Machine learning-accelerated quantum mechanics-based atomistic simulations for industrial applications
出版年份 2020 全文链接
标题
Machine learning-accelerated quantum mechanics-based atomistic simulations for industrial applications
作者
关键词
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出版物
JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN
Volume -, Issue -, Pages -
出版商
Springer Science and Business Media LLC
发表日期
2020-10-09
DOI
10.1007/s10822-020-00346-6
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注意:仅列出部分参考文献,下载原文获取全部文献信息。- Benchmarking of Semiempirical Quantum-Mechanical Methods on Systems Relevant to Computer-Aided Drug Design
- (2020) Kristian Kříž et al. Journal of Chemical Information and Modeling
- Lithium Ion Conduction in Cathode Coating Materials from On-the-Fly Machine Learning
- (2020) Chuhong Wang et al. CHEMISTRY OF MATERIALS
- Optimization and validation of a deep learning CuZr atomistic potential: Robust applications for crystalline and amorphous phases with near-DFT accuracy
- (2020) Christopher M. Andolina et al. JOURNAL OF CHEMICAL PHYSICS
- Inexpensive modeling of quantum dynamics using path integral generalized Langevin equation thermostats
- (2020) Venkat Kapil et al. JOURNAL OF CHEMICAL PHYSICS
- Artificial intelligence in chemistry and drug design
- (2020) Nathan Brown et al. JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN
- Quantum Chemical Calculation of Molecular and Periodic Peptide and Protein Structures
- (2020) Sarah Schmitz et al. JOURNAL OF PHYSICAL CHEMISTRY B
- Comparison of the Performance of Machine Learning Models in Representing High-Dimensional Free Energy Surfaces and Generating Observables
- (2020) Joseph R. Cendagorta et al. JOURNAL OF PHYSICAL CHEMISTRY B
- Combining the Fragmentation Approach and Neural Network Potential Energy Surfaces of Fragments for Accurate Calculation of Protein Energy
- (2020) Zhilong Wang et al. JOURNAL OF PHYSICAL CHEMISTRY B
- Ab initio phase diagram and nucleation of gallium
- (2020) Haiyang Niu et al. Nature Communications
- Efficient training of ANN potentials by including atomic forces via Taylor expansion and application to water and a transition-metal oxide
- (2020) April M. Cooper et al. npj Computational Materials
- Effect of local structural disorder on lithium diffusion behavior in amorphous silicon
- (2020) Wenwen Li et al. Physical Review Materials
- High-Precision Atomic Charge Prediction for Protein Systems Using Fragment Molecular Orbital Calculation and Machine Learning
- (2020) Koichiro Kato et al. Journal of Chemical Information and Modeling
- Pair-distribution-function guided optimization of fingerprints for atom-centered neural network potentials
- (2020) Lei Li et al. JOURNAL OF CHEMICAL PHYSICS
- Predicting the Activity and Selectivity of Bimetallic Metal Catalysts for Ethanol Reforming using Machine Learning
- (2020) Nongnuch Artrith et al. ACS Catalysis
- Exploring chemical compound space with quantum-based machine learning
- (2020) O. Anatole von Lilienfeld et al. Nature Reviews Chemistry
- Fast and Accurate Uncertainty Estimation in Chemical Machine Learning
- (2019) Félix Musil et al. Journal of Chemical Theory and Computation
- GFN2-xTB—An Accurate and Broadly Parametrized Self-Consistent Tight-Binding Quantum Chemical Method with Multipole Electrostatics and Density-Dependent Dispersion Contributions
- (2019) Christoph Bannwarth et al. Journal of Chemical Theory and Computation
- Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
- (2019) Chi Chen et al. CHEMISTRY OF MATERIALS
- Parallel Multistream Training of High-Dimensional Neural Network Potentials
- (2019) Andreas Singraber et al. Journal of Chemical Theory and Computation
- An Overview of Molecular Modeling for Drug Discovery with Specific Illustrative Examples of Applications
- (2019) Maral Aminpour et al. MOLECULES
- Applications of machine learning in drug discovery and development
- (2019) Jessica Vamathevan et al. NATURE REVIEWS DRUG DISCOVERY
- On the chain-melted phase of matter
- (2019) Victor Naden Robinson et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Structure prediction drives materials discovery
- (2019) Artem R. Oganov et al. Nature Reviews Materials
- Anharmonic effects in the low-frequency vibrational modes of aspirin and paracetamol crystals
