The Materials Genome Initiative(MGI)advanced a new paradigm for materials discovery and design,namely that the pace of new materials deployment could be accelerated through complementary efforts in theory,computation,...The Materials Genome Initiative(MGI)advanced a new paradigm for materials discovery and design,namely that the pace of new materials deployment could be accelerated through complementary efforts in theory,computation,and experiment.Along with numerous successes,new challenges are inviting researchers to refocus the efforts and approaches that were originally inspired by the MGI.In May 2017,the National Science Foundation sponsored the workshop“Advancing and Accelerating Materials Innovation Through the Synergistic Interaction among Computation,Experiment,and Theory:Opening New Frontiers”to review accomplishments that emerged from investments in science and infrastructure under the MGI,identify scientific opportunities in this new environment,examine how to effectively utilize new materials innovation infrastructure,and discuss challenges in achieving accelerated materials research through the seamless integration of experiment,computation,and theory.This article summarizes key findings from the workshop and provides perspectives that aim to guide the direction of future materials research and its translation into societal impacts.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
Obtaining accurate estimates of machine learning model uncertainties on newly predicted data is essential for understanding the accuracy of the model and whether its predictions can be trusted.A common approach to suc...Obtaining accurate estimates of machine learning model uncertainties on newly predicted data is essential for understanding the accuracy of the model and whether its predictions can be trusted.A common approach to such uncertainty quantification is to estimate the variance from an ensemble of models,which are often generated by the generally applicable bootstrap method.In this work,we demonstrate that the direct bootstrap ensemble standard deviation is not an accurate estimate of uncertainty but that it can be simply calibrated to dramatically improve its accuracy.We demonstrate the effectiveness of this calibration method for both synthetic data and numerous physical datasets from the field of Materials Science and Engineering.The approach is motivated by applications in physical and biological science but is quite general and should be applicable for uncertainty quantification in a wide range of machine learning regression models.展开更多
Knowledge of the domain of applicability of a machine learning model is essential to ensuring accurate and reliable model predictions.In this work,we develop a new and general approach of assessing model domain and de...Knowledge of the domain of applicability of a machine learning model is essential to ensuring accurate and reliable model predictions.In this work,we develop a new and general approach of assessing model domain and demonstrate that our approach provides accurate and meaningful domain designation across multiple model types and material property data sets.Our approach assesses the distance between data in feature space using kernel density estimation,where this distance provides an effective tool for domain determination.We show that chemical groups considered unrelated based on chemical knowledge exhibit significant dissimilarities by our measure.We also show that high measures of dissimilarity are associated with poor model performance(i.e.,high residual magnitudes)and poor estimates of model uncertainty(i.e.,unreliable uncertainty estimation).Automated tools are provided to enable researchers to establish acceptable dissimilarity thresholds to identify whether new predictions of their own machine learning models are in-domain versus out-of-domain.展开更多
文摘The Materials Genome Initiative(MGI)advanced a new paradigm for materials discovery and design,namely that the pace of new materials deployment could be accelerated through complementary efforts in theory,computation,and experiment.Along with numerous successes,new challenges are inviting researchers to refocus the efforts and approaches that were originally inspired by the MGI.In May 2017,the National Science Foundation sponsored the workshop“Advancing and Accelerating Materials Innovation Through the Synergistic Interaction among Computation,Experiment,and Theory:Opening New Frontiers”to review accomplishments that emerged from investments in science and infrastructure under the MGI,identify scientific opportunities in this new environment,examine how to effectively utilize new materials innovation infrastructure,and discuss challenges in achieving accelerated materials research through the seamless integration of experiment,computation,and theory.This article summarizes key findings from the workshop and provides perspectives that aim to guide the direction of future materials research and its translation into societal impacts.
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.
基金The National Science Foundation provided financial support for G.P.(Award#1545481)S.D.,A.P.,J.P.E.,and X.Y.(Award#1636950 and 1636910)+1 种基金R.J.and D.M.(Award#1931298)Financial support for A.G.and G.G.was provided by the University of Wisconsin Harvey D.Spangler Professorship.
文摘Obtaining accurate estimates of machine learning model uncertainties on newly predicted data is essential for understanding the accuracy of the model and whether its predictions can be trusted.A common approach to such uncertainty quantification is to estimate the variance from an ensemble of models,which are often generated by the generally applicable bootstrap method.In this work,we demonstrate that the direct bootstrap ensemble standard deviation is not an accurate estimate of uncertainty but that it can be simply calibrated to dramatically improve its accuracy.We demonstrate the effectiveness of this calibration method for both synthetic data and numerous physical datasets from the field of Materials Science and Engineering.The approach is motivated by applications in physical and biological science but is quite general and should be applicable for uncertainty quantification in a wide range of machine learning regression models.
基金the Bridge to the Doctorate:Wisconsin Louis Stokes Alliance for Minority Participation National Science Foundation(NSF)award number HRD-1612530the University of Wisconsin-Madison Graduate Engineering Research Scholars(GERS)fellowship program,and the PPG Coating Innovation Center for financial support for the initial part of this work.The other authors gratefully acknowledge support from the NSF Collaborative Research:Framework:Machine Learning Materials Innovation Infrastructure award number 1931306+1 种基金Lane E.Schultz also acknowledges this award for support for the latter part of this work.Machine learning was performed with the computational resources provided by XSEDE 2.0:Integrating,Enabling and Enhancing National Cyberinfrastructure with Expanding Community Involvement Grant ACI-1548562We thank former and current members of the Informatics Skunkworks at the University of Wisconsin-Madison for their contributions to early aspects of this work:Angelo Cortez,Evelin Yin,Jodie Felice Ritchie,Stanley Tzeng,Avi Sharma,Linxiu Zeng,and Vidit Agrawal.
文摘Knowledge of the domain of applicability of a machine learning model is essential to ensuring accurate and reliable model predictions.In this work,we develop a new and general approach of assessing model domain and demonstrate that our approach provides accurate and meaningful domain designation across multiple model types and material property data sets.Our approach assesses the distance between data in feature space using kernel density estimation,where this distance provides an effective tool for domain determination.We show that chemical groups considered unrelated based on chemical knowledge exhibit significant dissimilarities by our measure.We also show that high measures of dissimilarity are associated with poor model performance(i.e.,high residual magnitudes)and poor estimates of model uncertainty(i.e.,unreliable uncertainty estimation).Automated tools are provided to enable researchers to establish acceptable dissimilarity thresholds to identify whether new predictions of their own machine learning models are in-domain versus out-of-domain.
