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An autonomous laboratory for the accelerated synthesis of novel materials

Chemistry

An autonomous laboratory for the accelerated synthesis of novel materials

N. J. Szymanski, B. Rendy, et al.

Discover the groundbreaking work of Nathan J. Szymanski and his team as they unveil an autonomous laboratory (A-Lab) for synthesizing novel inorganic materials. In just 17 days, they successfully created 41 new compounds, driven by the synergy of machine learning and advanced computational techniques, highlighting the immense potential of AI in materials discovery.

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Playback language: English
Abstract
To accelerate the experimental realization of novel materials predicted by computation, an autonomous laboratory (A-Lab) for solid-state synthesis of inorganic powders is introduced. The A-Lab integrates computations, literature data, machine learning (ML), and active learning to plan and interpret experiments using robotics. Over 17 days, it synthesized 41 novel compounds from 58 targets, using ab initio phase-stability data and natural language models trained on literature synthesis data. Analysis of failures provides actionable suggestions for improving materials screening and synthesis design. The high success rate showcases the power of AI-driven platforms for autonomous materials discovery.
Publisher
Nature
Published On
Nov 29, 2023
Authors
Nathan J. Szymanski, Bernardus Rendy, Yuxing Fei, Rishi E. Kumar, Tanjin He, David Milsted, Matthew J. McDermott, Max Gallant, Ekin Dogus Cubuk, Amil Merchant, Haegyeom Kim, Anubhav Jain, Christopher J. Bartel, Kristin Persson, Yan Zeng, Gerbrand Ceder
Tags
autonomous laboratory
inorganic powders
machine learning
materials discovery
solid-state synthesis
AI-driven platforms
novel compounds

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