@article{9582, abstract = {The problem of finding dense induced bipartite subgraphs in H-free graphs has a long history, and was posed 30 years ago by Erdős, Faudree, Pach and Spencer. In this paper, we obtain several results in this direction. First we prove that any H-free graph with minimum degree at least d contains an induced bipartite subgraph of minimum degree at least cH log d/log log d, thus nearly confirming one and proving another conjecture of Esperet, Kang and Thomassé. Complementing this result, we further obtain optimal bounds for this problem in the case of dense triangle-free graphs, and we also answer a question of Erdœs, Janson, Łuczak and Spencer.}, author = {Kwan, Matthew Alan and Letzter, Shoham and Sudakov, Benny and Tran, Tuan}, issn = {1439-6912}, journal = {Combinatorica}, number = {2}, pages = {283--305}, publisher = {Springer}, title = {{Dense induced bipartite subgraphs in triangle-free graphs}}, doi = {10.1007/s00493-019-4086-0}, volume = {40}, year = {2020}, } @article{9583, abstract = {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).}, author = {Ferber, Asaf and Kwan, Matthew Alan}, issn = {2050-5094}, journal = {Forum of Mathematics}, publisher = {Cambridge University Press}, title = {{Almost all Steiner triple systems are almost resolvable}}, doi = {10.1017/fms.2020.29}, volume = {8}, year = {2020}, } @article{9630, abstract = {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.}, author = {Edelsbrunner, Herbert and Virk, Ziga and Wagner, Hubert}, issn = {1920180X}, journal = {Journal of Computational Geometry}, number = {2}, pages = {162--182}, publisher = {Carleton University}, title = {{Topological data analysis in information space}}, doi = {10.20382/jocg.v11i2a7}, volume = {11}, year = {2020}, } @inproceedings{9631, abstract = {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.}, author = {Aksenov, Vitaly and Alistarh, Dan-Adrian and Korhonen, Janne}, booktitle = {Advances in Neural Information Processing Systems}, isbn = {9781713829546}, issn = {10495258}, location = {Vancouver, Canada}, pages = {22361--22372}, publisher = {Curran Associates}, title = {{Scalable belief propagation via relaxed scheduling}}, volume = {33}, year = {2020}, } @inproceedings{9632, abstract = {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 neural 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 illustrate 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.}, author = {Singh, Sidak Pal and Alistarh, Dan-Adrian}, booktitle = {Advances in Neural Information Processing Systems}, isbn = {9781713829546}, issn = {10495258}, location = {Vancouver, Canada}, pages = {18098--18109}, publisher = {Curran Associates}, title = {{WoodFisher: Efficient second-order approximation for neural network compression}}, volume = {33}, year = {2020}, } @inproceedings{9633, abstract = {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.}, author = {Confavreux, Basile J and Zenke, Friedemann and Agnes, Everton J. and Lillicrap, Timothy and Vogels, Tim P}, booktitle = {Advances in Neural Information Processing Systems}, issn = {1049-5258}, location = {Vancouver, Canada}, pages = {16398--16408}, title = {{A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network}}, volume = {33}, year = {2020}, } @article{9658, abstract = {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.}, author = {Cheng, Bingqing and Ceriotti, Michele and Tribello, Gareth A.}, issn = {1089-7690}, journal = {The Journal of Chemical Physics}, number = {4}, publisher = {AIP Publishing}, title = {{Classical nucleation theory predicts the shape of the nucleus in homogeneous solidification}}, doi = {10.1063/1.5134461}, volume = {152}, year = {2020}, } @article{9664, abstract = {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.}, author = {Cheng, Bingqing and Frenkel, Daan}, issn = {1079-7114}, journal = {Physical Review Letters}, number = {13}, publisher = {American Physical Society}, title = {{Computing the heat conductivity of fluids from density fluctuations}}, doi = {10.1103/physrevlett.125.130602}, volume = {125}, year = {2020}, } @article{9666, abstract = {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.