标题
Machine-learned potentials for next-generation matter simulations
作者
关键词
-
出版物
NATURE MATERIALS
Volume 20, Issue 6, Pages 750-761
出版商
Springer Science and Business Media LLC
发表日期
2021-05-28
DOI
10.1038/s41563-020-0777-6
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- New Perspectives on CO2–Pt(111) Interaction with a High-Dimensional Neural Network Potential Energy Surface
- (2020) Marcos del Cueto et al. Journal of Physical Chemistry C
- 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
- Library-Based LAMMPS Implementation of High-Dimensional Neural Network Potentials
- (2019) Andreas Singraber et al. Journal of Chemical Theory and Computation
- Predicting Molecular Energy using Force-Field Optimized Geometries and Atomic Vector Representations Learned from Improved Deep Tensor Neural Network
- (2019) Jianing Lu et al. Journal of Chemical Theory and Computation
- From Molecular Fragments to the Bulk: Development of a Neural Network Potential for MOF-5
- (2019) Marco Eckhoff et al. Journal of Chemical Theory and Computation
- PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges
- (2019) Oliver T. Unke et al. Journal of Chemical Theory and Computation
- Iterative-Learning Strategy for the Development of Application-Specific Atomistic Force Fields
- (2019) Tran Doan Huan et al. Journal of Physical Chemistry C
- Atomic energy mapping of neural network potential
- (2019) Dongsun Yoo et al. Physical Review Materials
- Gaussian Process-Based Refinement of Dispersion Corrections
- (2019) Jonny Proppe et al. Journal of Chemical Theory and Computation
- Recent Advances and Perspectives on Nonadiabatic Mixed Quantum–Classical Dynamics
- (2018) Rachel Crespo-Otero et al. CHEMICAL REVIEWS
- DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics
- (2018) Han Wang et al. COMPUTER PHYSICS COMMUNICATIONS
- Data-driven learning and prediction of inorganic crystal structures
- (2018) Volker L. Deringer et al. FARADAY DISCUSSIONS
- Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning
- (2018) Tristan Bereau et al. JOURNAL OF CHEMICAL PHYSICS
- Hierarchical modeling of molecular energies using a deep neural network
- (2018) Nicholas Lubbers et al. JOURNAL OF CHEMICAL PHYSICS
- SchNet – A deep learning architecture for molecules and materials
- (2018) K. T. Schütt et al. JOURNAL OF CHEMICAL PHYSICS
- Machine learning of molecular properties: Locality and active learning
- (2018) Konstantin Gubaev et al. JOURNAL OF CHEMICAL PHYSICS
- Less is more: Sampling chemical space with active learning
- (2018) Justin S. Smith et al. JOURNAL OF CHEMICAL PHYSICS
- Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials
- (2018) Giulio Imbalzano et al. JOURNAL OF CHEMICAL PHYSICS
- Constant size descriptors for accurate machine learning models of molecular properties
- (2018) Christopher R. Collins et al. JOURNAL OF CHEMICAL PHYSICS
- Reinforced dynamics for enhanced sampling in large atomic and molecular systems
- (2018) Linfeng Zhang et al. JOURNAL OF CHEMICAL PHYSICS
- DeePCG: Constructing coarse-grained models via deep neural networks
- (2018) Linfeng Zhang et al. JOURNAL OF CHEMICAL PHYSICS
- Constructing High-Dimensional Neural Network Potential Energy Surfaces for Gas–Surface Scattering and Reactions
- (2018) Qinghua Liu et al. Journal of Physical Chemistry C
- Inclusion of Machine Learning Kernel Ridge Regression Potential Energy Surfaces in On-the-Fly Nonadiabatic Molecular Dynamics Simulation
- (2018) Deping Hu et al. Journal of Physical Chemistry Letters
- Realistic Atomistic Structure of Amorphous Silicon from Machine-Learning-Driven Molecular Dynamics
- (2018) Volker L. Deringer et al. Journal of Physical Chemistry Letters
