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AI discovers new material processing protocols using evolutionary search

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel closed-loop workflow that combines evolutionary search with uncertainty-aware deep kernel learning to discover new processing protocols for materials. This method was applied to ferroelectric thin films, successfully identifying waveform families that enhance nonlinear electromechanical response by activating specific domain-wall segments. The framework is presented as a generalizable approach for out-of-distribution discovery in various scientific and engineering domains, including synthesis, annealing, and battery formation. AI

IMPACT This research demonstrates a novel AI-driven approach for accelerating materials science discovery, potentially reducing experimental costs and time.

RANK_REASON The cluster contains a research paper detailing a new methodology for materials discovery. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI discovers new material processing protocols using evolutionary search

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The cluster contains a research paper detailing a new methodology for materials discovery. [lever_c_demoted from research: ic=1 ai=1.0]
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110 days old
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Yu Liu, Stanislav Udovenko, Ching-Che Lin, Jaegyu Kim, Lane W. Martin, Susan Trolier-McKinstry, Sergei V. Kalinin ·

    Closed-loop discovery of out-of-distribution processing protocols by evolutionary search and uncertainty-aware learning

    arXiv:2606.13859v1 Announce Type: cross Abstract: Many materials and chemical systems exhibit history-dependent responses, where functional outcomes are governed not only by final-state variables but by the time-dependent sequence of fields, temperatures, or chemical potentials a…

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