Efficient training of ANN potentials by including atomic forces via Taylor expansion and application to water and a transition-metal oxide

Artificial neural network(ANN)potentials enable the efficient large-scale atomistic modeling of complex materials with near firstprinciples accuracy.For molecular dynamics simulations,accurate energies and interatomic forces are a prerequisite,but training ANN potentials simultaneously on energies and forces from electronic structure calculations is computationally demanding.Here,we introduce an efficient alternative method for the training of ANN potentials on energy and force information,based on an extrapolation of the total energy via a Taylor expansion.By translating the force information to approximate energies,the quadratic scaling with the number of atoms exhibited by conventional force-training methods can be avoided,which enables the training on reference datasets containing complex atomic structures.

Performance of two complementary machine-learned potentials in modelling chemically complex systems

Chemically complex multicomponent alloys possess exceptional properties derived from an inexhaustible compositional space.The complexity however makes interatomic potential development challenging.We explore two complementary machine-learned potentials—the moment tensor potential(MTP)and the Gaussian moment neural network(GM-NN)—in simultaneously describing configurational and vibrational degrees of freedom in the Ta-V-Cr-W alloy family.Both models are equally accurate with excellent performance evaluated against density-functional-theory.They achieve root-mean-square-errors(RMSEs)in energies of less than a few meV/atom across 0 K ordered and high-temperature disordered configurations included in the training.Even for compositions not in training,relative energy RMSEs at high temperatures are within a few meV/atom.High-temperature molecular dynamics forces have similarly small RMSEs of about 0.15 eV/Åfor the disordered quaternary included in,and ternaries not part of training.MTPs achieve faster convergence with training size;GM-NNs are faster in execution.Active learning is partially beneficial and should be complemented with conventional human-based training set generation.