Atomic cluster expansion for accurate and transferable interatomic potentials

2019 ◽  
Vol 99 (1) ◽  
Author(s):  
Ralf Drautz
Keyword(s):  
Cluster Expansion ◽  
Interatomic Potentials ◽  
Atomic Cluster

scholarly journals Performant implementation of the atomic cluster expansion (PACE) and application to copper and silicon

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Yury Lysogorskiy ◽  
Cas van der Oord ◽  
Anton Bochkarev ◽  
Sarath Menon ◽  
Matteo Rinaldi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cluster Expansion ◽  
Large Scale ◽  
Pareto Front ◽  
General Purpose ◽  
Basis Functions ◽  
Atomic Energy ◽  
Atomistic Simulations ◽  
Interatomic Potentials ◽  
Atomic Cluster

AbstractThe atomic cluster expansion is a general polynomial expansion of the atomic energy in multi-atom basis functions. Here we implement the atomic cluster expansion in the performant C++ code that is suitable for use in large-scale atomistic simulations. We briefly review the atomic cluster expansion and give detailed expressions for energies and forces as well as efficient algorithms for their evaluation. We demonstrate that the atomic cluster expansion as implemented in shifts a previously established Pareto front for machine learning interatomic potentials toward faster and more accurate calculations. Moreover, general purpose parameterizations are presented for copper and silicon and evaluated in detail. We show that the Cu and Si potentials significantly improve on the best available potentials for highly accurate large-scale atomistic simulations.

scholarly journals Linear Atomic Cluster Expansion Force Fields for Organic Molecules: beyond RMSE

2021 ◽  
Author(s):  
David Peter Kovacs ◽  
Cas van der Oord ◽  
Jiri Kucera ◽  
Alice Allen ◽  
Daniel Cole ◽  
...  
Keyword(s):  
Cluster Expansion ◽  
Organic Molecules ◽  
Force Fields ◽  
Atomic Cluster ◽  
Expansion Force

scholarly journals Linear Atomic Cluster Expansion Force Fields for Organic Molecules: beyond RMSE

2021 ◽  
Author(s):  
David Peter Kovacs ◽  
Cas van der Oord ◽  
Jiri Kucera ◽  
Alice Allen ◽  
Daniel Cole ◽  
...  
Keyword(s):  
Cluster Expansion ◽  
Organic Molecules ◽  
Force Fields ◽  
Atomic Cluster ◽  
Expansion Force

scholarly journals Linear Atomic Cluster Expansion Force Fields for Organic Molecules: beyond RMSE

2021 ◽  
Author(s):  
David Peter Kovacs ◽  
Cas van der Oord ◽  
Jiri Kucera ◽  
Alice Allen ◽  
Daniel Cole ◽  
...  
Keyword(s):  
Cluster Expansion ◽  
Organic Molecules ◽  
Force Fields ◽  
Atomic Cluster ◽  
Expansion Force

Search of stable structures in cation deficient (V,Nb)CoSb half-Heusler alloys by an atomic cluster expansion

2021 ◽  
Author(s):  
Joaquin Miranda ◽  
Thomas Gruhn
Keyword(s):  
Structural Stability ◽  
Cluster Expansion ◽  
Heusler Alloys ◽  
Atomic Cluster ◽  
Wide Range ◽  
Stable Structures ◽  
Expansion Scheme

Using a cluster expansion scheme, we evaluate the structural stability of (V, Nb)CoSb half-Heusler alloys over a wide range of V and Nb concentrations when the alloy is simultaneously subject...

scholarly journals Monitoring Atomic Cluster Expansion by High-Harmonic Generation

2011 ◽  
Vol 107 (11) ◽  
Author(s):  
Vasily Strelkov ◽  
Ulf Saalmann ◽  
Andreas Becker ◽  
Jan M. Rost
Keyword(s):  
Harmonic Generation ◽  
Cluster Expansion ◽  
High Harmonic ◽  
High Harmonic Generation ◽  
Atomic Cluster

scholarly journals Machine-learned interatomic potentials for alloys and alloy phase diagrams

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Conrad W. Rosenbrock ◽  
Konstantin Gubaev ◽  
Alexander V. Shapeev ◽  
Livia B. Pártay ◽  
Noam Bernstein ◽  
...  
Keyword(s):  
Phase Diagrams ◽  
Cluster Expansion ◽  
Gaussian Approximation ◽  
Moment Tensor ◽  
Computational Cost ◽  
Lattice Models ◽  
Interatomic Potentials ◽  
Spherical Fourier Transform ◽  
Wide Range ◽  
Computational Materials

AbstractWe introduce machine-learned potentials for Ag-Pd to describe the energy of alloy configurations over a wide range of compositions. We compare two different approaches. Moment tensor potentials (MTPs) are polynomial-like functions of interatomic distances and angles. The Gaussian approximation potential (GAP) framework uses kernel regression, and we use the smooth overlap of atomic position (SOAP) representation of atomic neighborhoods that consist of a complete set of rotational and permutational invariants provided by the power spectrum of the spherical Fourier transform of the neighbor density. Both types of potentials give excellent accuracy for a wide range of compositions, competitive with the accuracy of cluster expansion, a benchmark for this system. While both models are able to describe small deformations away from the lattice positions, SOAP-GAP excels at transferability as shown by sensible transformation paths between configurations, and MTP allows, due to its lower computational cost, the calculation of compositional phase diagrams. Given the fact that both methods perform nearly as well as cluster expansion but yield off-lattice models, we expect them to open new avenues in computational materials modeling for alloys.

scholarly journals Linear Atomic Cluster Expansion Force Fields for Organic Molecules: beyond RMSE

2021 ◽  
Author(s):  
David Peter Kovacs ◽  
Cas van der Oord ◽  
Jiri Kucera ◽  
Alice Allen ◽  
Daniel Cole ◽  
...  
Keyword(s):  
Potential Energy ◽  
Potential Energy Surface ◽  
Cluster Expansion ◽  
Organic Molecules ◽  
Energy Surface ◽  
Force Fields ◽  
Atomic Cluster ◽  
Symmetric Polynomials ◽  
Benchmark Datasets ◽  
Expansion Force

We demonstrate that fast and accurate linear force fields can be built for molecules using the Atomic Cluster Expansion (ACE) framework. The ACE models parametrize the Potential Energy Surface in terms of body ordered symmetric polynomials making the functional form reminiscent of traditional molecular mechanics force fields. We show that the 4 or 5-body ACE force fields improve on the accuracy of the empirical force fields by up to a factor of 10, reaching the accuracy typical of recently proposed machine learning based approaches. We not only show state of the art accuracy and speed on the widely used MD17 and ISO17 benchmark datasets, but also go beyond RMSE by comparing a number of ML and empirical force fields to ACE on more important tasks such as normal mode prediction, high temperature molecular dynamics, dihedral torsional profile prediction and even bond breaking. We also demonstrate the smoothness, transferability and extrapolation capabilities of ACE on a new challenging benchmark dataset comprising a potential energy surface of a flexible drug-like molecule.

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