Publications by authors named "Max Gallant"

To close the gap between the rates of computational screening and experimental realization of novel materials, 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.

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Article Synopsis
  • Synthesis of new inorganic materials is challenging due to limited guidance on optimal solid-state procedures.
  • Researchers introduced primary and secondary competition metrics to assess how likely target materials will form compared to impurities in solid-state reactions.
  • They applied these metrics to analyze thousands of reactions, identifying efficient synthesis methods for barium titanate (BaTiO) that outperform traditional approaches by using unconventional precursors.
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