摘要
Predicting phase stability in high entropy alloys(HEAs),such as phase fractions as functions of composition and temperature,is essential for understanding alloy properties and screening desirable materials.Traditional methods like CALPHAD are computationally intensive for exploring high-dimensional compositional spaces.To address such a challenge,this study explored and compared the effectiveness of random forests(RF)and deep neural networks(DNN)for accelerating materials discovery by building surrogate models of phase stability prediction.For interpolation scenarios(testing on the same order of system as trained),RF models generally produce smaller errors than DNN models.However,for extrapolation scenarios(training on lower-order systems and testing on higher order systems),DNNs generalize more effectively than traditional ML models.DNN demonstrate the potential to predict topologically relevant phase composition when data were missing,making it a powerful predictive tool in materials discovery frameworks.
基金
Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344
supported by the Laboratory Directed Research and Development(LDRD)program under project tracking code 22-SI-007,and has been reviewed and released under LLNL-JRNL-848291.
