Active learning of linearly parametrized interatomic potentials
TLDR
It is shown that the proposed active learning approach to the fitting of machine learning interatomic potentials is highly efficient in training potentials on the fly, ensuring that no extrapolation is attempted and leading to a completely reliable atomistic simulation without any significant decrease in accuracy.About:
This article is published in Computational Materials Science.The article was published on 2017-12-01 and is currently open access. It has received 386 citations till now. The article focuses on the topics: Active learning (machine learning) & Interatomic potential.read more
Citations
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Journal ArticleDOI
SchNet - A deep learning architecture for molecules and materials.
Kristof T. Schütt,Huziel E. Sauceda,Pieter-Jan Kindermans,Alexandre Tkatchenko,Klaus-Robert Müller +4 more
TL;DR: SchNet as mentioned in this paper is a deep learning architecture specifically designed to model atomistic systems by making use of continuous-filter convolutional layers, where the model learns chemically plausible embeddings of atom types across the periodic table.
Journal ArticleDOI
SchNet - a deep learning architecture for molecules and materials
Kristof T. Schütt,Huziel E. Sauceda,Pieter-Jan Kindermans,Alexandre Tkatchenko,Klaus-Robert Müller +4 more
TL;DR: SchNet as discussed by the authors is a deep learning architecture specifically designed to model atomistic systems by making use of continuous-filter convolutional layers, which can accurately predict a range of properties across chemical space for molecules and materials.
Journal ArticleDOI
Towards exact molecular dynamics simulations with machine-learned force fields.
Stefan Chmiela,Huziel E. Sauceda,Klaus-Robert Müller,Klaus-Robert Müller,Klaus-Robert Müller,Alexandre Tkatchenko +5 more
TL;DR: A flexible machine-learning force-field with high-level accuracy for molecular dynamics simulations is developed, for flexible molecules with up to a few dozen atoms and insights into the dynamical behavior of these molecules are provided.
Journal ArticleDOI
Performance and Cost Assessment of Machine Learning Interatomic Potentials.
Yunxing Zuo,Chi Chen,Xiang-Guo Li,Zhi Deng,Yiming Chen,Jörg Behler,Gábor Csányi,Alexander V. Shapeev,Aidan P. Thompson,Mitchell Wood,Shyue Ping Ong +10 more
TL;DR: A comprehensive evaluation of ML-IAPs based on four local environment descriptors --- atom-centered symmetry functions (ACSF), smooth overlap of atomic positions (SOAP), the Spectral Neighbor Analysis Potential (SNAP) bispectrum components, and moment tensors --- using a diverse data set generated using high-throughput density functional theory (DFT) calculations.
Journal ArticleDOI
Less is more: Sampling chemical space with active learning
TL;DR: Active learning via query by committee (AL-QBC) as discussed by the authors uses the disagreement between an ensemble of ML potentials to infer the reliability of the ensemble's prediction, which improves the overall fitness of ANAKIN-ME (ANI) deep learning potentials by mitigating human biases in deciding what new training data to use.
References
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Generalized Gradient Approximation Made Simple
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Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set.
Georg Kresse,Jürgen Furthmüller +1 more
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Journal ArticleDOI
Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set
Georg Kresse,Jürgen Furthmüller +1 more
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