Designing metallic glasses in silico is a major challenge in materials science given their disordered atomic structure and the vast compositional space to explore.Here,wetackle this challenge by finding optimal compos...Designing metallic glasses in silico is a major challenge in materials science given their disordered atomic structure and the vast compositional space to explore.Here,wetackle this challenge by finding optimal compositions for target mechanical properties.We apply Bayesian exploration for the CuZrAl composition,a paradigmatic metallic glass known for its good glass forming ability.We exploit an automated loop with an online database,a Bayesian optimization algorithm,and molecular dynamics simulations.From the ubiquitous 50/50 CuZr starting point,we map the composition landscape,changing the ratio of elements and adding aluminum,to characterize the yield stress and the shear modulus.This approach demonstrates with relatively modest effort that the system has an optimal composition window for the yield stress around aluminum concentration cAl=15%and zirconium concentration cZr=30%.We also explore several cooling rates(“process parameters”)and find that the best mechanical properties for a composition result from being most affected by the cooling procedure.Our Bayesian approach paves the novel way for the design of metallic glasses with“small data”,with an eye toward both future in silico design and experimental applications exploiting this toolbox.展开更多
基金supported by the European Union Horizon 2020 research and innovation program under grant agreement no.857470 and from the European Regional Development Fund via the Foundation for Polish Science International Research Agenda PLUS program grant No.MAB PLUS/2018/8support from the Academy of Finland(361245 and 317464)+4 种基金from the Finnish Cultural Foundation.S.B.acknowledges support from the National Science Center in Poland through the SONATA BIS grant DEC-2023/50/E/ST3/00569from the Foundation for Polish Science in Poland through the FIRST TEAM FENG.02.02-IP.05-0177/23 projectsupport from the FinnCERES flagship(151830423)Business Finland(211835,211909,and 211989)Future Makers programs.The authors acknowledge the computational resources provided by the Aalto University School of Science“Science-IT”project.
文摘Designing metallic glasses in silico is a major challenge in materials science given their disordered atomic structure and the vast compositional space to explore.Here,wetackle this challenge by finding optimal compositions for target mechanical properties.We apply Bayesian exploration for the CuZrAl composition,a paradigmatic metallic glass known for its good glass forming ability.We exploit an automated loop with an online database,a Bayesian optimization algorithm,and molecular dynamics simulations.From the ubiquitous 50/50 CuZr starting point,we map the composition landscape,changing the ratio of elements and adding aluminum,to characterize the yield stress and the shear modulus.This approach demonstrates with relatively modest effort that the system has an optimal composition window for the yield stress around aluminum concentration cAl=15%and zirconium concentration cZr=30%.We also explore several cooling rates(“process parameters”)and find that the best mechanical properties for a composition result from being most affected by the cooling procedure.Our Bayesian approach paves the novel way for the design of metallic glasses with“small data”,with an eye toward both future in silico design and experimental applications exploiting this toolbox.
