- Home
- Publications
- Publication Search
- Publication Details
Title
Machine learning for interatomic potential models
Authors
Keywords
-
Journal
JOURNAL OF CHEMICAL PHYSICS
Volume 152, Issue 5, Pages 050902
Publisher
AIP Publishing
Online
2020-02-06
DOI
10.1063/1.5126336
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Machine Learning Classical Interatomic Potentials for Molecular Dynamics from First-Principles Training Data
- (2019) Henry Chan et al. Journal of Physical Chemistry C
- Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
- (2019) Chi Chen et al. CHEMISTRY OF MATERIALS
- Atom-density representations for machine learning
- (2019) Michael J. Willatt et al. JOURNAL OF CHEMICAL PHYSICS
- 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
- Genetic algorithms for computational materials discovery accelerated by machine learning
- (2019) Paul C. Jennings et al. npj Computational Materials
- Machine-learned multi-system surrogate models for materials prediction
- (2019) Chandramouli Nyshadham et al. npj Computational Materials
- Thermal conductivity of single-layer MoS2(1−x)Se2x alloys from molecular dynamics simulations with a machine-learning-based interatomic potential
- (2019) Xiaokun Gu et al. COMPUTATIONAL MATERIALS SCIENCE
- Moment tensor potentials as a promising tool to study diffusion processes
- (2019) I.I. Novoselov et al. COMPUTATIONAL MATERIALS SCIENCE
- General Atomic Neighborhood Fingerprint for Machine Learning-Based Methods
- (2019) Rohit Batra et al. Journal of Physical Chemistry C
- Multi-objective optimization of interatomic potentials with application to MgO
- (2019) Eugene Joseph Ragasa et al. MODELLING AND SIMULATION IN MATERIALS SCIENCE AND ENGINEERING
- Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning
- (2019) Justin S. Smith et al. Nature Communications
- An electrostatic spectral neighbor analysis potential for lithium nitride
- (2019) Zhi Deng et al. npj Computational Materials
- Applying a machine learning interatomic potential to unravel the effects of local lattice distortion on the elastic properties of multi-principal element alloys
- (2019) Mehdi Jafary-Zadeh et al. JOURNAL OF ALLOYS AND COMPOUNDS
- Recent advances and applications of machine learning in solid-state materials science
- (2019) Jonathan Schmidt et al. npj Computational Materials
- De novo exploration and self-guided learning of potential-energy surfaces
- (2019) Noam Bernstein et al. npj Computational Materials
- Fast, accurate, and transferable many-body interatomic potentials by symbolic regression
- (2019) Alberto Hernandez et al. npj Computational Materials
- An atomistic fingerprint algorithm for learning ab initio molecular force fields
- (2018) Yu-Hang Tang 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
- Linearized machine-learning interatomic potentials for non-magnetic elemental metals: Limitation of pairwise descriptors and trend of predictive power
- (2018) Akira Takahashi et al. JOURNAL OF CHEMICAL PHYSICS
- Extending the accuracy of the SNAP interatomic potential form
- (2018) Mitchell A. Wood 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
- Realistic Atomistic Structure of Amorphous Silicon from Machine-Learning-Driven Molecular Dynamics
- (2018) Volker L. Deringer et al. Journal of Physical Chemistry Letters
- Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
- (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
- On-the-Fly Machine Learning of Atomic Potential in Density Functional Theory Structure Optimization
- (2018) T. L. Jacobsen et al. PHYSICAL REVIEW LETTERS
- The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics
- (2018) Kun Yao et al. Chemical Science
- Achieving DFT accuracy with a machine-learning interatomic potential: Thermomechanics and defects in bcc ferromagnetic iron
- (2018) Daniele Dragoni et al. PHYSICAL REVIEW MATERIALS
- The Use of Cluster Expansions to Predict the Structures and Properties of Surfaces and Nanostructured Materials
- (2018) Liang Cao et al. Journal of Chemical Information and Modeling
- 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
- Toward Reliable and Transferable Machine Learning Potentials: Uniform Training by Overcoming Sampling Bias
- (2018) Wonseok Jeong et al. Journal of Physical Chemistry C
- Machine learning material properties from the periodic table using convolutional neural networks
- (2018) Xiaolong Zheng et al. Chemical Science
