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arXiv 2020年

作者： Grigas, Alex T. Mei, Zhe Treado, John D. Levine, Zachary A. Regan, Lynne O'Hern, Corey S. Graduate Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States Integrated Graduate Program in Physical and Engineering Biology Yale University New HavenCT06520 United States Department of Chemistry Yale University New HavenCT06520 United States Department of Mechanical Engineering and Materials Science Yale University New HavenCT06520 United States Department of Pathology Yale University New HavenCT06520 United States Department of Molecular Biophysics and Biochemistry Yale University New HavenCT06520 United States Institute of Quantitative Biology Biochemistry and Biotechnology Centre for Synthetic and Systems Biology School of Biological Sciences University of Edinburgh Department of Physics Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States

The ability to consistently distinguish real protein structures from computationally generated model decoys is not yet a solved problem. One route to distinguish real protein structures from decoys is to delineate the important physical features that specify a real protein. For example, it has long been appreciated that the hydrophobic cores of proteins contribute signifficantly to their stability. As a dataset of decoys to compare with real protein structures, we studied submissions to the bi-annual CASP competition (speciffically CASP11, 12, and 13), in which researchers attempt to predict the structure of a protein only knowing its amino acid sequence. Our analysis reveals that many of the submissions possess cores that do not recapitulate the features that define real proteins. In particular, the model structures appear more densely packed (because of energetically unfavorable atomic overlaps), contain too few residues in the core, and have improper distributions of hydrophobic residues throughout the structure. Based on these observations, we developed a deep learning method, which incorporates key physical features of protein cores, to predict how well a computational model recapitulates the real protein structure without knowledge of the structure of the target sequence. By identifying the important features of protein structure, our method is able to rank decoys from the CASP competitions equally well, if not better than, state-of-the-art methods that incorporate many additional features. Copyright © 2020, The Authors. All rights reserved.

关键词： Proteins

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Small inter-event times govern epidemic spreading on temporal networks

arXiv

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arXiv 2019年

作者： Masuda, Naoki Holme, Petter Department of Mathematics University at Buffalo State University of New York BuffaloNY14260-2900 United States Computational and Data-Enabled Science and Engineering Program University at Buffalo State University of New York BuffaloNY14260-5030 United States Institute of Innovative Research Tokyo Institute of Technology Yokohama226-8503 Japan

Just like the degrees of human and animal interaction networks, the distribution of the times between interactions is known to often be right-skewed and fat-tailed. Both these distributions affect epidemic dynamics strongly, but, as we show in this Letter, for very different reasons. Whereas the high degrees of the tail are critical for facilitating epidemics, it is the small interevent times that control the dynamics of epidemics. We investigate this effect both analytically and numerically for different versions of the Susceptible-Infected-Recovered model on different types of networks. Copyright © 2019, The Authors. All rights reserved.

关键词： Epidemiology

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Ab initio phonon transport across grain boundaries in graphene using machine learning based on small dataset

arXiv

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arXiv 2019年

作者： Hashemi, Amirreza Guo, Ruiqiang Esfarjani, Keivan Lee, Sangyeop Computational Modeling and Simulation Program University of Pittsburgh PittsburghPA15261 United States Department of Mechanical Engineering and Materials Science University of Pittsburgh PittsburghPA15261 United States Department of Mechanical and Aerospace Engineering University of Virginia CharlottesvilleVA22904 United States Department of Materials Science and Engineering University of Virginia CharlottesvilleVA22904 United States Department of Physics University of Virginia CharlottesvilleVA22904 United States Department of Physics and Astronomy University of Pittsburgh PittsburghPA15261 United States

