We have developed a machine learning model for critical cooling rates for metallic glasses based on computational properties,supporting in-silico screening for desired Rc values and significantly reducing reliance on ...We have developed a machine learning model for critical cooling rates for metallic glasses based on computational properties,supporting in-silico screening for desired Rc values and significantly reducing reliance on time-consuming laboratory work.We compare results for features derived from easy-tocompute functions of elemental properties to more complex physically motivated properties using ab initio,machine-learning potentials,and empirical potential molecular dynamics methods.The established approach enables property acquisition across a diverse range of alloys.Analysis of various features for 34 alloys from 20 chemical systems shows that the best model for critical cooling rates was learned from one elemental property-based feature and three simulated features.The elemental property based feature is an ideal entropy value based on alloy stoichiometry.The simulated features were acquired from estimates of energies above the convex hull,changes in heat capacity,and the fraction of icosahedra-like Voronoi polyhedra.Models were assessed through a demanding cross validation test based on repeatedly leaving out full chemical systems as test sets and had an R2 of 0.78 and a mean average error of 0.76 in units of lg(K/s).We demonstrate with Shapley additive explanation analysis that the most impactful features have physically reasonable influence on model predictions.The established methodology can be applied to other high-throughput studies of material properties of diverse compositions.展开更多
Irradiation increases the yield stress and embrittles light water reactor(LWR)pressure vessel steels.In this study,we demonstrate some of the potential benefits and risks of using machine learning models to predict ir...Irradiation increases the yield stress and embrittles light water reactor(LWR)pressure vessel steels.In this study,we demonstrate some of the potential benefits and risks of using machine learning models to predict irradiation hardening extrapolated to low flux,high fluence,extended life conditions.The machine learning training data included the Irradiation Variable for lower flux irradiations up to an intermediate fluence,plus the Belgian Reactor 2 and Advanced Test Reactor 1 for very high flux irradiations,up to very high fluence.Notably,the machine learning model predictions for the high fluence,intermediate flux Advanced Test Reactor 2 irradiations are superior to extrapolations of existing hardening models.The successful extrapolations showed that machine learning models are capable of capturing key intermediate flux effects at high fluence.Similar approaches,applied to expanded databases,could be used to predict hardening in LWRs under life-extension conditions.展开更多
基金Wisconsin Louis Stokes Alliance for Minority Participation National Science Foundation(NSF)award number HRD-1612530the University of Wisconsine Madison Graduate Engineering Research Scholars(GERS)fellowship program for the financial support for graduate student investigation,and the PPG Coating Innovation Center for financial support.Dr.Afflerbach gratefully acknowledges research support from the NSF through the University of Wisconsin Materials Research Science and Engineering Center(DMR-2309000)+1 种基金All authors gratefully acknowledge support from the NSF Collaborative Research:Framework:Machine Learning Materials Innovation Infrastructure award number 1931306Machine learning was performed with the computational resources provided by XSEDE 2.0:Integrating,Enabling and Enhancing National Cyberinfrastructure with Expanding Community Involvement Grant ACI-1548562.
文摘We have developed a machine learning model for critical cooling rates for metallic glasses based on computational properties,supporting in-silico screening for desired Rc values and significantly reducing reliance on time-consuming laboratory work.We compare results for features derived from easy-tocompute functions of elemental properties to more complex physically motivated properties using ab initio,machine-learning potentials,and empirical potential molecular dynamics methods.The established approach enables property acquisition across a diverse range of alloys.Analysis of various features for 34 alloys from 20 chemical systems shows that the best model for critical cooling rates was learned from one elemental property-based feature and three simulated features.The elemental property based feature is an ideal entropy value based on alloy stoichiometry.The simulated features were acquired from estimates of energies above the convex hull,changes in heat capacity,and the fraction of icosahedra-like Voronoi polyhedra.Models were assessed through a demanding cross validation test based on repeatedly leaving out full chemical systems as test sets and had an R2 of 0.78 and a mean average error of 0.76 in units of lg(K/s).We demonstrate with Shapley additive explanation analysis that the most impactful features have physically reasonable influence on model predictions.The established methodology can be applied to other high-throughput studies of material properties of diverse compositions.
基金D.M.,H.W.,R.J.,and T.M.gratefully acknowledge partial funding from NSF SI2-SSI award 1148011the Light Water Reactor Sustainability program,and Nuclear Energy University Program (NEUP) 21-24382+1 种基金Y.-c.L.gratefully acknowledge the financial support from Graduate Student Study Abroad Program (GSSAP) (107-2917-I-006-008),project (110-2222-E-006-008) from the Ministry of Science and Technology (MOST)the Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) and MOST (110-2634-F-006-017) in Taiwan,China.
文摘Irradiation increases the yield stress and embrittles light water reactor(LWR)pressure vessel steels.In this study,we demonstrate some of the potential benefits and risks of using machine learning models to predict irradiation hardening extrapolated to low flux,high fluence,extended life conditions.The machine learning training data included the Irradiation Variable for lower flux irradiations up to an intermediate fluence,plus the Belgian Reactor 2 and Advanced Test Reactor 1 for very high flux irradiations,up to very high fluence.Notably,the machine learning model predictions for the high fluence,intermediate flux Advanced Test Reactor 2 irradiations are superior to extrapolations of existing hardening models.The successful extrapolations showed that machine learning models are capable of capturing key intermediate flux effects at high fluence.Similar approaches,applied to expanded databases,could be used to predict hardening in LWRs under life-extension conditions.