- Machine learning with force-field-inspired descriptors for materials: Fast screening and mapping energy landscape
- (2018) Kamal Choudhary et al. PHYSICAL REVIEW MATERIALS
- Accelerating high-throughput searches for new alloys with active learning of interatomic potentials
- (2018) Konstantin Gubaev et al. COMPUTATIONAL MATERIALS SCIENCE
- Machine Learning a General-Purpose Interatomic Potential for Silicon
- (2018) Albert P. Bartók et al. Physical Review X
- Study of Li atom diffusion in amorphous Li3PO4 with neural network potential
- (2017) Wenwen Li et al. JOURNAL OF CHEMICAL PHYSICS
- Investigation of a Quantum Monte Carlo Protocol To Achieve High Accuracy and High-Throughput Materials Formation Energies
- (2017) Kayahan Saritas et al. Journal of Chemical Theory and Computation
- Machine Learning Force Field Parameters from Ab Initio Data
- (2017) Ying Li et al. Journal of Chemical Theory and Computation
- Automated Training of ReaxFF Reactive Force Fields for Energetics of Enzymatic Reactions
- (2017) Tomáš Trnka et al. Journal of Chemical Theory and Computation
- 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
- Addressing uncertainty in atomistic machine learning
- (2017) Andrew A. Peterson et al. PHYSICAL CHEMISTRY CHEMICAL PHYSICS
- 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
- 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
- Machine learning in materials informatics: recent applications and prospects
- (2017) Rampi Ramprasad et al. npj Computational Materials
- A universal strategy for the creation of machine learning-based atomistic force fields
- (2017) Tran Doan Huan et al. npj Computational Materials
- Neural network potential for Al-Mg-Si alloys
- (2017) Ryo Kobayashi et al. PHYSICAL REVIEW MATERIALS
- 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
- Perspective: Machine learning potentials for atomistic simulations
- (2016) Jörg Behler JOURNAL OF CHEMICAL PHYSICS
- Molecular graph convolutions: moving beyond fingerprints
- (2016) Steven Kearnes et al. JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN
- Machine Learning Force Fields: Construction, Validation, and Outlook
- (2016) V. Botu et al. Journal of Physical Chemistry C
- Ab Initio-Based Bond Order Potential to Investigate Low Thermal Conductivity of Stanene Nanostructures
- (2016) Mathew J. Cherukara et al. Journal of Physical Chemistry Letters
- Limitations of reactive atomistic potentials in describing defect structures in oxides
- (2016) Teemu Hynninen et al. MODELLING AND SIMULATION IN MATERIALS SCIENCE AND ENGINEERING
- Moment Tensor Potentials: A Class of Systematically Improvable Interatomic Potentials
- (2016) Alexander V. Shapeev MULTISCALE MODELING & SIMULATION
- Machine Learning Energies of 2 Million Elpasolite(ABC2D6)Crystals
- (2016) Felix A. Faber et al. PHYSICAL REVIEW LETTERS
- Challenges in large scale quantum mechanical calculations
- (2016) Laura E. Ratcliff et al. Wiley Interdisciplinary Reviews-Computational Molecular Science
- The ReaxFF reactive force-field: development, applications and future directions
- (2016) Thomas P Senftle et al. npj Computational Materials
- A general-purpose machine learning framework for predicting properties of inorganic materials
- (2016) Logan Ward et al. npj Computational Materials
- Machine learning for quantum mechanics in a nutshell
- (2015) Matthias Rupp INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY
- Gaussian approximation potentials: A brief tutorial introduction
- (2015) Albert P. Bartók et al. INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY
- Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials
- (2015) A.P. Thompson et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Accurate and efficient linear scaling DFT calculations with universal applicability
- (2015) Stephan Mohr et al. PHYSICAL CHEMISTRY CHEMICAL PHYSICS
- First-principles interatomic potentials for ten elemental metals via compressed sensing
- (2015) Atsuto Seko et al. PHYSICAL REVIEW B
- Molecular Dynamics with On-the-Fly Machine Learning of Quantum-Mechanical Forces
- (2015) Zhenwei Li et al. PHYSICAL REVIEW LETTERS
- Towards accurate prediction of catalytic activity in IrO2 nanoclusters via first principles-based variable charge force field
- (2015) F. G. Sen et al. Journal of Materials Chemistry A
- Adaptive machine learning framework to accelerateab initiomolecular dynamics
