Uncertainty quantification of predicting stable structures for high-entropy alloys using Bayesian neural networks

High entropy alloys(HEAs)have excellent application prospects in catalysis because of their rich components and configuration space.In this work,we develop a Bayesian neural network(BNN)based on energies calculated with density functional theory to search the configuration space of the CoNiRhRu HEA system.The BNN model was developed by considering six independent features of Co-Ni,Co-Rh,CoRu,Ni-Rh,Ni-Ru,and Rh-Ru in different shells and energies of structures as the labels.The root mean squared error of the energy predicted by BNN is 1.37 me V/atom.Moreover,the influence of feature periodicity on the energy of HEA in theoretical calculations is discussed.We found that when the neural network is optimized to a certain extent,only using the accuracy indicator of root mean square error to evaluate model performance is no longer accurate in some scenarios.More importantly,we reveal the importance of uncertainty quantification for neural networks to predict new structures of HEAs with proper confidence based on BNN.

Structural screening of phosphorus sulfur ternary hydride PSH_(6) with a high-temperature superconductivity at 130 GPa

In our study,we constructed a series of inorganic nonmetallic ternary hydrides PSH6 by first-principles structural screening under pressure of 200 GPa.The structural stability under lower pressure are examined.Focusing on the structural stability,electronic and phonon properties,as well as the possible superconducting properties within the framework of Bardeen−Cooper−Schrieffer(BCS)theory,we show that PSH6 with space group possesses a superconducting transition temperature of 146 K at 130 GPa.In the pressure range of 100−200 GPa,our work suggests that the ternary phosphorus-sulfur-hydrogen would act as a promising compositional and elemental space for achieving high-temperature superconductivity.