- (2019) Nathaniel Raimbault et al. PHYSICAL REVIEW MATERIALS
- Screening billions of candidates for solid lithium-ion conductors: A transfer learning approach for small data
- (2019) Ekin D. Cubuk et al. JOURNAL OF CHEMICAL PHYSICS
- Stability and Phase Transition of Cobalt Oxide Phases by Machine Learning Global Potential Energy Surface
- (2019) Fan-Chen Kong et al. Journal of Physical Chemistry C
- Phase Transitions of Hybrid Perovskites Simulated by Machine-Learning Force Fields Trained on the Fly with Bayesian Inference
- (2019) Ryosuke Jinnouchi et al. PHYSICAL REVIEW LETTERS
- Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning
- (2019) Justin S. Smith et al. Nature Communications
- Learning Retrosynthetic Planning through Simulated Experience
- (2019) John S. Schreck et al. ACS Central Science
- An electrostatic spectral neighbor analysis potential for lithium nitride
- (2019) Zhi Deng et al. npj Computational Materials
- Car–Parrinello Monitor for More Robust Born–Oppenheimer Molecular Dynamics
- (2019) Lee-Ping Wang et al. Journal of Chemical Theory and Computation
- Hiding in the Crowd: Spectral Signatures of Overcoordinated Hydrogen-Bond Environments
- (2019) Tobias Morawietz et al. Journal of Physical Chemistry Letters
- Fundamentals of inorganic solid-state electrolytes for batteries
- (2019) Theodosios Famprikis et al. NATURE MATERIALS
- Using Gaussian process regression to simulate the vibrational Raman spectra of molecular crystals
- (2019) Nathaniel Raimbault et al. NEW JOURNAL OF PHYSICS
- Incorporating long-range physics in atomic-scale machine learning
- (2019) Andrea Grisafi et al. JOURNAL OF CHEMICAL PHYSICS
- Optimal designs for pairwise calculation: An application to free energy perturbation in minimizing prediction variability
- (2019) Qingyi Yang et al. JOURNAL OF COMPUTATIONAL CHEMISTRY
- BRADSHAW: a system for automated molecular design
- (2019) Darren V. S. Green et al. JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN
- Unsupervised discovery of solid-state lithium ion conductors
- (2019) Ying Zhang et al. Nature Communications
- Machine learning for heterogeneous catalyst design and discovery
- (2018) Bryan R. Goldsmith et al. AICHE JOURNAL
- Structure-based design of targeted covalent inhibitors
- (2018) Richard Lonsdale et al. CHEMICAL SOCIETY REVIEWS
- DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics
- (2018) Han Wang et al. COMPUTER PHYSICS COMMUNICATIONS
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- (2018) Nongnuch Artrith et al. JOURNAL OF CHEMICAL PHYSICS
- wACSF—Weighted atom-centered symmetry functions as descriptors in machine learning potentials
- (2018) M. Gastegger et al. JOURNAL OF CHEMICAL PHYSICS
- Molecular Dynamics Simulations with Quantum Mechanics/Molecular Mechanics and Adaptive Neural Networks
- (2018) Lin Shen et al. Journal of Chemical Theory and Computation
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- (2018) Florian Strauß et al. Journal of Physical Chemistry C
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- (2018) Ya Zhuo et al. Journal of Physical Chemistry Letters
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- (2018) Tobias Morawietz et al. Journal of Physical Chemistry Letters
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- (2018) Geng Sun et al. JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
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- (2018) Honghui Shang et al. NEW JOURNAL OF PHYSICS
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- (2018) Andrea Grisafi et al. PHYSICAL REVIEW LETTERS
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- (2018) Tian Xie et al. PHYSICAL REVIEW LETTERS
- Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics
- (2018) Linfeng Zhang et al. PHYSICAL REVIEW LETTERS
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- (2018) N. I. Sorokin PHYSICS OF THE SOLID STATE
- The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics
- (2018) Kun Yao et al. Chemical Science
- Large-scale comparison of machine learning methods for drug target prediction on ChEMBL
- (2018) Andreas Mayr et al. Chemical Science
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- (2018) Laura Pérez-Benito et al. Scientific Reports
- Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules
- (2018) Rafael Gómez-Bombarelli et al. ACS Central Science
- Thermodynamic limit for synthesis of metastable inorganic materials
- (2018) Muratahan Aykol et al. Science Advances
- Nuclear quantum effects enter the mainstream
- (2018) Thomas E. Markland et al. Nature Reviews Chemistry
- The structural and compositional factors that control the Li-ion conductivity in LiPON electrolytes
- (2018) Valentina Lacivita et al. CHEMISTRY OF MATERIALS
- Machine learning for molecular and materials science
- (2018) Keith T. Butler et al. NATURE
- Inverse molecular design using machine learning: Generative models for matter engineering
- (2018) Benjamin Sanchez-Lengeling et al. SCIENCE
- Escaping Atom Types in Force Fields Using Direct Chemical Perception
- (2018) David L. Mobley et al. Journal of Chemical Theory and Computation
- SchNetPack: A Deep Learning Toolbox For Atomistic Systems
- (2018) K. T. Schütt et al. Journal of Chemical Theory and Computation
- Solvation Free Energy Calculations with Quantum Mechanics/Molecular Mechanics and Machine Learning Models
- (2018) Pan Zhang et al. JOURNAL OF PHYSICAL CHEMISTRY B
- Silicon Liquid Structure and Crystal Nucleation from Ab Initio Deep Metadynamics
- (2018) Luigi Bonati et al. PHYSICAL REVIEW LETTERS
- Chemical shifts in molecular solids by machine learning
- (2018) Federico M. Paruzzo et al. Nature Communications
- Molecular Dynamics Fingerprints (MDFP): Machine Learning from MD Data To Predict Free-Energy Differences
- (2017) Sereina Riniker Journal of Chemical Information and Modeling
- Study of Li atom diffusion in amorphous Li3PO4 with neural network potential
- (2017) Wenwen Li et al. JOURNAL OF CHEMICAL PHYSICS
- Prospective Evaluation of Free Energy Calculations for the Prioritization of Cathepsin L Inhibitors
- (2017) Bernd Kuhn et al. JOURNAL OF MEDICINAL CHEMISTRY
- Cu Diffusion in Amorphous Ta2O5 Studied with a Simplified Neural Network Potential
- (2017) Wenwen Li et al. JOURNAL OF THE PHYSICAL SOCIETY OF JAPAN
- Combining theory and experiment in electrocatalysis: Insights into materials design
- (2017) Zhi Wei Seh et al. SCIENCE
- Real single ion solvation free energies with quantum mechanical simulation
- (2017) Timothy T. Duignan et al. Chemical Science
- Machine learning molecular dynamics for the simulation of infrared spectra
- (2017) Michael Gastegger et al. Chemical Science
- ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
- (2017) J. S. Smith et al. Chemical Science
- Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models
- (2017) Bowen Liu et al. ACS Central Science
- Machine learning of accurate energy-conserving molecular force fields
- (2017) Stefan Chmiela et al. Science Advances
- An implementation of artificial neural-network potentials for atomistic materials simulations: Performance for TiO 2
- (2016) Nongnuch Artrith et al. COMPUTATIONAL MATERIALS SCIENCE
- Amp : A modular approach to machine learning in atomistic simulations
- (2016) Alireza Khorshidi et al. COMPUTER PHYSICS COMMUNICATIONS
- Ab Initio Quality NMR Parameters in Solid-State Materials Using a High-Dimensional Neural-Network Representation
- (2016) Jérôme Cuny et al. Journal of Chemical Theory and Computation
- Molecular graph convolutions: moving beyond fingerprints
- (2016) Steven Kearnes et al. JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN
- How van der Waals interactions determine the unique properties of water
- (2016) Tobias Morawietz et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Lithium diffusion coefficient in amorphous lithium phosphate thin films measured by secondary ion mass spectroscopy with isotope exchange methods
- (2016) Naoaki Kuwata et al. SOLID STATE IONICS
- The Cambridge Structural Database
- (2016) Colin R. Groom et al. Acta Crystallographica Section B-Structural Science Crystal Engineering and Materials
- First-principles data set of 45,892 isolated and cation-coordinated conformers of 20 proteinogenic amino acids
- (2016) Matti Ropo et al. Scientific Data
- Computational predictions of energy materials using density functional theory
- (2016) Anubhav Jain et al. Nature Reviews Materials
- The ReaxFF reactive force-field: development, applications and future directions