Establishing the structure-property relationship for grain boundaries (GBs) is critical for developing next generation functional materials, but has been severely hampered due to its extremely large configurational space. Atomistic simulations with low computational cost and high predictive power are strongly desirable, but the conventional simulations using empirical interatomic potentials and density functional theory suffer from the lack of predictive power and high computational cost, respectively. A machine learning interatomic potential (MLIP) recently emerged but often requires an extensive size of the training dataset, making it a less feasible approach. Here we demonstrate that an MLIP trained with a rationally designed small training dataset can predict thermal transport across GBs in graphene with ab initio accuracy at an affordable computational cost. In particular, we employed a rational approach based on the structural unit model to find a small set of GBs that can represent the entire configurational space and thus can serve as a cost-effective training dataset for the MLIP. Only 5 GBs were found to be enough to represent the entire configurational space of graphene GBs. Using the atomistic Green's function approach and the MLIP, we revealed that the structure-thermal resistance relation in graphene does not follow the common understanding that large dislocation density causes larger thermal resistance. In fact, thermal resistance is nearly independent of dislocation density at room temperature and is higher when the dislocation density is small at sub-room temperature. We explain this intriguing behavior with the buckling near a GB causing a strong scattering of flexural phonon modes. Our work shows that a machine learning technique combined with conventional wisdom (e.g., structural unit model) can extend the recent success of ab initio thermal transport simulation, which has been mostly limited to single crystals, to complex yet practically importa

关键词： Grain boundaries

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How to verify the precision of density-functional-theory implementations via reproducible and universal workflows

arXiv

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arXiv 2023年

作者： Bosoni, Emanuele Beal, Louis Bercx, Marnik Blaha, Peter Blügel, Stefan Bröder, Jens Callsen, Martin Cottenier, Stefaan Degomme, Augustin Dikan, Vladimir Eimre, Kristjan Flage-Larsen, Espen Fornari, Marco Garcia, Alberto Genovese, Luigi Giantomassi, Matteo Huber, Sebastiaan P. Janssen, Henning Kastlunger, Georg Krack, Matthias Kresse, Georg Kühne, Thomas D. Lejaeghere, Kurt Madsen, Georg K.H. Marsman, Martijn Marzari, Nicola Michalicek, Gregor Mirhosseini, Hossein Müller, Tiziano M.A. Petretto, Guido Pickard, Chris J. Poncé, Samuel Rignanese, Gian-Marco Rubel, Oleg Ruh, Thomas Sluydts, Michael Vanpoucke, Danny E.P. Vijay, Sudarshan Wolloch, Michael Wortmann, Daniel Yakutovich, Aliaksandr V. Yu, Jusong Zadoks, Austin Zhu, Bonan Pizzi, Giovanni Institut de Ciència de Materials de Barcelona ICMAB-CSIC Campus UAB Bellaterra08193 Spain Univ. Grenoble-Alpes CEA IRIG-MEM-L Sim Grenoble38000 France LausanneCH-1015 Switzerland Institute for Materials Chemistry Technical University of Vienna Getreidemarkt 9/165-TC ViennaA-1060 Austria Peter Grünberg Institut Institute for Advanced Simulation Forschungszentrum Jülich JARA JülichD-52425 Germany Forschungszentrum Jülich JülichD-52425 Germany Department of Electromechanical Systems and Metal Engineering Ghent University Belgium Ghent University Belgium Institute of Atomic and Molecular Sciences Academia Sinica Taipei10617 Taiwan Norwegian EuroHPC Competence Center Sigma2 AS Norway SINTEF Industry Materials Physics Oslo Norway Department of Physics Science of Advanced Materials Program Central Michigan University Mount PleasantMI48859 United States Université catholique de Louvain Chemin des Étoiles 8 Louvain-la-Neuve1348 Belgium LausanneCH-1015 Switzerland Kongens Lyngby2800 Denmark VilligenCH-5232 Switzerland University of Vienna Faculty of Physics Center for Computational Materials Science Kolingasse 14-16 ViennaA-1090 Austria VASP Software GmbH Sensengasse 8 ViennaA-1090 Austria Helmholtz-Zentrum Dresden-Rossendorf GörlitzD-02826 Germany Center for Sustainable Systems Design University of Paderborn PaderbornD-33098 Germany OCAS NV ArcelorMittal Global R&D Gent Pres. J. F. Kennedylaan 3 ZelzateB-9060 Belgium Dynamics of Condensed Matter Theoretical Chemistry University of Paderborn PaderbornD-33098 Germany HPE HPC EMEA Research Lab BaselCH-4051 Switzerland Department of Materials Science & Metallurgy University of Cambridge 27 Charles Babbage Road CambridgeCB3 0FS United Kingdom Advanced Institute for Materials Research Tohoku University 2-1-1 Katahira Aoba Sendai980-8577 Japan Department of Materials Science and Engineering McMaster University 1280 Main Street West HamiltonONL8S 4L8 Canada ePot

