An autonomous laboratory for the accelerated synthesis of inorganic materials

2023/11/29 by Nathan J. Szymanski, Bernardus Rendy, Yuxing Fei +13 · 46 citations
Materials Science · #Electronic and Structural Properties of Oxides #Machine Learning in Materials Science #X-ray Diffraction in Crystallography

paper · pdf · doi:10.1038/s41586-023-06734-w

openalex publication_date 2023/11/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01

Abstract

, we introduce the A-Lab, an autonomous laboratory for the solid-state synthesis of inorganic powders. This platform uses computations, historical data from the literature, machine learning (ML) and active learning to plan and interpret the outcomes of experiments performed using robotics. Over 17 days of continuous operation, the A-Lab realized 41 novel compounds from a set of 58 targets including a variety of oxides and phosphates that were identified using large-scale ab initio phase-stability data from the Materials Project and Google DeepMind. Synthesis recipes were proposed by natural-language models trained on the literature and optimized using an active-learning approach grounded in thermodynamics. Analysis of the failed syntheses provides direct and actionable suggestions to improve current techniques for materials screening and synthesis design. The high success rate demonstrates the effectiveness of artificial-intelligence-driven platforms for autonomous materials discovery and motivates further integration of computations, historical knowledge and robotics.

Citations

• <i>GSAS-II</i>: the genesis of a modern open-source all purpose crystallography software package
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