- (2014) Venkatesh Botu et al. INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY
- Representing potential energy surfaces by high-dimensional neural network potentials
- (2014) J Behler JOURNAL OF PHYSICS-CONDENSED MATTER
- Combinatorial screening for new materials in unconstrained composition space with machine learning
- (2014) B. Meredig et al. PHYSICAL REVIEW B
- Uncertainty Quantification in Multiscale Simulation of Materials: A Prospective
- (2013) Aleksandr Chernatynskiy et al. Annual Review of Materials Research
- 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
- Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies
- (2013) Katja Hansen et al. Journal of Chemical Theory and Computation
- Global optimization of parameters in the reactive force field ReaxFF for SiOH
- (2013) Henrik R. Larsson et al. JOURNAL OF COMPUTATIONAL CHEMISTRY
- Parameterization of a reactive force field using a Monte Carlo algorithm
- (2013) E. Iype et al. JOURNAL OF COMPUTATIONAL CHEMISTRY
- Classical atomistic simulations of surfaces and heterogeneous interfaces with the charge-optimized many body (COMB) potentials
- (2013) Tao Liang et al. MATERIALS SCIENCE & ENGINEERING R-REPORTS
- Cluster expansion made easy with Bayesian compressive sensing
- (2013) Lance J. Nelson et al. PHYSICAL REVIEW B
- Compressive sensing as a paradigm for building physics models
- (2013) Lance J. Nelson et al. PHYSICAL REVIEW B
- On representing chemical environments
- (2013) Albert P. Bartók et al. PHYSICAL REVIEW B
- Computational aspects of many-body potentials
- (2012) Steven J. Plimpton et al. MRS BULLETIN
- Ab initiodetermination of structure-property relationships in alloy nanoparticles
- (2012) Tim Mueller PHYSICAL REVIEW B
- 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
- Statistical approaches to forcefield calibration and prediction uncertainty in molecular simulation
- (2011) Fabien Cailliez et al. JOURNAL OF CHEMICAL PHYSICS
- Atom-centered symmetry functions for constructing high-dimensional neural network potentials
- (2011) Jörg Behler JOURNAL OF CHEMICAL PHYSICS
- Determination of best-fit potential parameters for a reactive force field using a genetic algorithm
- (2011) Poonam Pahari et al. JOURNAL OF MOLECULAR MODELING
- Support vector machine regression (LS-SVM)—an alternative to artificial neural networks (ANNs) for the analysis of quantum chemistry data?
- (2011) Roman M. Balabin et al. PHYSICAL CHEMISTRY CHEMICAL PHYSICS
- High-dimensional neural-network potentials for multicomponent systems: Applications to zinc oxide
- (2011) Nongnuch Artrith et al. PHYSICAL REVIEW B
- Efficient hybrid evolutionary optimization of interatomic potential models
- (2010) W. Michael Brown et al. JOURNAL OF CHEMICAL PHYSICS
- Second-generation charge-optimized many-body potential forSi/SiO2and amorphous silica
- (2010) Tzu-Ray Shan et al. PHYSICAL REVIEW B
- Exact expressions for structure selection in cluster expansions
- (2010) Tim Mueller 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
- Molecular dissociation of hydrogen peroxide (HOOH) on a neural network ab initio potential surface with a new configuration sampling method involving gradient fitting
- (2009) Hung M. Le et al. JOURNAL OF CHEMICAL PHYSICS
- Simultaneous fitting of a potential-energy surface and its corresponding force fields using feedforward neural networks
- (2009) A. Pukrittayakamee et al. JOURNAL OF CHEMICAL PHYSICS
- How to quantify energy landscapes of solids
- (2009) Artem R. Oganov et al. JOURNAL OF CHEMICAL PHYSICS
- Continuum variational and diffusion quantum Monte Carlo calculations
- (2009) R J Needs et al. JOURNAL OF PHYSICS-CONDENSED MATTER
- Bayesian approach to cluster expansions
- (2009) Tim Mueller et al. PHYSICAL REVIEW B
- Cluster expansion method for multicomponent systems based on optimal selection of structures for density-functional theory calculations
- (2009) Atsuto Seko et al. PHYSICAL REVIEW B
- A divide-and-conquer linear scaling three-dimensional fragment method for large scale electronic structure calculations
- (2008) Zhengji Zhao et al. JOURNAL OF PHYSICS-CONDENSED MATTER
- The SIESTA method; developments and applicability
- (2008) Emilio Artacho et al. JOURNAL OF PHYSICS-CONDENSED MATTER
Find the ideal target journal for your manuscript
Explore over 38,000 international journals covering a vast array of academic fields.
SearchAdd 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 Now