In the past decades many density-functional theory methods and codes adopting periodic boundary conditions have been developed and are now extensively used in condensed matter physics and materials science research. Only in 2016, however, their precision (i.e., to which extent properties computed with different codes agree among each other) was systematically assessed on elemental crystals: a first crucial step to evaluate the reliability of such computations. We discuss here general recommendations for verification studies aiming at further testing precision and transferability of density-functional-theory computational approaches and codes. We illustrate such recommendations using a greatly expanded protocol covering the whole periodic table from Z=1 to 96 and characterizing 10 prototypical cubic compounds for each element: 4 unaries and 6 oxides, spanning a wide range of coordination numbers and oxidation states. The primary outcome is a reference dataset of 960 equations of state cross-checked between two all-electron codes, then used to verify and improve nine pseudopotential-based approaches. Such effort is facilitated by deploying AiiDA common workflows that perform automatic input parameter selection, provide identical input/output interfaces across codes, and ensure full reproducibility. Finally, we discuss the extent to which the current results for total energies can be reused for different goals (e.g., obtaining formation energies). © 2023, CC BY.

关键词： Density functional theory

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End-to-end symmetry preserving inter-atomic potential energy model for finite and extended systems 32

End-to-end symmetry preserving inter-atomic potential energy...

引用

32nd Conference on Neural Information Processing Systems, NeurIPS 2018

作者： Zhang, Linfeng Han, Jiequn Wang, Han Saidi, Wissam A. Car, Roberto Weinan, E. Program in Applied and Computational Mathematics Princeton University United States Institute of Applied Physics and Computational Mathematics China CAEP Software Center for High Performance Numerical Simulation China Department of Mechanical Engineering and Materials Science University of Pittsburgh United States Department of Chemistry Department of Physics Princeton University United States Princeton Institute for the Science and Technology of Materials Princeton University United States Department of Mathematics Princeton University United States Beijing Institute of Big Data Research China

Machine learning models are changing the paradigm of molecular modeling, which is a fundamental tool for material science, chemistry, and computational biology. Of particular interest is the inter-atomic potential energy surface (PES). Here we develop Deep Potential - Smooth Edition (DeepPot-SE), an end-to-end machine learning-based PES model, which is able to efficiently represent the PES of a wide variety of systems with the accuracy of ab initio quantum mechanics models. By construction, DeepPot-SE is extensive and continuously differentiable, scales linearly with system size, and preserves all the natural symmetries of the system. Further, we show that DeepPot-SE describes finite and extended systems including organic molecules, metals, semiconductors, and insulators with high fidelity. © 2018 Curran Associates Inc..All rights reserved.