}, author = {Reinhardt, Aleks and Pickard, Chris J. and Cheng, Bingqing}, issn = {1463-9084}, journal = {Physical Chemistry Chemical Physics}, number = {22}, pages = {12697--12705}, publisher = {Royal Society of Chemistry}, title = {{Predicting the phase diagram of titanium dioxide with random search and pattern recognition}}, doi = {10.1039/d0cp02513e}, volume = {22}, year = {2020}, } @article{9671, abstract = {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.}, author = {Monserrat, Bartomeu and Brandenburg, Jan Gerit and Engel, Edgar A. and Cheng, Bingqing}, issn = {2041-1723}, journal = {Nature Communications}, number = {1}, publisher = {Springer Nature}, title = {{Liquid water contains the building blocks of diverse ice phases}}, doi = {10.1038/s41467-020-19606-y}, volume = {11}, year = {2020}, } @article{9675, abstract = {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.}, author = {Cheng, Bingqing and Griffiths, Ryan-Rhys and Wengert, Simon and Kunkel, Christian and Stenczel, Tamas and Zhu, Bonan and Deringer, Volker L. and Bernstein, Noam and Margraf, Johannes T. and Reuter, Karsten and Csanyi, Gabor}, issn = {1520-4898}, journal = {Accounts of Chemical Research}, number = {9}, pages = {1981--1991}, publisher = {American Chemical Society}, title = {{Mapping materials and molecules}}, doi = {10.1021/acs.accounts.0c00403}, volume = {53}, year = {2020}, } @article{9685, abstract = {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.}, author = {Cheng, Bingqing and Mazzola, Guglielmo and Pickard, Chris J. and Ceriotti, Michele}, issn = {1476-4687}, journal = {Nature}, number = {7824}, pages = {217--220}, publisher = {Springer Nature}, title = {{Evidence for supercritical behaviour of high-pressure liquid hydrogen}}, doi = {10.1038/s41586-020-2677-y}, volume = {585}, year = {2020}, } @unpublished{10012, abstract = {We prove that in the absence of topological changes, the notion of BV solutions to planar multiphase mean curvature flow does not allow for a mechanism for (unphysical) non-uniqueness. Our approach is based on the local structure of the energy landscape near a classical evolution by mean curvature. Mean curvature flow being the gradient flow of the surface energy functional, we develop a gradient-flow analogue of the notion of calibrations. Just like the existence of a calibration guarantees that one has reached a global minimum in the energy landscape, the existence of a "gradient flow calibration" ensures that the route of steepest descent in the energy landscape is unique and stable.}, author = {Fischer, Julian L and Hensel, Sebastian and Laux, Tim and Simon, Thilo}, booktitle = {arXiv}, title = {{The local structure of the energy landscape in multiphase mean curvature flow: weak-strong uniqueness and stability of evolutions}}, year = {2020}, } @unpublished{10022, abstract = {We consider finite-volume approximations of Fokker-Planck equations on bounded convex domains in R^d and study the corresponding gradient flow structures. We reprove the convergence of the discrete to continuous Fokker-Planck equation via the method of Evolutionary Γ-convergence, i.e., we pass to the limit at the level of the gradient flow structures, generalising the one-dimensional result obtained by Disser and Liero. The proof is of variational nature and relies on a Mosco convergence result for functionals in the discrete-to-continuum limit that is of independent interest. Our results apply to arbitrary regular meshes, even though the associated discrete transport distances may fail to converge to the Wasserstein distance in this generality.}, author = {Forkert, Dominik L and Maas, Jan and Portinale, Lorenzo}, booktitle = {arXiv}, pages = {33}, title = {{Evolutionary Γ-convergence of entropic gradient flow structures for Fokker-Planck equations in multiple dimensions}}, year = {2020}, } @inproceedings{10328, abstract = {We discus noise channels in coherent electro-optic up-conversion between microwave and optical fields, in particular due to optical heating. We also report on a novel configuration, which promises to be flexible and highly efficient.}, author = {Lambert, Nicholas J. and Mobassem, Sonia and Rueda Sanchez, Alfredo R and Schwefel, Harald G.L.