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Accelerated prediction of atomically precise cluster structures using on-the-fly machine learning

npj Computational Materials 2022年第1期8卷 1644-1653页

作者： Yunzhe Wang Shanping Liu Peter Lile Sam Norwood Alberto Hernandez Sukriti Manna tim mueller Department of Materials Science and Engineering Johns Hopkins UniversityBaltimoreMD21218USA

The chemical and structural properties of atomically precise nanoclusters are of great interest in numerous applications,but predicting the stable structures of clusters can be computationally *** this work,we present a procedure for rapidly predicting low-energy structures of nanoclusters by combining a genetic algorithm with interatomic potentials actively learned *** this approach to aluminum clusters with 21 to 55 atoms,we have identified structures with lower energy than any reported in the literature for 25 out of the 35 *** benchmarks indicate that the active learning procedure accelerated the average search speed by about an order of magnitude relative to genetic algorithm searches using only density functional *** work demonstrates a feasible way to systematically discover stable structures for large nanoclusters and provides insights into the transferability of machine-learned interatomic potentials for nanoclusters.

关键词： precise atomic cluster

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Fast,accurate,and transferable many-body interatomic potentials by symbolic regression

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npj Computational Materials 2019年第1期5卷 161-171页

作者： Alberto Hernandez Adarsh Balasubramanian Fenglin Yuan Simon A.M.Mason tim mueller Department of Materials Science and Engineering Johns Hopkins UniversityBaltimoreMDUSA

The length and time scales of atomistic simulations are limited by the computational cost of the methods used to predict material *** recent years there has been great progress in the use of machine-learning algorithms to develop fast and accurate interatomic potential models,but it remains a challenge to develop models that generalize well and are fast enough to be used at extreme time and length *** address this challenge,we have developed a machine-learning algorithm based on symbolic regression in the form of genetic programming that is capable of discovering accurate,computationally efficient many-body potential *** key to our approach is to explore a hypothesis space of models based on fundamental physical principles and select models within this hypothesis space based on their accuracy,speed,and *** focus on simplicity reduces the risk of overfitting the training data and increases the chances of discovering a model that generalizes *** algorithm was validated by rediscovering an exact Lennard-Jones potential and a Sutton-Chen embedded-atom method potential from training data generated using these *** using training data generated from density functional theory calculations,we found potential models for elemental copper that are simple,as fast as embedded-atom models,and capable of accurately predicting properties outside of their training *** approach requires relatively small sets of training data,making it possible to generate training data using highly accurate methods at a reasonable computational *** present our approach,the forms of the discovered models,and assessments of their transferability,accuracy and speed.

关键词： symbolic simplicity transfer

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Machine-learned multi-system surrogate models for materials prediction

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npj Computational Materials 2019年第1期5卷 702-707页

作者： Chandramouli Nyshadham Matthias Rupp Brayden Bekker Alexander V.Shapeev tim mueller Conrad W.Rosenbrock Gábor Csányi David W.Wingate Gus L.W.Hart Department of Physics and Astronomy Brigham Young UniversityProvoUT 84602USA Fritz Haber Institute of the Max Planck Society Faradayweg 4–614195 BerlinGermany Skolkovo Institute of Science and Technology Skolkovo Innovation CenterBuilding 3Moscow 143026Russia Department of Materials Science and Engineering Johns Hopkins UniversityBaltimoreMD 21218USA Engineering Laboratory University of CambridgeTrumpington StreetCambridge CB21PZUK Computer Science Department Brigham Young UniversityProvoUT 84602USA Citrine Informatics 702 Marshall StreetRedwood CityCA 94063USA

Surrogate machine-learning models are transforming computational materials science by predicting properties of materials with the accuracy of ab initio methods at a fraction of the computational *** demonstrate surrogate models that simultaneously interpolate energies of different materials on a dataset of 10 binary alloys(AgCu,AlFe,AlMg,AlNi,AlTi,CoNi,CuFe,CuNi,FeV,and NbNi)with 10 different species and all possible fcc,bcc,and hcp structures up to eight atoms in the unit cell,15,950 structures in *** find that the deviation of prediction errors when increasing the number of simultaneously modeled alloys is<1 meV/*** state-of-the-art materials representations and learning algorithms were found to qualitatively agree on the prediction errors of formation enthalpy with relative errors of<2.5% for all systems.

关键词： alloys prediction CoNi

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