The fusion of experimental automation and machine learning has catalyzed a new era in materials research,prominently featuring Gaussian Process(GP)Bayesian Optimization(BO)driven autonomous experiments.Here we introdu...The fusion of experimental automation and machine learning has catalyzed a new era in materials research,prominently featuring Gaussian Process(GP)Bayesian Optimization(BO)driven autonomous experiments.Here we introduce a Dual-GP approach that enhances traditional GPBO by adding a secondary surrogate model to dynamically constrain the experimental space based on realtime assessments of the raw experimental data.This Dual-GP approach enhances the optimization efficiency of traditional GPBO by isolating more promising space for BO sampling and more valuable experimental data for primary GP training.We also incorporate a flexible,human-in-the-loop intervention method in the Dual-GP workflow to adjust for unanticipated results.We demonstrate the effectiveness of the Dual-GP model with synthetic model data and implement this approach in autonomous pulsed laser deposition experimental data.This Dual-GP approach has broad applicability in diverse GPBO-driven experimental settings,providing a more adaptable and precise framework for refining autonomous experimentation for more efficient optimization.展开更多
Autonomous experimentation–or self-driving labs–offers a systematic approach to accelerate materials discovery by integrating automated synthesis,characterization,and data-driven decisionmaking.We present a closed-l...Autonomous experimentation–or self-driving labs–offers a systematic approach to accelerate materials discovery by integrating automated synthesis,characterization,and data-driven decisionmaking.We present a closed-loop workflow for the on-demand synthesis and structural characterization of colloidal gold nanoparticles,enabling direct mapping from composition to nanoscale structure.Our framework leverages differentiable models of spectral shape to address two central tasks in self-driving labs:(a)phase mapping,or identifying compositional regions with distinct structural behavior;and(b)material retrosynthesis,or optimizing compositions for target structure.Using functional data analysis,we develop a data-driven model with generative pre-training,active learning,and high-throughput experiments to predict spectral responses across composition space.We demonstrate the approach on seed-mediated growth of gold nanoparticles,showcasing its ability to extract design rules,reveal secondary interactions,and efficiently navigate morphology space.Gradient-based optimization of the models enables inverse design,making this a unified platform.展开更多
Materials genome engineering represents the new frontier of materials research,and is disrupting the conventional“trial and error”paradigm for materials innovation.In the present perspective,the author reflects on t...Materials genome engineering represents the new frontier of materials research,and is disrupting the conventional“trial and error”paradigm for materials innovation.In the present perspective,the author reflects on the major achievements already made in five sub-domains,including high-efficiency materials computation and design,revolutionary experimental technologies,materials big data technologies,research and development of advanced materials,and industrial applications.Furthermore,the author lays out five crucial directions of future efforts for maturing the relevant technologies.These directions include cross-scale modeling and computational design,artificial intelligence for materials science,automatic and intelligent experimentation,digital twin,and data resource management and sharing.展开更多
基金supported by the Center for Nanophase Materials Sciences(CNMS),which is a US Department of Energy,Office of Science User Facility at Oak Ridge National Laboratory.
文摘The fusion of experimental automation and machine learning has catalyzed a new era in materials research,prominently featuring Gaussian Process(GP)Bayesian Optimization(BO)driven autonomous experiments.Here we introduce a Dual-GP approach that enhances traditional GPBO by adding a secondary surrogate model to dynamically constrain the experimental space based on realtime assessments of the raw experimental data.This Dual-GP approach enhances the optimization efficiency of traditional GPBO by isolating more promising space for BO sampling and more valuable experimental data for primary GP training.We also incorporate a flexible,human-in-the-loop intervention method in the Dual-GP workflow to adjust for unanticipated results.We demonstrate the effectiveness of the Dual-GP model with synthetic model data and implement this approach in autonomous pulsed laser deposition experimental data.This Dual-GP approach has broad applicability in diverse GPBO-driven experimental settings,providing a more adaptable and precise framework for refining autonomous experimentation for more efficient optimization.
基金funded primarily by the US Department of Energy (DOE), Office of Science, and Office of Basic Energy Sciences (BES) under award number DE-SC0019911Funding for H.T.C. was provided through the Energy Frontier Research Centers program: CSSAS—The Center for the Science of Synthesis Across Scales—under Award Number DE-SC0019288+2 种基金A.G. was supported by the University of Washington Molecular Engineering Materials Center (MEM-C, NSF grant DMR-2308979) as a part of the Academic Year Research Acceleration Research Experience for Undergraduates program. This work was also facilitated by the advanced computational, storage, and networking infrastructure provided by the Hyak supercomputer system and the Department of Chemical Engineering at the University of Washington. Part of this work was conducted at the Molecular Analysis Facility, a National Nanotechnology Coordinated Infrastructure (NNCI) site at the University of Washingtonsupported in part by funds from the National Science Foundation (awards NNCI-2025489, NNCI-1542101)the Molecular Engineering & Sciences Institute, and the Clean Energy Institute.
文摘Autonomous experimentation–or self-driving labs–offers a systematic approach to accelerate materials discovery by integrating automated synthesis,characterization,and data-driven decisionmaking.We present a closed-loop workflow for the on-demand synthesis and structural characterization of colloidal gold nanoparticles,enabling direct mapping from composition to nanoscale structure.Our framework leverages differentiable models of spectral shape to address two central tasks in self-driving labs:(a)phase mapping,or identifying compositional regions with distinct structural behavior;and(b)material retrosynthesis,or optimizing compositions for target structure.Using functional data analysis,we develop a data-driven model with generative pre-training,active learning,and high-throughput experiments to predict spectral responses across composition space.We demonstrate the approach on seed-mediated growth of gold nanoparticles,showcasing its ability to extract design rules,reveal secondary interactions,and efficiently navigate morphology space.Gradient-based optimization of the models enables inverse design,making this a unified platform.
基金supported by the major consulting project of the Chinese Academy of Engineering(No.2021-JJZD-01).
文摘Materials genome engineering represents the new frontier of materials research,and is disrupting the conventional“trial and error”paradigm for materials innovation.In the present perspective,the author reflects on the major achievements already made in five sub-domains,including high-efficiency materials computation and design,revolutionary experimental technologies,materials big data technologies,research and development of advanced materials,and industrial applications.Furthermore,the author lays out five crucial directions of future efforts for maturing the relevant technologies.These directions include cross-scale modeling and computational design,artificial intelligence for materials science,automatic and intelligent experimentation,digital twin,and data resource management and sharing.