}, booktitle = {OSA Quantum 2.0 Conference}, isbn = {9-781-5575-2820-9}, location = {Washington, DC, United States}, publisher = {Optica Publishing Group}, title = {{New designs and noise channels in electro-optic microwave to optical up-conversion}}, doi = {10.1364/QUANTUM.2020.QTu8A.1}, year = {2020}, } @article{10336, abstract = {Biological membranes can dramatically accelerate the aggregation of normally soluble protein molecules into amyloid fibrils and alter the fibril morphologies, yet the molecular mechanisms through which this accelerated nucleation takes place are not yet understood. Here, we develop a coarse-grained model to systematically explore the effect that the structural properties of the lipid membrane and the nature of protein–membrane interactions have on the nucleation rates of amyloid fibrils. We identify two physically distinct nucleation pathways—protein-rich and lipid-rich—and quantify how the membrane fluidity and protein–membrane affinity control the relative importance of those molecular pathways. We find that the membrane’s susceptibility to reshaping and being incorporated into the fibrillar aggregates is a key determinant of its ability to promote protein aggregation. We then characterize the rates and the free-energy profile associated with this heterogeneous nucleation process, in which the surface itself participates in the aggregate structure. Finally, we compare quantitatively our data to experiments on membrane-catalyzed amyloid aggregation of α-synuclein, a protein implicated in Parkinson’s disease that predominately nucleates on membranes. More generally, our results provide a framework for understanding macromolecular aggregation on lipid membranes in a broad biological and biotechnological context.}, author = {Krausser, Johannes and Knowles, Tuomas P. J. and Šarić, Anđela}, issn = {1091-6490}, journal = {Proceedings of the National Academy of Sciences}, number = {52}, pages = {33090--33098}, publisher = {National Academy of Sciences}, title = {{Physical mechanisms of amyloid nucleation on fluid membranes}}, doi = {10.1073/pnas.2007694117}, volume = {117}, year = {2020}, } @article{10341, abstract = {Tracing the motion of macromolecules, viruses, and nanoparticles adsorbed onto cell membranes is currently the most direct way of probing the complex dynamic interactions behind vital biological processes, including cell signalling, trafficking, and viral infection. The resulting trajectories are usually consistent with some type of anomalous diffusion, but the molecular origins behind the observed anomalous behaviour are usually not obvious. Here we use coarse-grained molecular dynamics simulations to help identify the physical mechanisms that can give rise to experimentally observed trajectories of nanoscopic objects moving on biological membranes. We find that diffusion on membranes of high fluidities typically results in normal diffusion of the adsorbed nanoparticle, irrespective of the concentration of receptors, receptor clustering, or multivalent interactions between the particle and membrane receptors. Gel-like membranes on the other hand result in anomalous diffusion of the particle, which becomes more pronounced at higher receptor concentrations. This anomalous diffusion is characterised by local particle trapping in the regions of high receptor concentrations and fast hopping between such regions. The normal diffusion is recovered in the limit where the gel membrane is saturated with receptors. We conclude that hindered receptor diffusivity can be a common reason behind the observed anomalous diffusion of viruses, vesicles, and nanoparticles adsorbed on cell and model membranes. Our results enable direct comparison with experiments and offer a new route for interpreting motility experiments on cell membranes.}, author = {Debets, V. E. and Janssen, L. M. C. and Šarić, Anđela}, issn = {1744-683X}, journal = {Soft Matter}, keywords = {condensed matter physics, general chemistry}, number = {47}, pages = {10628--10639}, publisher = {Royal Society of Chemistry}, title = {{Characterising the diffusion of biological nanoparticles on fluid and cross-linked membranes}}, doi = {10.1039/d0sm00712a}, volume = {16}, year = {2020}, } @article{10342, abstract = {The blood-brain barrier is made of polarized brain endothelial cells (BECs) phenotypically conditioned by the central nervous system (CNS). Although transport across BECs is of paramount importance for nutrient uptake as well as ridding the brain of waste products, the intracellular sorting mechanisms that regulate successful receptor-mediated transcytosis in BECs remain to be elucidated. Here, we used a synthetic multivalent system with tunable avidity to the low-density lipoprotein receptor–related protein 1 (LRP1) to investigate the mechanisms of transport across BECs. We used a combination of conventional and super-resolution microscopy, both in vivo and in vitro, accompanied with biophysical modeling of transport kinetics and membrane-bound interactions to elucidate the role of membrane-sculpting protein syndapin-2 on fast transport via tubule formation. We show that high-avidity cargo biases the LRP1 toward internalization associated with fast degradation, while mid-avidity augments the formation of syndapin-2 tubular carriers promoting a fast shuttling across.