Sensitivity and dimensionality of atomic environment representations used for machine learning interatomic potentials
Published 2020 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Sensitivity and dimensionality of atomic environment representations used for machine learning interatomic potentials
Authors
Keywords
-
Journal
JOURNAL OF CHEMICAL PHYSICS
Volume 153, Issue 14, Pages 144106
Publisher
AIP Publishing
Online
2020-10-12
DOI
10.1063/5.0016005
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Performance and Cost Assessment of Machine Learning Interatomic Potentials
- (2020) Yunxing Zuo et al. JOURNAL OF PHYSICAL CHEMISTRY A
- Quantifying Chemical Structure and Machine-Learned Atomic Energies in Amorphous and Liquid Silicon
- (2019) Noam Bernstein et al. ANGEWANDTE CHEMIE-INTERNATIONAL EDITION
- Moment tensor potentials as a promising tool to study diffusion processes
- (2019) I.I. Novoselov et al. COMPUTATIONAL MATERIALS SCIENCE
- 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
- Machine Learning Interatomic Potentials as Emerging Tools for Materials Science
- (2019) Volker L. Deringer et al. ADVANCED MATERIALS
- Crowd-sourcing materials-science challenges with the NOMAD 2018 Kaggle competition
- (2019) Christopher Sutton et al. npj Computational Materials
- Machine learning in chemoinformatics and drug discovery
- (2018) Yu-Chen Lo et al. DRUG DISCOVERY TODAY
- Constant size descriptors for accurate machine learning models of molecular properties
- (2018) Christopher R. Collins et al. JOURNAL OF CHEMICAL PHYSICS
- Atomic Energies from a Convolutional Neural Network
- (2018) Xin Chen et al. Journal of Chemical Theory and Computation
- Many-Body Descriptors for Predicting Molecular Properties with Machine Learning: Analysis of Pairwise and Three-Body Interactions in Molecules
- (2018) Wiktor Pronobis et al. Journal of Chemical Theory and Computation
- 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
- Learning atoms for materials discovery
- (2018) Quan Zhou et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Machine learning hydrogen adsorption on nanoclusters through structural descriptors
- (2018) Marc O. J. Jäger et al. npj Computational Materials
- Achieving DFT accuracy with a machine-learning interatomic potential: Thermomechanics and defects in bcc ferromagnetic iron
- (2018) Daniele Dragoni et al. PHYSICAL REVIEW MATERIALS
- Data-driven design of inorganic materials with the Automatic Flow Framework for Materials Discovery
- (2018) Corey Oses et al. MRS BULLETIN
- PubChem 2019 update: improved access to chemical data
- (2018) Sunghwan Kim et al. NUCLEIC ACIDS RESEARCH
- ChEMBL: towards direct deposition of bioassay data
- (2018) David Mendez et al. NUCLEIC ACIDS RESEARCH
- Representations in neural network based empirical potentials
- (2017) Ekin D. Cubuk et al. JOURNAL OF CHEMICAL PHYSICS
- DrugBank 5.0: a major update to the DrugBank database for 2018
- (2017) David S Wishart et al. NUCLEIC ACIDS RESEARCH
- Quantum-chemical insights from deep tensor neural networks
- (2017) Kristof T. Schütt et al. Nature Communications
- An implementation of artificial neural-network potentials for atomistic materials simulations: Performance for TiO 2
- (2016) Nongnuch Artrith et al. COMPUTATIONAL MATERIALS SCIENCE
- Descriptors and their selection methods in QSAR analysis: paradigm for drug design
- (2016) Danishuddin et al. DRUG DISCOVERY TODAY
- Perspective: Machine learning potentials for atomistic simulations
- (2016) Jörg Behler JOURNAL OF CHEMICAL PHYSICS
- Materials science with large-scale data and informatics: Unlocking new opportunities
- (2016) Joanne Hill et al. MRS BULLETIN
- The Cambridge Structural Database
- (2016) Colin R. Groom et al. Acta Crystallographica Section B-Structural Science Crystal Engineering and Materials
- Machine learning for quantum mechanics in a nutshell
- (2015) Matthias Rupp INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY
- Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space
- (2015) Katja Hansen et al. Journal of Physical Chemistry Letters
- The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies
- (2015) Scott Kirklin et al. npj Computational Materials
- 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
- Descriptor Selection Methods in Quantitative Structure–Activity Relationship Studies: A Review Study
- (2013) Mohsen Shahlaei CHEMICAL REVIEWS
- Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)
- (2013) James E. Saal et al. JOM
- Neural networks for local structure detection in polymorphic systems
- (2013) Philipp Geiger et al. JOURNAL OF CHEMICAL PHYSICS
- Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
- (2013) Anubhav Jain et al. APL Materials
- AFLOW: An automatic framework for high-throughput materials discovery
- (2012) Stefano Curtarolo et al. COMPUTATIONAL MATERIALS SCIENCE
- AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations
- (2012) Stefano Curtarolo et al. COMPUTATIONAL MATERIALS SCIENCE
- Enumeration of 166 Billion Organic Small Molecules in the Chemical Universe Database GDB-17
- (2012) Lars Ruddigkeit et al. Journal of Chemical Information and Modeling
- 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
- Atom-centered symmetry functions for constructing high-dimensional neural network potentials
- (2011) Jörg Behler JOURNAL OF CHEMICAL PHYSICS
- High-dimensional neural-network potentials for multicomponent systems: Applications to zinc oxide
- (2011) Nongnuch Artrith 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
- Permutationally invariant potential energy surfaces in high dimensionality
- (2009) Bastiaan J. Braams et al. INTERNATIONAL REVIEWS IN PHYSICAL CHEMISTRY
Discover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversationBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started