- (2016) Thomas P Senftle et al. npj Computational Materials
- Computational understanding of Li-ion batteries
- (2016) Alexander Urban et al. npj Computational Materials
- Machine learning bandgaps of double perovskites
- (2016) G. Pilania et al. Scientific Reports
- Grand canonical molecular dynamics simulations of Cu–Au nanoalloys in thermal equilibrium using reactive ANN potentials
- (2015) Nongnuch Artrith et al. COMPUTATIONAL MATERIALS SCIENCE
- The impact of molecular dynamics on drug design: applications for the characterization of ligand–macromolecule complexes
- (2015) Jérémie Mortier et al. DRUG DISCOVERY TODAY
- Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties
- (2015) O. Anatole von Lilienfeld et al. INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY
- Crystal structure representations for machine learning models of formation energies
- (2015) Felix Faber et al. INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY
- From the Sabatier principle to a predictive theory of transition-metal heterogeneous catalysis
- (2015) Andrew J. Medford et al. JOURNAL OF CATALYSIS
- High-Dimensional Neural Network Potentials for Organic Reactions and an Improved Training Algorithm
- (2015) Michael Gastegger et al. Journal of Chemical Theory and Computation
- Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach
- (2015) Raghunathan Ramakrishnan et al. Journal of Chemical Theory and Computation
- Phase transition in lithium garnet oxide ionic conductors Li 7 La 3 Zr 2 O 12 : The role of Ta substitution and H 2 O/CO 2 exposure
- (2015) Yuxing Wang et al. JOURNAL OF POWER SOURCES
- Adaptive machine learning framework to accelerateab initiomolecular dynamics
- (2014) Venkatesh Botu et al. INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY
- Binding Free Energy Calculations for Lead Optimization: Assessment of Their Accuracy in an Industrial Drug Design Context
- (2014) Nadine Homeyer et al. Journal of Chemical Theory and Computation
- Accurate Modeling of Organic Molecular Crystals by Dispersion-Corrected Density Functional Tight Binding (DFTB)
- (2014) Jan Gerit Brandenburg et al. Journal of Physical Chemistry Letters
- Understanding the Composition and Activity of Electrocatalytic Nanoalloys in Aqueous Solvents: A Combination of DFT and Accurate Neural Network Potentials
- (2014) Nongnuch Artrith et al. NANO LETTERS
- How to represent crystal structures for machine learning: Towards fast prediction of electronic properties
- (2014) K. T. Schütt et al. PHYSICAL REVIEW B
- Progress and prospective of solid-state lithium batteries
- (2013) Kazunori Takada ACTA MATERIALIA
- 25th Anniversary Article: Understanding the Lithiation of Silicon and Other Alloying Anodes for Lithium-Ion Batteries
- (2013) Matthew T. McDowell et al. ADVANCED MATERIALS
- Considerations for choosing and using force fields and interatomic potentials in materials science and engineering
- (2013) Chandler A. Becker et al. CURRENT OPINION IN SOLID STATE & MATERIALS SCIENCE
- Tetragonal Li10GeP2S12 and Li7GePS8 – exploring the Li ion dynamics in LGPS Li electrolytes
- (2013) Alexander Kuhn et al. Energy & Environmental Science
- Accuracy Assessment and Automation of Free Energy Calculations for Drug Design
- (2013) Clara D. Christ et al. Journal of Chemical Information and Modeling
- Neural networks for local structure detection in polymorphic systems
- (2013) Philipp Geiger et al. JOURNAL OF CHEMICAL PHYSICS
- Metrics for measuring distances in configuration spaces
- (2013) Ali Sadeghi et al. JOURNAL OF CHEMICAL PHYSICS
- Automated Force Field Parameterization for Nonpolarizable and Polarizable Atomic Models Based on Ab Initio Target Data
- (2013) Lei Huang et al. Journal of Chemical Theory and Computation
- Sensitivity of ab Initio vs Empirical Methods in Computing Structural Effects on NMR Chemical Shifts for the Example of Peptides
- (2013) Chris Vanessa Sumowski et al. Journal of Chemical Theory and Computation
- A Density-Functional Theory-Based Neural Network Potential for Water Clusters Including van der Waals Corrections
- (2013) Tobias Morawietz et al. JOURNAL OF PHYSICAL CHEMISTRY A
- Fast Crystallization of the Phase Change Compound GeTe by Large-Scale Molecular Dynamics Simulations
- (2013) Gabriele C. Sosso et al. Journal of Physical Chemistry Letters