关键词： Potential energy

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Critical mass effect in evolutionary games triggered by zealots

arXiv

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arXiv 2019年

作者： Cardillo, Alessio Masuda, Naoki Department of Engineering Mathematics University of Bristol BristolBS8 1UB United Kingdom Department of Computer Science and Mathematics University Rovira i Virgili TarragonaE-43007 Spain University of Zaragoza ZaragozaE-50018 Spain Department of Mathematics University at Buffalo BuffaloNY14260-2900 United States Computational and Data-Enabled Science and Engineering Program University at Buffalo State University of New York BuffaloNY14260-5030 United States

Tiny perturbations may trigger large responses in systems near criticality, shifting them across equilibria. Committed minorities are suggested to be responsible for the emergence of collective behaviors in many physical, social, and biological systems. Using evolutionary game theory, we address the question whether a finite fraction of zealots can drive the system to large-scale coordination. We find that a tipping point exists in coordination games, whereas the same phenomenon depends on the selection pressure, update rule, and network structure in other types of games. Our study paves the way to understand social systems driven by the individuals’ benefit in presence of zealots, such as human vaccination behavior or cooperative transports in animal groups. Copyright © 2019, The Authors. All rights reserved.

关键词： Game theory

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Pretreatment Chemometrics in Holistic Biogas Life Cycle Assessment: Framing Case Study with Carica papaya

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Waste and Biomass Valorization 2018年 1-14页

作者： Jensen, Cory D. Olugbemide, A.D. Akpa, F.A.O. Oladipo, Ajayi College of Engineering and Computational Sciences Colorado School of Mines GoldenCO United States Division of Liberal Arts and International Studies Joint Policy Program with the University of Colorado Colorado School of Mines GoldenCO United States Auchi Polytechnic AuchiEdo State Nigeria Department of Basic Sciences Auchi Polytechnic AuchiEdo State Nigeria Department of Science Laboratory Technology Auchi Polytechnic AuchiEdo State Nigeria Department of Agricultural and Bio-Environmental Engineering Auchi Polytechnic AuchiEdo State Nigeria

This research explores synergistic pre-treatment parameters and chemometrics as applicable to systems level biogas related research frameworks (such as the sustainable development goals) for anaerobic digestion biomass processes-policy. Specifically, the hypothesis that simple feedstock pretreatment-digestion methods can be applied to rapidly evaluate biochemical-biogas potential of anaerobically digested Carica papaya petioles was tested. Pretreatment parameters explored include hydration, i.e. 1:4, 1:6, 1:8 and 1:10 substrate mass to water weight ratios and hydrochloric acid pretreatment chemistry, i.e. 1%—0.3 M, 3%—0.7 M, 5%—1.0 M, in batch digestions experiments with non-inoculated C. papaya petioles. Biomass feedstock was obtained from Edo State, Nigeria. Digestions were performed at ambient temperature, i.e. 27 °C, for 3 weeks. Pretreated C. papaya petiole samples were analyzed with Fourier transform infrared spectroscopy to characterize spectra with characteristic absorbance ratios indicative of physicochemical changes in biomass that include;cross-linked lignin ratio, the lateral order index, and hydrogen bond intensity. Cumulative gaseous yields from C. papaya petiole digestions were fit to the modified Gompertz form and optimized with non-linear regression in Matlab® to derive mathematical parameters, i.e. daily maximum production rate of biogas, a lag phase prior to biogas production, and biogas potential. An optimum C. papaya petiole pretreatment digestion scenario, i.e. 1:4 substrate-to-water ratio and 3%, i.e. 0.7 M, HCl pretreatment resulted in a 150% increase in yield, (10.9 mL gas/g VS) compared to non-pretreated C. papaya petiole digestion. Analysis with the modified Gompertz form also suggests rapid biogas potential assessment can be performed in a few days-time, i.e. approximately 10 days with distinct visual characterization of individual digestion performance. It was concluded that the applied digestion procedures with the use of C. papaya peti

关键词： Anaerobic digestion

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Tegavivint triggers TECR-dependent nonapoptotic cancer cell death

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Nature chemical biology 2025年 2025 May 26页

