Rapid discovery and synthesis of future materials requires intelligent data acquisition strategies to navigate large design spaces.A popular strategy is Bayesian optimization,which aims to find candidates that maximiz...Rapid discovery and synthesis of future materials requires intelligent data acquisition strategies to navigate large design spaces.A popular strategy is Bayesian optimization,which aims to find candidates that maximize material properties;however,materials design often requires finding specific subsets of the design space which meet more complex or specialized goals.We present a framework that captures experimental goals through straightforward user-defined filtering algorithms.These algorithms are automatically translated into one of three intelligent,parameter-free,sequential data collection strategies(SwitchBAX,InfoBAX,and MeanBAX),bypassing the time-consuming and difficult process of task-specific acquisition function design.Our framework is tailored for typical discrete search spaces involving multiple measured physical properties and short time-horizon decision making.We demonstrate this approach on datasets for TiO2 nanoparticle synthesis and magnetic materials characterization,and show that our methods are significantly more efficient than state-of-the-art approaches.Overall,our framework provides a practical solution for navigating the complexities of materials design,and helps lay groundwork for the accelerated development of advanced materials.展开更多
基金supported in part by the U.S.Department of Energy,Office of Science,Office of Basic Energy Sciences under Contract No.DE-AC02-76SF00515A.R.and F.H.J.acknowledge funding from the National Science Foundation(NSF)program Designing Materials to Revolutionize and Engineer our Future(DMREF)via a project DMR-1922312C.J.T.was supported by the U.S.Department of Energy,Office of Science,Basic Energy Sciences,Chemical Sciences,Geosciences,and Biosciences Division under SLAC Contract No.DE-AC02-76SF00515.
文摘Rapid discovery and synthesis of future materials requires intelligent data acquisition strategies to navigate large design spaces.A popular strategy is Bayesian optimization,which aims to find candidates that maximize material properties;however,materials design often requires finding specific subsets of the design space which meet more complex or specialized goals.We present a framework that captures experimental goals through straightforward user-defined filtering algorithms.These algorithms are automatically translated into one of three intelligent,parameter-free,sequential data collection strategies(SwitchBAX,InfoBAX,and MeanBAX),bypassing the time-consuming and difficult process of task-specific acquisition function design.Our framework is tailored for typical discrete search spaces involving multiple measured physical properties and short time-horizon decision making.We demonstrate this approach on datasets for TiO2 nanoparticle synthesis and magnetic materials characterization,and show that our methods are significantly more efficient than state-of-the-art approaches.Overall,our framework provides a practical solution for navigating the complexities of materials design,and helps lay groundwork for the accelerated development of advanced materials.
