Extending the accuracy of the SNAP interatomic potential form

Wood, Mitchell; Thompson, Aidan P.

doi:10.1063/1.5017641

Cited by 187 publications

(129 citation statements)

References 63 publications

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“…19). Wood et al 442 proposed an improvement of the Fig. 18 Phase diagram of 45,000 Li x Si 1−x structures depicting the formation energies predicted using the general neural network potential (green stars) and the density functional theory reference formation energies (black circles).…”

Section: Machine Learning Force Fieldsmentioning

confidence: 99%

Recent advances and applications of machine learning in solid-state materials science

et al. 2019

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One of the most exciting tools that have entered the material science toolbox in recent years is machine learning. This collection of statistical methods has already proved to be capable of considerably speeding up both fundamental and applied research. At present, we are witnessing an explosion of works that develop and apply machine learning to solid-state systems. We provide a comprehensive overview and analysis of the most recent research in this topic. As a starting point, we introduce machine learning principles, algorithms, descriptors, and databases in materials science. We continue with the description of different machine learning approaches for the discovery of stable materials and the prediction of their crystal structure. Then we discuss research in numerous quantitative structure-property relationships and various approaches for the replacement of first-principle methods by machine learning. We review how active learning and surrogate-based optimization can be applied to improve the rational design process and related examples of applications. Two major questions are always the interpretability of and the physical understanding gained from machine learning models. We consider therefore the different facets of interpretability and their importance in materials science. Finally, we propose solutions and future research paths for various challenges in computational materials science.

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Section: Machine Learning Force Fieldsmentioning

confidence: 99%

Recent advances and applications of machine learning in solid-state materials science

et al. 2019

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“…[76,218] The spectral neighbor analysis potential (SNAP) fits the potential energy surface to a linear or quadratic model of the coefficients of the bispectrum of local atomic density functions. [107,131,[218][219][220] A particular challenge in IAP development for battery materials-many of which are ionic compoundsis the treatment of long-range electrostatics. Recently, Deng et al [131] has augmented the linear SNAP approach with an electrostatic term and developed an eSNAP model for Li 3 N, one of the earliest discovered lithium solid electrolytes with anisotropic Li diffusion mechanism in different crystallography orientations.…”

Section: Machine Learning Interatomic Potentialsmentioning

confidence: 99%

A Critical Review of Machine Learning of Energy Materials

Chen

Zuo

et al. 2020

Advanced Energy Materials

420

281

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Machine learning (ML) is rapidly revolutionizing many fields and is starting to change landscapes for physics and chemistry. With its ability to solve complex tasks autonomously, ML is being exploited as a radically new way to help find material correlations, understand materials chemistry, and accelerate the discovery of materials. Here, an in‐depth review of the application of ML to energy materials, including rechargeable alkali‐ion batteries, photovoltaics, catalysts, thermoelectrics, piezoelectrics, and superconductors, is presented. A conceptual framework is first provided for ML in materials science, with a broad overview of different ML techniques as well as best practices. This is followed by a critical discussion of how ML is applied in energy materials. This review is concluded with the perspectives on major challenges and opportunities in this exciting field.

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“…DISCUSSION AND CONCLUSIONTo conclude, we have developed SNAP models for fcc Ni, Cu as well as the binary Ni-Mo system.For fcc metals such as Ni and Cu, we find that the elemental SNAP models offer only a modest improvement over well-established EAM/MEAM potentials. This is unlike the case for bcc metals such as Mo, Ta and W, for which EAM/MEAM potentials generally perform relatively poorly and SNAP models have been demonstrated to lead to significant reductions in prediction error in energies, forces and various materials properties5,12,28 .…”

mentioning

confidence: 91%

Quantum-accurate spectral neighbor analysis potential models for Ni-Mo binary alloys and fcc metals

Chen

et al. 2018

Phys. Rev. B

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In recent years, efficient inter-atomic potentials approaching the accuracy of density functional theory (DFT) calculations have been developed using rigorous atomic descriptors satisfying strict invariances, for example, to translation, rotation, permutation of homonuclear atoms, among others. In this work, we generalize the spectral neighbor analysis potential (SNAP) model to bcc-fcc binary alloy systems. We demonstrate that machine-learned SNAP models can yield significant improvements even over well-established, high-performing embedded atom method (EAM) and modified EAM (MEAM) potentials for fcc Cu and Ni. We also report on the development of a SNAP model for the fcc Ni-bcc Mo binary system by machine learning a carefully-constructed large computed data set of elemental and intermetallic compounds. We demonstrate that this binary Ni-Mo SNAP model can achieve excellent agreement with experiments in the prediction of Ni-Mo phase diagram as well as near-DFT accuracy in the prediction of many key properties such as elastic constants, formation energies, melting points, etc., across the entire binary composition range. In contrast, the existing Ni-Mo EAM has significant errors in the prediction of the phase diagram and completely fails in binary compounds. This work provides a systematic model development process for multicomponent alloy systems, including an efficient procedure to optimize the hyper-parameters in the model fitting, and paves the way to long-time, large-scale simulations of such systems. arXiv:1806.04777v2 [cond-mat.mtrl-sci] 16 Aug 2018 Machine learning (ML) models based on robust local environment descriptors have recently emerged as an approach to describe the potential energy surface (PES) of systems of atoms with near-quantum accuracy at several of orders magnitude lower cost than ab initio methods. 1-6 Effective local environment descriptors must be invariant under translation, rotation, and permutation of homonuclear atoms, and have the properties of uniqueness and differentiability. 7 Examples of such descriptors include symmetry functions 1,8 , smooth overlap of atomic positions (SOAP) 4,9 , bispectrum 2,5 , Coulomb matrix 3,10,11 , among others.A typical approach is to fit the PES as a function of these descriptors by machine learning on ab initio data sets, using techniques ranging from simple linear regression 5,12 to kernel ridge regression 6,7 to neural networks [13][14][15][16] .Thus far, the development of ML potentials based on local environment descriptors have largely been limited to elements and oxides. The Gaussian approximation potential (GAP) using the SOAP descriptor has been applied on Si 4 , C 17,18 , W 9 , P 19 , and Fe 20 , and neural network models based on symmetry functions have been fitted for Si 21 , C 22 , Na 23 , ZnO 24 , TiO 2 25 , GeTe 26 , and Li 3 PO 4 27 . Thompson and Wood 5,28 have developed linear and quadratic models based on the SO(4) bispectrum -the Spectral Analysis Neighbor Potential or SNAP -for bcc Ta and W. Chen et al. 12 later showed that a line...

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Extending the accuracy of the SNAP interatomic potential form

Cited by 187 publications

References 63 publications

Recent advances and applications of machine learning in solid-state materials science

Recent advances and applications of machine learning in solid-state materials science

A Critical Review of Machine Learning of Energy Materials

Quantum-accurate spectral neighbor analysis potential models for Ni-Mo binary alloys and fcc metals

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