Interfaces between materials are critical to the performance of many devices,yet predicting their structure is computationally demanding due to the vast configuration space.We introduce RAFFLE,a software package for e...Interfaces between materials are critical to the performance of many devices,yet predicting their structure is computationally demanding due to the vast configuration space.We introduce RAFFLE,a software package for efficiently exploring low-energy interface configurations between arbitrary crystal pairs,enabling the generation of ensembles of interface structures.RAFFLE leverages physical insights and genetic algorithms to intelligently sample configurations,using dynamically evolving 2-,3-,and 4-body distribution functions as generalised structural descriptors.These descriptors are refined through active learning to guide atom placement strategies.RAFFLE performs well across diverse systems,including bulk materials,intercalation compounds,and interfaces.It correctly recovers known bulk phases of aluminum and MoS2,and predicts stable phases in intercalation and grain-boundary systems.For Si∣Ge interfaces,it finds intermixed and abrupt structures to be similarly stable.By accelerating interface structure prediction,RAFFLE offers a powerful tool for materials discovery,enabling efficient exploration of complex configuration spaces.展开更多
基金supported in part by the Government Office for Science and the Royal Academy of Engineering under the UK Intelligence Community Postdoctoral Research Fellowships scheme(Grant No.ICRF2425-8-148)J.Pitfield was supported by VILLUM FONDEN through Investigator Grant,ProjectNo.16562+3 种基金by the Danish National Research Foundation through the Centre of Excellence“InterCat”(Grant Agreement No:DNRF150)Additionally,we thank the EPSRC for funding J.Pitfield(EP/L015331/1)F.H.Davies(EP/X013375/1)via the EPSRC Centre for Doctoral Training in Metamaterialsthe Leverhulme for funding S.P.Hepplestone and N.T.Taylor(RPG-2021-086).Via our membership of the UK’s HEC Materials Chemistry Consortium,which is funded by EPSRC(EP/R029431),this work used the ARCHER2 UK National Supercomputing Service(https://www.archer2.ac.uk)95 within the framework of a Grand Challenge project.The authors acknowledge the use of the University of Exeter High-Performance Computing(HPC)facility ISCA for this work.
文摘Interfaces between materials are critical to the performance of many devices,yet predicting their structure is computationally demanding due to the vast configuration space.We introduce RAFFLE,a software package for efficiently exploring low-energy interface configurations between arbitrary crystal pairs,enabling the generation of ensembles of interface structures.RAFFLE leverages physical insights and genetic algorithms to intelligently sample configurations,using dynamically evolving 2-,3-,and 4-body distribution functions as generalised structural descriptors.These descriptors are refined through active learning to guide atom placement strategies.RAFFLE performs well across diverse systems,including bulk materials,intercalation compounds,and interfaces.It correctly recovers known bulk phases of aluminum and MoS2,and predicts stable phases in intercalation and grain-boundary systems.For Si∣Ge interfaces,it finds intermixed and abrupt structures to be similarly stable.By accelerating interface structure prediction,RAFFLE offers a powerful tool for materials discovery,enabling efficient exploration of complex configuration spaces.