- De Novo Determination of the Crystal Structure of a Large Drug Molecule by Crystal Structure Prediction-Based Powder NMR Crystallography
- (2013) Maria Baias et al. JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
- The high-throughput highway to computational materials design
- (2013) Stefano Curtarolo et al. NATURE MATERIALS
- On representing chemical environments
- (2013) Albert P. Bartók et al. PHYSICAL REVIEW B
- Tuning the Reactivity of aCu/ZnONanocatalyst via Gas Phase Pressure
- (2013) Luis Martínez-Suárez et al. PHYSICAL REVIEW LETTERS
- A neural network potential-energy surface for the water dimer based on environment-dependent atomic energies and charges
- (2012) Tobias Morawietz et al. JOURNAL OF CHEMICAL PHYSICS
- Perspective on density functional theory
- (2012) Kieron Burke JOURNAL OF CHEMICAL PHYSICS
- Systematic Parametrization of Polarizable Force Fields from Quantum Chemistry Data
- (2012) Lee-Ping Wang et al. Journal of Chemical Theory and Computation
- Neural network potentials for metals and oxides - First applications to copper clusters at zinc oxide
- (2012) Nongnuch Artrith et al. PHYSICA STATUS SOLIDI B-BASIC SOLID STATE PHYSICS
- High-dimensional neural network potentials for metal surfaces: A prototype study for copper
- (2012) Nongnuch Artrith et al. PHYSICAL REVIEW B
- Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
- (2012) Matthias Rupp et al. PHYSICAL REVIEW LETTERS
- The Active Site of Methanol Synthesis over Cu/ZnO/Al2O3 Industrial Catalysts
- (2012) M. Behrens et al. SCIENCE
- Alchemical free energy methods for drug discovery: progress and challenges
- (2011) John D Chodera et al. CURRENT OPINION IN STRUCTURAL BIOLOGY
- Atom-centered symmetry functions for constructing high-dimensional neural network potentials
- (2011) Jörg Behler JOURNAL OF CHEMICAL PHYSICS
- Benchmarking Semiempirical Methods for Thermochemistry, Kinetics, and Noncovalent Interactions: OMx Methods Are Almost As Accurate and Robust As DFT-GGA Methods for Organic Molecules
- (2011) Martin Korth et al. Journal of Chemical Theory and Computation
- Advanced Corrections of Hydrogen Bonding and Dispersion for Semiempirical Quantum Mechanical Methods
- (2011) Jan Řezáč et al. Journal of Chemical Theory and Computation
- Potentiostatic Intermittent Titration Technique for Electrodes Governed by Diffusion and Interfacial Reaction
- (2011) Juchuan Li et al. Journal of Physical Chemistry C
- Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations
- (2011) Jörg Behler PHYSICAL CHEMISTRY CHEMICAL PHYSICS
- High-dimensional neural-network potentials for multicomponent systems: Applications to zinc oxide
- (2011) Nongnuch Artrith et al. PHYSICAL REVIEW B
- Li2B6O9F2, a New Acentric Fluorooxoborate
- (2011) Thomas Pilz et al. ZEITSCHRIFT FUR ANORGANISCHE UND ALLGEMEINE CHEMIE
- A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu
- (2010) Stefan Grimme et al. JOURNAL OF CHEMICAL PHYSICS
- Ab initioquality neural-network potential for sodium
- (2010) Hagai Eshet et al. PHYSICAL REVIEW B
- Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons
- (2010) Albert P. Bartók et al. PHYSICAL REVIEW LETTERS
- Basic ingredients of free energy calculations: A review
- (2009) Clara D. Christ et al. JOURNAL OF COMPUTATIONAL CHEMISTRY
- Impedance Analysis of Silicon Nanowire Lithium Ion Battery Anodes
- (2009) Riccardo Ruffo et al. Journal of Physical Chemistry C
- Li-ion diffusion in amorphous Si films prepared by RF magnetron sputtering: A comparison of using liquid and polymer electrolytes
- (2009) J. Xie et al. MATERIALS CHEMISTRY AND PHYSICS
- Determination of the diffusion coefficient of lithium ions in nano-Si
- (2009) N. Ding et al. SOLID STATE IONICS
- Vibrational Spectroscopy and Conformational Structure of Protonated Polyalanine Peptides Isolated in the Gas Phase
- (2008) Timothy D. Vaden et al. JOURNAL OF PHYSICAL CHEMISTRY A
- Metadynamics Simulations of the High-Pressure Phases of Silicon Employing a High-Dimensional Neural Network Potential
- (2008) Jörg Behler et al. PHYSICAL REVIEW LETTERS
- Well-Tempered Metadynamics: A Smoothly Converging and Tunable Free-Energy Method
- (2008) Alessandro Barducci et al. PHYSICAL REVIEW LETTERS
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