Surrogate modeling techniques have become indispensable in accelerating the discovery and optimization of high-entropy alloys(HEAs),especially when integrating computational predictions with sparse experimental observ...Surrogate modeling techniques have become indispensable in accelerating the discovery and optimization of high-entropy alloys(HEAs),especially when integrating computational predictions with sparse experimental observations.This study systematically evaluates the training and testing performance of four prominent surrogate models—conventional Gaussian processes(cGP),Deep Gaussian processes(DGP),encoder-decoder neural networks for multi-output regression and eXtreme Gradient Boosting(XGBoost)—applied to a hybrid dataset of experimental and computational properties of the 8-component HEA system Al-Co-Cr-Cu-Fe-Mn-Ni-V.We specifically assess their capabilities in predicting correlated material properties,including yield strength,hardness,modulus,ultimate tensile strength,elongation,and average hardness under dynamic/quasi-static conditions,alongside auxiliary computational properties.The comparison highlights the strengths of hierarchical deep modeling approaches in handling heteroscedastic,heterotopic,and incomplete data commonly encountered in materials science.Our findings illustrate that combined surrogate models such as DGPs infused with machine-learned priors outperformother surrogates by effectively capturing inter-property correlations and by assimilating prior knowledge.This enhanced predictive accuracy positions the combined surrogate models as powerful tools for robust and dataefficient materials design.展开更多
基金supported by the Texas A&M University System National Laboratories Office of the Texas A&M University System and Los Alamos National Laboratory as part of the Joint Research Collaboration Program. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Los Alamos National Laboratory or The Texas A&M University Systemsupport from the U.S. Department of Energy (DOE) ARPA-E CHADWICK Program through Project DE‐AR0001988JJ acknowledges support from the Los Alamos National Laboratory Laboratory (LANL) Laboratory Directed Research and Development Program under project number 20220815PRD4. LANL is operated by Triad National Security, LLC, for the National Nuclear Security Administration of the U.S. Department of Energy (Contract No. 89233218CNA000001). Original data were generated within the BIRDSHOT Center (https://birdshot.tamu.edu), supported by the Army Research Laboratory under Cooperative Agreement (CA) NumberW911NF-22-2-0106 (MM, DK, DA, VA and RA acknowledge partial support from this CA). NF acknowledges support from AFRL through a subcontract with ARCTOS, TOPS VI (165852-19F5830-19-02-C1). Calculations were carried out at Texas A&M High-Performance Research Computing (HPRC).
文摘Surrogate modeling techniques have become indispensable in accelerating the discovery and optimization of high-entropy alloys(HEAs),especially when integrating computational predictions with sparse experimental observations.This study systematically evaluates the training and testing performance of four prominent surrogate models—conventional Gaussian processes(cGP),Deep Gaussian processes(DGP),encoder-decoder neural networks for multi-output regression and eXtreme Gradient Boosting(XGBoost)—applied to a hybrid dataset of experimental and computational properties of the 8-component HEA system Al-Co-Cr-Cu-Fe-Mn-Ni-V.We specifically assess their capabilities in predicting correlated material properties,including yield strength,hardness,modulus,ultimate tensile strength,elongation,and average hardness under dynamic/quasi-static conditions,alongside auxiliary computational properties.The comparison highlights the strengths of hierarchical deep modeling approaches in handling heteroscedastic,heterotopic,and incomplete data commonly encountered in materials science.Our findings illustrate that combined surrogate models such as DGPs infused with machine-learned priors outperformother surrogates by effectively capturing inter-property correlations and by assimilating prior knowledge.This enhanced predictive accuracy positions the combined surrogate models as powerful tools for robust and dataefficient materials design.