}, author = {Tian, Xiaohe and Leite, Diana M. and Scarpa, Edoardo and Nyberg, Sophie and Fullstone, Gavin and Forth, Joe and Matias, Diana and Apriceno, Azzurra and Poma, Alessandro and Duro-Castano, Aroa and Vuyyuru, Manish and Harker-Kirschneck, Lena and Šarić, Anđela and Zhang, Zhongping and Xiang, Pan and Fang, Bin and Tian, Yupeng and Luo, Lei and Rizzello, Loris and Battaglia, Giuseppe}, issn = {2375-2548}, journal = {Science Advances}, keywords = {multidisciplinary}, number = {48}, publisher = {American Association for the Advancement of Science}, title = {{On the shuttling across the blood-brain barrier via tubule formation: Mechanism and cargo avidity bias}}, doi = {10.1126/sciadv.abc4397}, volume = {6}, year = {2020}, } @article{10344, abstract = {In this study, we investigate the role of the surface patterning of nanostructures for cell membrane reshaping. To accomplish this, we combine an evolutionary algorithm with coarse-grained molecular dynamics simulations and explore the solution space of ligand patterns on a nanoparticle that promote efficient and reliable cell uptake. Surprisingly, we find that in the regime of low ligand number the best-performing structures are characterized by ligands arranged into long one-dimensional chains that pattern the surface of the particle. We show that these chains of ligands provide particles with high rotational freedom and they lower the free energy barrier for membrane crossing. Our approach reveals a set of nonintuitive design rules that can be used to inform artificial nanoparticle construction and the search for inhibitors of viral entry.}, author = {Forster, Joel C. and Krausser, Johannes and Vuyyuru, Manish R. and Baum, Buzz and Šarić, Anđela}, issn = {1079-7114}, journal = {Physical Review Letters}, number = {22}, publisher = {American Physical Society}, title = {{Exploring the design rules for efficient membrane-reshaping nanostructures}}, doi = {10.1103/physrevlett.125.228101}, volume = {125}, year = {2020}, } @article{10346, abstract = {One of the most robust examples of self-assembly in living organisms is the formation of collagen architectures. Collagen type I molecules are a crucial component of the extracellular matrix, where they self-assemble into fibrils of well-defined axial striped patterns. This striped fibrillar pattern is preserved across the animal kingdom and is important for the determination of cell phenotype, cell adhesion, and tissue regulation and signaling. The understanding of the physical processes that determine such a robust morphology of self-assembled collagen fibrils is currently almost completely missing. Here, we develop a minimal coarse-grained computational model to identify the physical principles of the assembly of collagen-mimetic molecules. We find that screened electrostatic interactions can drive the formation of collagen-like filaments of well-defined striped morphologies. The fibril axial pattern is determined solely by the distribution of charges on the molecule and is robust to the changes in protein concentration, monomer rigidity, and environmental conditions. We show that the striped fibrillar pattern cannot be easily predicted from the interactions between two monomers but is an emergent result of multibody interactions. Our results can help address collagen remodeling in diseases and aging and guide the design of collagen scaffolds for biotechnological applications.}, author = {Hafner, Anne E. and Gyori, Noemi G. and Bench, Ciaran A. and Davis, Luke K. and Šarić, Anđela}, issn = {0006-3495}, journal = {Biophysical Journal}, keywords = {biophysics}, number = {9}, pages = {1791--1799}, publisher = {Cell Press}, title = {{Modeling fibrillogenesis of collagen-mimetic molecules}}, doi = {10.1016/j.bpj.2020.09.013}, volume = {119}, year = {2020}, }