作者： Logan Leak Ziwei Wang Alby J Joseph Brianna Johnson Alyssa A Chan Cassandra M Decosto Leslie Magtanong Pin-Joe Ko Weaverly Colleen Lee Joan Ritho Sophia Manukian Alec Millner Shweta Chitkara Jennifer J Salinas Rachid Skouta Matthew G Rees Melissa M Ronan Jennifer A Roth Chad L Myers Jason Moffat Charles Boone Steven J Bensinger David A Nathanson G Ekin Atilla-Gokcumen Everett J Moding Scott J Dixon Department of Biology Stanford University Stanford CA USA. Department of Radiation Oncology Stanford University School of Medicine Stanford CA USA. Department of Chemistry University at Buffalo The State University of New York Buffalo NY USA. Department of Molecular and Medical Pharmacology University of California Los Angeles Los Angeles CA USA. Department of Chemistry University of Massachusetts Amherst Amherst MA USA. Department of Biology University of Massachusetts Amherst Amherst MA USA. Broad Institute of MIT and Harvard Cambridge MA USA. Department of Computer Science and Engineering Bioinformatics and Computational Biology Graduate Program University of Minnesota-Twin Cities Minneapolis MN USA. Program in Genetics & Genome Biology The Hospital for Sick Children Toronto Ontario Canada. Department of Molecular Genetics University of Toronto Toronto Ontario Canada. Donnelly Centre University of Toronto Toronto Ontario Canada. RIKEN Center for Sustainable Resource Science Saitama Japan. Department of Microbiology Immunology and Molecular Genetics University of California Los Angeles Los Angeles CA USA. UCLA Lipidomics Laboratory University of California Los Angeles Los Angeles CA USA. Jonsson Comprehensive Cancer Center David Geffen School of Medicine University of California Los Angeles Los Angeles CA USA. Department of Biology Stanford University Stanford CA USA. sjdixon@stanford.edu.

Small molecules that induce nonapoptotic cell death are of fundamental mechanistic interest and may be useful to treat certain cancers. Here we report that tegavivint, a drug candidate undergoing human clinical trials, can activate a unique mechanism of nonapoptotic cell death in sarcomas and other cancer cells. This lethal mechanism is distinct from ferroptosis, necroptosis and pyroptosis and requires the lipid metabolic enzyme trans-2,3-enoyl-CoA reductase (TECR). TECR is canonically involved in the synthesis of very-long-chain fatty acids but appears to promote nonapoptotic cell death in response to CIL56 and tegavivint via the synthesis of the saturated long-chain fatty acid palmitate. These findings outline a lipid-dependent nonapoptotic cell death mechanism that can be induced by a drug candidate currently being tested in humans.

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High-Throughput computational Studies in Catalysis and Materials Research, and their Impact on Rational Design

arXiv

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arXiv 2019年

作者： Afzal, Mohammad Atif Faiz Hachmann, Johannes Schrödinger Inc. PortlandOR97204 United States Department of Chemical and Biological Engineering University at Buffalo State University of New York BuffaloNY14260 United States Computational and Data-Enabled Science and Engineering Graduate Program University at Buffalo The State University of New York BuffaloNY14260 United States New York State Center of Excellence in Materials Informatics BuffaloNY14203 United States

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End-to-end symmetry preserving inter-atomic potential energy model for finite and extended systems 18

End-to-end symmetry preserving inter-atomic potential energy...

引用

Proceedings of the 32nd International Conference on Neural Information Processing Systems

作者： Linfeng Zhang Jiequn Han Han Wang Wissam A. Saidi Roberto Car E. Weinan Program in Applied and Computational Mathematics Princeton University Institute of Applied Physics and Computational Mathematics China and CAEP Software Center for High Performance Numerical Simulation China Department of Mechanical Engineering and Materials Science University of Pittsburgh Program in Applied and Computational Mathematics Princeton University and Department of Chemistry and Department of Physics Princeton University and Princeton Institute for the Science and Technology of Materials Princeton University Program in Applied and Computational Mathematics Princeton University and Department of Mathematics Princeton University and Beijing Institute of Big Data Research China

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