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
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.
