The 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 PACE that is suitable for use ...The 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 PACE 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 PACE 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.展开更多
A public data-analytics competition was organized by the Novel Materials Discovery(NOMAD)Centre of Excellence and hosted by the online platform Kaggle by using a dataset of 3,000(Al_(x)GayIn_(1-x-y))_(2)O_(3) compound...A public data-analytics competition was organized by the Novel Materials Discovery(NOMAD)Centre of Excellence and hosted by the online platform Kaggle by using a dataset of 3,000(Al_(x)GayIn_(1-x-y))_(2)O_(3) compounds.Its aim was to identify the best machinelearning(ML)model for the prediction of two key physical properties that are relevant for optoelectronic applications:the electronic bandgap energy and the crystalline formation energy.Here,we present a summary of the top-three ranked ML approaches.The first-place solution was based on a crystal-graph representation that is novel for the ML of properties of materials.The second-place model combined many candidate descriptors from a set of compositional,atomic-environment-based,and average structural properties with the light gradient-boosting machine regression model.The third-place model employed the smooth overlap of atomic position representation with a neural network.The Pearson correlation among the prediction errors of nine ML models(obtained by combining the top-three ranked representations with all three employed regression models)was examined by using the Pearson correlation to gain insight into whether the representation or the regression model determines the overall model performance.Ensembling relatively decorrelated models(based on the Pearson correlation)leads to an even higher prediction accuracy.展开更多
基金The authors acknowledge helpful discussions with Marc Cawkwell.R.D.acknowledges funding through the German Science Foundation(DFG),project number 405621217Sandia National Laboratories is a multimission laboratory managed and operated by National Technology&Engineering Solutions of Sandia,LLC,a wholly owned subsidiary of Honeywell International Inc.,for the U.S.Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
文摘The 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 PACE 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 PACE 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.
基金The project received funding from the European Union’s Horizon 2020 research and innovation program(grant agreement no.676580)the Molecular Simulations from First Principles(MS1P).C.S.gratefully acknowledges funding by the Alexander von Humboldt Foundation.
文摘A public data-analytics competition was organized by the Novel Materials Discovery(NOMAD)Centre of Excellence and hosted by the online platform Kaggle by using a dataset of 3,000(Al_(x)GayIn_(1-x-y))_(2)O_(3) compounds.Its aim was to identify the best machinelearning(ML)model for the prediction of two key physical properties that are relevant for optoelectronic applications:the electronic bandgap energy and the crystalline formation energy.Here,we present a summary of the top-three ranked ML approaches.The first-place solution was based on a crystal-graph representation that is novel for the ML of properties of materials.The second-place model combined many candidate descriptors from a set of compositional,atomic-environment-based,and average structural properties with the light gradient-boosting machine regression model.The third-place model employed the smooth overlap of atomic position representation with a neural network.The Pearson correlation among the prediction errors of nine ML models(obtained by combining the top-three ranked representations with all three employed regression models)was examined by using the Pearson correlation to gain insight into whether the representation or the regression model determines the overall model performance.Ensembling relatively decorrelated models(based on the Pearson correlation)leads to an even higher prediction accuracy.