- Towards the simulation of biomolecules: optimisation of peptide-capped glycine using FFLUX
- (2018) Joseph C. R. Thacker et al. MOLECULAR SIMULATION
- Understanding the thermal properties of amorphous solids using machine-learning-based interatomic potentials
- (2018) Gabriele C. Sosso et al. MOLECULAR SIMULATION
- Growth Mechanism and Origin of High sp3 Content in Tetrahedral Amorphous Carbon
- (2018) Miguel A. Caro et al. PHYSICAL REVIEW LETTERS
- Data-Driven Learning of Total and Local Energies in Elemental Boron
- (2018) Volker L. Deringer et al. PHYSICAL REVIEW LETTERS
- Local protein solvation drives direct down-conversion in phycobiliprotein PC645 via incoherent vibronic transport
- (2018) Samuel M. Blau et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics
- (2018) Kun Yao et al. Chemical Science
- Machine learning meets volcano plots: Computational discovery of cross-coupling catalysts
- (2018) Benjamin Meyer et al. Chemical Science
- Machine learning of correlated dihedral potentials for atomistic molecular force fields
- (2018) Pascal Friederich et al. Scientific Reports
- Data-driven learning and prediction of inorganic crystal structures
- (2018) Volker L. Deringer et al. FARADAY DISCUSSIONS
- Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules
- (2018) Rafael Gómez-Bombarelli et al. ACS Central Science
- Accelerating the discovery of materials for clean energy in the era of smart automation
- (2018) Daniel P. Tabor et al. Nature Reviews Materials
- Transferable Dynamic Molecular Charge Assignment Using Deep Neural Networks
- (2018) Benjamin Nebgen et al. Journal of Chemical Theory and Computation
- Error-Controlled Exploration of Chemical Reaction Networks with Gaussian Processes
- (2018) Gregor N. Simm et al. Journal of Chemical Theory and Computation
- Modeling the Phase-Change Memory Material, Ge2Sb2Te5, with a Machine-Learned Interatomic Potential
- (2018) Felix C. Mocanu et al. JOURNAL OF PHYSICAL CHEMISTRY B
- Discovering a Transferable Charge Assignment Model Using Machine Learning
- (2018) Andrew E. Sifain et al. Journal of Physical Chemistry Letters
- 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
- Chimera: enabling hierarchy based multi-objective optimization for self-driving laboratories
- (2018) Florian Häse et al. Chemical Science
- Towards exact molecular dynamics simulations with machine-learned force fields
- (2018) Stefan Chmiela et al. Nature Communications
- Phoenics: A Bayesian Optimizer for Chemistry
- (2018) Florian Häse et al. ACS Central Science
- Deep reinforcement learning for de novo drug design
- (2018) Mariya Popova et al. Science Advances
- Adaptive coupling of a deep neural network potential to a classical force field
- (2018) Linfeng Zhang et al. JOURNAL OF CHEMICAL PHYSICS
- Gaussian Process Regression for Transition State Search
- (2018) Alexander Denzel 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
- Deep Learning for Nonadiabatic Excited-State Dynamics
- (2018) Wen-Kai Chen et al. Journal of Physical Chemistry Letters
- Neural network potentials for dynamics and thermodynamics of gold nanoparticles
- (2017) Siva Chiriki et al. JOURNAL OF CHEMICAL PHYSICS
- Interpolation of intermolecular potentials using Gaussian processes
- (2017) Elena Uteva et al. JOURNAL OF CHEMICAL PHYSICS
- Representations in neural network based empirical potentials
- (2017) Ekin D. Cubuk et al. JOURNAL OF CHEMICAL PHYSICS
- Neural Network and Nearest Neighbor Algorithms for Enhancing Sampling of Molecular Dynamics
- (2017) Raimondas Galvelis et al. Journal of Chemical Theory and Computation
- Direct Quantum Dynamics Using Grid-Based Wave Function Propagation and Machine-Learned Potential Energy Surfaces
