Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials

Thompson, Aidan P.; Swiler, Laura Painton; Trott, Christian Robert; Foiles, Stephen M.; Tucker, Garritt J.

doi:10.1016/j.jcp.2014.12.018

Cited by 896 publications

(685 citation statements)

References 26 publications

Supporting

Mentioning

676

Contrasting

Unclassified

Order By: Relevance

“…The Spectral Neighbor Analysis Potential (SNAP) introduced by Thompson et al 59 represents a linear version of GAPs and is based on the same bispectrum components, which are now assumed to be related linearly to the atomic energies. The atomic energy of atom i in SNAP is then given as a linear combination of the K projected bispectrum components by 59 E i,SNAP = β…”

Section: E Spectral Neighbor Analysis Potentialmentioning

confidence: 99%

See 1 more Smart Citation

Perspective: Machine learning potentials for atomistic simulations

Behler

2016

The Journal of Chemical Physics

1,182

955

View full text Add to dashboard Cite

Nowadays, computer simulations have become a standard tool in essentially all fields of chemistry, condensed matter physics, and materials science. In order to keep up with state-of-the-art experiments and the ever growing complexity of the investigated problems, there is a constantly increasing need for simulations of more realistic, i.e., larger, model systems with improved accuracy. In many cases, the availability of sufficiently efficient interatomic potentials providing reliable energies and forces has become a serious bottleneck for performing these simulations. To address this problem, currently a paradigm change is taking place in the development of interatomic potentials. Since the early days of computer simulations simplified potentials have been derived using physical approximations whenever the direct application of electronic structure methods has been too demanding. Recent advances in machine learning (ML) now offer an alternative approach for the representation of potential-energy surfaces by fitting large data sets from electronic structure calculations. In this perspective, the central ideas underlying these ML potentials, solved problems and remaining challenges are reviewed along with a discussion of their current applicability and limitations. Published by AIP Publishing. [http://dx

show abstract

Section: E Spectral Neighbor Analysis Potentialmentioning

confidence: 99%

“…The atomic energy of atom i in SNAP is then given as a linear combination of the K projected bispectrum components by 59 E i,SNAP = β…”

Section: E Spectral Neighbor Analysis Potentialmentioning

confidence: 99%

Perspective: Machine learning potentials for atomistic simulations

Behler

2016

The Journal of Chemical Physics

1,182

955

View full text Add to dashboard Cite

show abstract

“…The issue of reproducing long range IFCs is also overcome by interatomic potentials which model the potential energy surface as dependent upon a smooth and flexible atomic neighborhood for every atom, such as the Gaussian approximated potential 45 and the spectral neighbor analysis potential. 46 Lastly, the issue of dispersion is also overcome by a Taylor expansion of the potential energy around the equilibrium positions. Such methods, could in the future be used in combination with more standard EIPs, to produce even more accurate POPs, that not only replicate forces, energies, stresses and thermal conductivities, but also more accurately reproduce dispersions.…”

Section: Discussionmentioning

confidence: 99%

Empirical interatomic potentials optimized for phonon properties

et al. 2017

View full text Add to dashboard Cite

Molecular dynamics simulations have been extensively used to study phonons and gain insight, but direct comparisons to experimental data are often difficult, due to a lack of accurate empirical interatomic potentials for different systems. As a result, this issue has become a major barrier to realizing the promise associated with advanced atomistic-level modeling techniques. Here, we present a general method for specifically optimizing empirical interatomic potentials from ab initio inputs for the study of phonon transport properties, thereby resulting in phonon optimized potentials. The method uses a genetic algorithm to directly fit the empirical parameters of the potential to the key properties that determine whether or not the atomic level dynamics and most notably the phonon transport are described properly.

show abstract

“…The alternatives to the ACSF features are those based on a direct featurization of the local atomic neighbor density function . 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 . A particular challenge in IAP development for battery materials—many of which are ionic compounds—is the treatment of long‐range electrostatics.…”

Section: Applicationmentioning

confidence: 99%

A Critical Review of Machine Learning of Energy Materials

Chen

Zuo

et al. 2020

Advanced Energy Materials

420

281

View full text Add to dashboard Cite

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.

show abstract

Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials

Cited by 896 publications

References 26 publications

Perspective: Machine learning potentials for atomistic simulations

Perspective: Machine learning potentials for atomistic simulations

Empirical interatomic potentials optimized for phonon properties

A Critical Review of Machine Learning of Energy Materials

Contact Info

Product

Resources

About