@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}, } @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}, isbn = {9781713829546}, issn = {1049-5258}, location = {Vancouver, Canada}, pages = {22361--22372}, publisher = {Neural Information Processing Systems Foundation}, 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}, isbn = {9781713829546}, issn = {1049-5258}, location = {Vancouver, Canada}, pages = {18098--18109}, publisher = {Neural Information Processing Systems Foundation}, title = {{WoodFisher: Efficient second-order approximation for neural network compression}}, 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{9699, abstract = {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 ices. 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.}, author = {Monserrat, Bartomeu and Brandenburg, Jan Gerit and Engel, Edgar A. and Cheng, Bingqing}, booktitle = {arXiv}, title = {{Extracting ice phases from liquid water: Why a machine-learning water model generalizes so well}}, doi = {10.48550/arXiv.2006.13316}, year = {2020}, } @misc{9708, abstract = {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.}, author = {Hartstein, Mate and Hsu, Yu-Te and Modic, Kimberly A and Porras, Juan and Loew, Toshinao and Le Tacon, Matthieu and Zuo, Huakun and Wang, Jinhua and Zhu, Zengwei and Chan, Mun and McDonald, Ross and Lonzarich, Gilbert and Keimer, Bernhard and Sebastian, Suchitra and Harrison, Neil}, publisher = {Apollo - University of Cambridge}, title = {{Accompanying dataset for 'Hard antinodal gap revealed by quantum oscillations in the pseudogap regime of underdoped high-Tc superconductors'}}, doi = {10.17863/cam.50169}, year = {2020}, } @misc{9713, abstract = {Additional analyses of the trajectories}, author = {Gupta, Chitrak and Khaniya, Umesh and Chan, Chun Kit and Dehez, Francois and Shekhar, Mrinal and Gunner, M.R. and Sazanov, Leonid A and Chipot, Christophe and Singharoy, Abhishek}, publisher = {American Chemical Society }, title = {{Supporting information}}, doi = {10.1021/jacs.9b13450.s001}, year = {2020}, } @misc{9776, author = {Grah, Rok and Friedlander, Tamar}, publisher = {Public Library of Science}, title = {{Supporting information}}, doi = {10.1371/journal.pcbi.1007642.s001}, year = {2020}, } @misc{9777, author = {Grah, Rok and Friedlander, Tamar}, publisher = {Public Library of Science}, title = {{Maximizing crosstalk}}, doi = {10.1371/journal.pcbi.1007642.s002}, year = {2020}, } @misc{9779, author = {Grah, Rok and Friedlander, Tamar}, publisher = {Public Library of Science}, title = {{Distribution of crosstalk values}}, doi = {10.1371/journal.pcbi.1007642.s003}, year = {2020}, } @misc{9780, abstract = {PADREV : 4,4'-dimethoxy[1,1'-biphenyl]-2,2',5,5'-tetrol Space Group: C 2 (5), Cell: a 24.488(16)Å b 5.981(4)Å c 3.911(3)Å, α 90° β 91.47(3)° γ 90°}, author = {Schlemmer, Werner and Nothdurft, Philipp and Petzold, Alina and Riess, Gisbert and Frühwirt, Philipp and Schmallegger, Max and Gescheidt-Demner, Georg and Fischer, Roland and Freunberger, Stefan Alexander and Kern, Wolfgang and Spirk, Stefan}, publisher = {CCDC}, title = {{CCDC 1991959: Experimental Crystal Structure Determination}}, doi = {10.5517/ccdc.csd.cc24vsrk}, year = {2020}, } @misc{9798, abstract = {Fitness interactions between mutations can influence a population’s evolution in many different ways. While epistatic effects are difficult to measure precisely, important information is captured by the mean and variance of log fitnesses for individuals carrying different numbers of mutations. We derive predictions for these quantities from a class of simple fitness landscapes, based on models of optimizing selection on quantitative traits. We also explore extensions to the models, including modular pleiotropy, variable effect sizes, mutational bias and maladaptation of the wild type. We illustrate our approach by reanalysing a large dataset of mutant effects in a yeast snoRNA. Though characterized by some large epistatic effects, these data give a good overall fit to the non-epistatic null model, suggesting that epistasis might have limited influence on the evolutionary dynamics in this system. We also show how the amount of epistasis depends on both the underlying fitness landscape and the distribution of mutations, and so is expected to vary in consistent ways between new mutations, standing variation and fixed mutations.}, author = {Fraisse, Christelle and Welch, John J.}, publisher = {Royal Society of London}, title = {{Simulation code for Fig S2 from the distribution of epistasis on simple fitness landscapes}}, doi = {10.6084/m9.figshare.7957472.v1}, year = {2020}, } @misc{9799, abstract = {Fitness interactions between mutations can influence a population’s evolution in many different ways. While epistatic effects are difficult to measure precisely, important information is captured by the mean and variance of log fitnesses for individuals carrying different numbers of mutations. We derive predictions for these quantities from a class of simple fitness landscapes, based on models of optimizing selection on quantitative traits. We also explore extensions to the models, including modular pleiotropy, variable effect sizes, mutational bias and maladaptation of the wild type. We illustrate our approach by reanalysing a large dataset of mutant effects in a yeast snoRNA. Though characterized by some large epistatic effects, these data give a good overall fit to the non-epistatic null model, suggesting that epistasis might have limited influence on the evolutionary dynamics in this system. We also show how the amount of epistasis depends on both the underlying fitness landscape and the distribution of mutations, and so is expected to vary in consistent ways between new mutations, standing variation and fixed mutations.}, author = {Fraisse, Christelle and Welch, John J.}, publisher = {Royal Society of London}, title = {{Simulation code for Fig S1 from the distribution of epistasis on simple fitness landscapes}}, doi = {10.6084/m9.figshare.7957469.v1}, year = {2020}, } @misc{9814, abstract = {Data and mathematica notebooks for plotting figures from Language learning with communication between learners}, author = {Ibsen-Jensen, Rasmus and Tkadlec, Josef and Chatterjee, Krishnendu and Nowak, Martin}, publisher = {Royal Society}, title = {{Data and mathematica notebooks for plotting figures from language learning with communication between learners from language acquisition with communication between learners}}, doi = {10.6084/m9.figshare.5973013.v1}, year = {2020}, } @article{5681, abstract = {We introduce dynamically warping grids for adaptive liquid simulation. Our primary contributions are a strategy for dynamically deforming regular grids over the course of a simulation and a method for efficiently utilizing these deforming grids for liquid simulation. Prior work has shown that unstructured grids are very effective for adaptive fluid simulations. However, unstructured grids often lead to complicated implementations and a poor cache hit rate due to inconsistent memory access. Regular grids, on the other hand, provide a fast, fixed memory access pattern and straightforward implementation. Our method combines the advantages of both: we leverage the simplicity of regular grids while still achieving practical and controllable spatial adaptivity. We demonstrate that our method enables adaptive simulations that are fast, flexible, and robust to null-space issues. At the same time, our method is simple to implement and takes advantage of existing highly-tuned algorithms.}, author = {Hikaru, Ibayashi and Wojtan, Christopher J and Thuerey, Nils and Igarashi, Takeo and Ando, Ryoichi}, issn = {1941-0506}, journal = {IEEE Transactions on Visualization and Computer Graphics}, number = {6}, pages = {2288--2302}, publisher = {IEEE}, title = {{Simulating liquids on dynamically warping grids}}, doi = {10.1109/TVCG.2018.2883628}, volume = {26}, year = {2020}, }