We propose a strategy for generating unbiased and systematically extendable training data for machine learning interatomic potentials(MLIP)for multicomponent alloys,called Automated Small SYmmetric Structure Training ...We propose a strategy for generating unbiased and systematically extendable training data for machine learning interatomic potentials(MLIP)for multicomponent alloys,called Automated Small SYmmetric Structure Training or ASSYST.Based on exploring the full space of random crystal structures with space groups,it facilitates the construction of training sets for MLIPs in an automatic way without prior knowledge of the material in question.The advantages of this approach are that only cells consisting of few atoms(≈10)are needed for the DFT training set,and the size and completeness of the data set can be systematically controlled with very few parameters.We validate that potentials fitted this way can accurately describe a wide range of binary and ternary phases,random alloys,as well as point and extended defects,that have not been part of the training set.Finally,we estimate the binary phase diagrams with good experimental agreement.We demonstrate that the overall excellent performance is not a coincidence,but a consequence of the extensive sampling in phase space of ASSYST.Overall,this means that ASSYST will enable the largely autonomous generation of highquality DFT reference data and MLIPs.展开更多
Even minute amounts of one solute atom per one million bulk atoms may give rise to qualitative changes in the mechanical response and fracture resistance of modern structural materials.These changes are commonly relat...Even minute amounts of one solute atom per one million bulk atoms may give rise to qualitative changes in the mechanical response and fracture resistance of modern structural materials.These changes are commonly related to enrichment by several orders of magnitude of the solutes at structural defects in the host lattice.The underlying concept—segregation—is thus fundamental in materials science.To include it in modern strategies of materials design,accurate and realistic computational modelling tools are necessary.However,the enormous number of defect configurations as well as sites solutes can occupy requires models which rely on severe approximations.In the present study we combine a high-throughput study containing more than 1 million data points with machine learning to derive a computationally highly efficient framework which opens the opportunity to model this important mechanism on a routine basis.展开更多
基金funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) through the Collaborative Research Center 1394 (SFB 1394, No. 409476157) and Project No. 405621160MP would like to thank Prince Matthews for setting up the hcp grain boundaries, Sarath Menon for providing support for CALPHY34Bengt Hallstedt for providing plots of the Mg/Ca and Al/Ca phase diagrams from his assessments, Chad Sinclair together with SFB 1394 for funding a research stay at UBC Vancouver where part of this work was conducted, as well Mira Todorova and Ali Tehranchi for fruitful discussions and Ralf Drautz for critical reading of the manuscript.
文摘We propose a strategy for generating unbiased and systematically extendable training data for machine learning interatomic potentials(MLIP)for multicomponent alloys,called Automated Small SYmmetric Structure Training or ASSYST.Based on exploring the full space of random crystal structures with space groups,it facilitates the construction of training sets for MLIPs in an automatic way without prior knowledge of the material in question.The advantages of this approach are that only cells consisting of few atoms(≈10)are needed for the DFT training set,and the size and completeness of the data set can be systematically controlled with very few parameters.We validate that potentials fitted this way can accurately describe a wide range of binary and ternary phases,random alloys,as well as point and extended defects,that have not been part of the training set.Finally,we estimate the binary phase diagrams with good experimental agreement.We demonstrate that the overall excellent performance is not a coincidence,but a consequence of the extensive sampling in phase space of ASSYST.Overall,this means that ASSYST will enable the largely autonomous generation of highquality DFT reference data and MLIPs.
基金This project has received funding from the European Research Council(ERC)under the European Union’s Horizon 2020 research and innovation programme(Grant Agreement No.639211).
文摘Even minute amounts of one solute atom per one million bulk atoms may give rise to qualitative changes in the mechanical response and fracture resistance of modern structural materials.These changes are commonly related to enrichment by several orders of magnitude of the solutes at structural defects in the host lattice.The underlying concept—segregation—is thus fundamental in materials science.To include it in modern strategies of materials design,accurate and realistic computational modelling tools are necessary.However,the enormous number of defect configurations as well as sites solutes can occupy requires models which rely on severe approximations.In the present study we combine a high-throughput study containing more than 1 million data points with machine learning to derive a computationally highly efficient framework which opens the opportunity to model this important mechanism on a routine basis.