- (2017) Gareth W. Richings et al. Journal of Chemical Theory and Computation
- Intrinsic Bond Energies from a Bonds-in-Molecules Neural Network
- (2017) Kun Yao et al. Journal of Physical Chemistry Letters
- High-Dimensional Atomistic Neural Network Potentials for Molecule–Surface Interactions: HCl Scattering from Au(111)
- (2017) Brian Kolb et al. Journal of Physical Chemistry Letters
- Accurate Neural Network Description of Surface Phonons in Reactive Gas–Surface Dynamics: N2 + Ru(0001)
- (2017) Khosrow Shakouri et al. Journal of Physical Chemistry Letters
- Proton-Transfer Mechanisms at the Water–ZnO Interface: The Role of Presolvation
- (2017) Vanessa Quaranta et al. Journal of Physical Chemistry Letters
- Neural network predictions of oxygen interactions on a dynamic Pd surface
- (2017) Jacob R. Boes et al. MOLECULAR SIMULATION
- Intrinsic map dynamics exploration for uncharted effective free-energy landscapes
- (2017) Eliodoro Chiavazzo et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Machine learning for quantum dynamics: deep learning of excitation energy transfer properties
- (2017) Florian Häse 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
- Quantum-chemical insights from deep tensor neural networks
- (2017) Kristof T. Schütt et al. Nature Communications
- ANI-1, A data set of 20 million calculated off-equilibrium conformations for organic molecules
- (2017) Justin S. Smith et al. Scientific Data
- Machine learning unifies the modeling of materials and molecules
- (2017) Albert P. Bartók et al. Science Advances
- Machine learning of accurate energy-conserving molecular force fields
- (2017) Stefan Chmiela et al. Science Advances
- The state of understanding of the lithium-ion-battery graphite solid electrolyte interphase (SEI) and its relationship to formation cycling
- (2016) Seong Jin An et al. CARBON
- Perspective: Quantum mechanical methods in biochemistry and biophysics
- (2016) Qiang Cui JOURNAL OF CHEMICAL PHYSICS
- Permutation invariant potential energy surfaces for polyatomic reactions using atomistic neural networks
- (2016) Brian Kolb et al. JOURNAL OF CHEMICAL PHYSICS
- Perspective: Machine learning potentials for atomistic simulations
- (2016) Jörg Behler JOURNAL OF CHEMICAL PHYSICS
- Toward amino acid typing for proteins in FFLUX
- (2016) Timothy L. Fletcher et al. JOURNAL OF COMPUTATIONAL CHEMISTRY
- Mode specific dynamics in the H2 + SH → H + H2S reaction
- (2016) Dandan Lu et al. PHYSICAL CHEMISTRY CHEMICAL PHYSICS
- Comparing molecules and solids across structural and alchemical space
- (2016) Sandip De et al. PHYSICAL CHEMISTRY CHEMICAL PHYSICS
- Machine Learning Energies of 2 Million Elpasolite(ABC2D6)Crystals
- (2016) Felix A. Faber et al. PHYSICAL REVIEW LETTERS
- Machine learning exciton dynamics
- (2016) Florian Häse et al. Chemical Science
- The ReaxFF reactive force-field: development, applications and future directions
- (2016) Thomas P Senftle et al. npj Computational Materials
- Learning from the Harvard Clean Energy Project: The Use of Neural Networks to Accelerate Materials Discovery
- (2015) Edward O. Pyzer-Knapp et al. ADVANCED FUNCTIONAL MATERIALS
- Many Molecular Properties from One Kernel in Chemical Space
- (2015) Raghunathan Ramakrishnan et al. CHIMIA
- Machine learning for quantum mechanics in a nutshell
- (2015) Matthias Rupp INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY
- Constructing high-dimensional neural network potentials: A tutorial review
- (2015) Jörg Behler 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
- Communication: An accurate full 15 dimensional permutationally invariant potential energy surface for the OH + CH4 → H2O + CH3 reaction
- (2015) Jun Li et al. JOURNAL OF CHEMICAL PHYSICS
