01965c</a>","ama":"Meisl G, Rajah L, Cohen SAI, et al. Scaling behaviour and rate-determining steps in filamentous self-assembly. <i>Chemical Science</i>. 2017;8(10):7087-7097. doi:<a href=\"https://doi.org/10.1039/c7sc01965c\">10.1039/c7sc01965c</a>","chicago":"Meisl, Georg, Luke Rajah, Samuel A. I. Cohen, Manuela Pfammatter, Anđela Šarić, Erik Hellstrand, Alexander K. Buell, et al. “Scaling Behaviour and Rate-Determining Steps in Filamentous Self-Assembly.” <i>Chemical Science</i>. Royal Society of Chemistry, 2017. <a href=\"https://doi.org/10.1039/c7sc01965c\">https://doi.org/10.1039/c7sc01965c</a>.","ieee":"G. Meisl <i>et al.</i>, “Scaling behaviour and rate-determining steps in filamentous self-assembly,” <i>Chemical Science</i>, vol. 8, no. 10. Royal Society of Chemistry, pp. 7087–7097, 2017."},"date_updated":"2021-11-29T10:00:00Z","external_id":{"pmid":["29147538"]},"acknowledgement":"The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP7/2007-2013) through the ERC grant PhysProt (agreement no. 337969) (SL, TPJK), Sidney Sussex College Cambridge (GM), the Frances and Augusta Newman Foundation (TPJK), the Biotechnology and Biological Science Research Council (TPJK), the Swedish Research Council (SL), the Academy of Medical Sciences (AŠ), Wellcome Trust (AŠ), and the Cambridge Centre for Misfolding Diseases (CMD, TPJK, MV).","volume":8,"ddc":["540"],"extern":"1"}]