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De novo exploration and self-guided learning of potentialenergy surfaces

npj Computational Materials 2019年第1期5卷 262-270页

作者： noam bernstein Gábor Csányi Volker L.Deringer Center for Materials Physics and Technology U.S.Naval Research LaboratoryWashingtonDC 20375USA Department of Engineering University of CambridgeCambridge CB21PZUK Department of Chemistry University of OxfordOxford OX13QRUK

Interatomic potential models based on machine learning(ML)are rapidly developing as tools for material ***,because of their flexibility,they require large fitting databases that are normally created with substantial manual selection and tuning of reference ***,we show that ML potentials can be built in a largely automated fashion,exploring and fitting potential-energy surfaces from the beginning(de novo)within one and the same *** key enabling step is the use of a configuration-averaged kernel metric that allows one to select the few most relevant and diverse structures at each *** resulting potentials are accurate and robust for the wide range of configurations that occur during structure searching,despite only requiring a relatively small number of single-point DFT calculations on small unit *** apply the method to materials with diverse chemical nature and coordination environments,marking an important step toward the more routine application of ML potentials in physics,chemistry,and materials science.

关键词： apply routine searching

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Machine-learned interatomic potentials for alloys and alloy phase diagrams

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npj Computational Materials 2021年第1期7卷 139-147页

作者： Conrad W.Rosenbrock Konstantin Gubaev Alexander VShapeev Livia B.Pártay noam bernstein Gábor Csányi Gus L.W.Hart Department of Physics and Astronomy Brigham Young UniversityProvoUTUSA84602 Skolkovo Institute of Science and Technology Skolkovo Innovation CenterNobel Str.3Moscow 143026Russia Department of Chemistry University of ReadingWhiteknightsReading RG66ADUK Department of Chemistry University of WarwickCoventry CV47ALUK US Naval Research Laboratory Center for Computational Materials ScienceWashingtonDC 20375USA Department of Engineering University of CambridgeTrumpington StreetCambridge CB21PZUK

We introduce machine-learned potentials for Ag-Pd to describe the energy of alloy configurations over a wide range of *** compare two different *** tensor potentials(MTPs)are polynomial-like functions of interatomic distances and *** 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 *** types of potentials give excellent accuracy for a wide range of compositions,competitive with the accuracy of cluster expansion,a benchmark for this *** 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 *** 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.

关键词： alloy alloys phase

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