- High-Dimensional Neural Network Potentials for Organic Reactions and an Improved Training Algorithm
- (2015) Michael Gastegger et al. Journal of Chemical Theory and Computation
- Transferable Atomic Multipole Machine Learning Models for Small Organic Molecules
- (2015) Tristan Bereau et al. Journal of Chemical Theory and Computation
- Deep learning
- (2015) Yann LeCun et al. NATURE
- Adaptive machine learning framework to accelerateab initiomolecular dynamics
- (2014) Venkatesh Botu et al. INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY
- Electrostatic Forces: Formulas for the First Derivatives of a Polarizable, Anisotropic Electrostatic Potential Energy Function Based on Machine Learning
- (2014) Matthew J. L. Mills et al. Journal of Chemical Theory and Computation
- Representing potential energy surfaces by high-dimensional neural network potentials
- (2014) J Behler JOURNAL OF PHYSICS-CONDENSED MATTER
- 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
- Proposed definition of crystal substructure and substructural similarity
- (2014) Lusann Yang et al. PHYSICAL REVIEW B
- How to represent crystal structures for machine learning: Towards fast prediction of electronic properties
- (2014) K. T. Schütt et al. PHYSICAL REVIEW B
- Accuracy and transferability of Gaussian approximation potential models for tungsten
- (2014) Wojciech J. Szlachta et al. PHYSICAL REVIEW B
- Quantum chemistry structures and properties of 134 kilo molecules
- (2014) Raghunathan Ramakrishnan et al. Scientific Data
- Neural networks for local structure detection in polymorphic systems
- (2013) Philipp Geiger et al. JOURNAL OF CHEMICAL PHYSICS
- Communication: Non-radiative recombination via conical intersection at a semiconductor defect
- (2013) Yinan Shu et al. JOURNAL OF CHEMICAL PHYSICS
- Optimizing Protein–Protein van der Waals Interactions for the AMBER ff9x/ff12 Force Field
- (2013) Dail E. Chapman et al. Journal of Chemical Theory and Computation
- Machine-learning approach for one- and two-body corrections to density functional theory: Applications to molecular and condensed water
- (2013) Albert P. Bartók et al. PHYSICAL REVIEW B
- On representing chemical environments
- (2013) Albert P. Bartók et al. PHYSICAL REVIEW B
- Optimizing transition states via kernel-based machine learning
- (2012) Zachary D. Pozun et al. JOURNAL OF CHEMICAL PHYSICS
- Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
- (2012) Matthias Rupp et al. PHYSICAL REVIEW LETTERS
- Denaturant-dependent folding of GFP
- (2012) G. Reddy et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- How Robust Are Protein Folding Simulations with Respect to Force Field Parameterization?
- (2011) Stefano Piana et al. BIOPHYSICAL JOURNAL
- DFTB3: Extension of the Self-Consistent-Charge Density-Functional Tight-Binding Method (SCC-DFTB)
- (2011) Michael Gaus et al. Journal of Chemical Theory and Computation
- 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
- How Fast-Folding Proteins Fold
- (2011) K. Lindorff-Larsen et al. SCIENCE
- Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons
- (2010) Albert P. Bartók et al. PHYSICAL REVIEW LETTERS
- Dynamically Polarizable Water Potential Based on Multipole Moments Trained by Machine Learning
- (2009) Chris M. Handley et al. Journal of Chemical Theory and Computation
- CUR matrix decompositions for improved data analysis
- (2009) Michael W. Mahoney et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Add your recorded webinar
Do you already have a recorded webinar? Grow your audience and get more views by easily listing your recording on Peeref.
Upload NowAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started