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AI expands Alexandria database with 1.3M new stable compounds

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new multi-stage workflow for computational materials discovery, achieving a 99% success rate in identifying stable compounds. This process utilized the Matra-Genoa generative model, Orb-v2 potential, and ALIGNN graph neural network to generate over 119 million candidate structures. The workflow successfully added 1.3 million DFT-validated compounds to the ALEXANDRIA database, including 74,000 new stable materials, expanding it to 5.8 million structures. AI

IMPACT Introduces a novel AI-driven workflow that significantly improves the efficiency and success rate of discovering new stable materials.

RANK_REASON This is a research paper detailing a new workflow and dataset for materials discovery. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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

AI expands Alexandria database with 1.3M new stable compounds

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This is a research paper detailing a new workflow and dataset for materials discovery. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Th\'eo Cavignac (Research Center Future Energy Materials and Systems of the University Alliance Ruhr and ICAMS, Ruhr University Bochum, Bochum, Germany), Jonathan Schmidt (Department of Materials, ETH Z\"urich, Z\"urich, Switzerland), Pierre-Paul De Breuc ·

    AI-Driven Expansion and Application of the Alexandria Database

    arXiv:2512.09169v2 Announce Type: replace-cross Abstract: We present a novel multi-stage workflow for computational materials discovery that achieves a 99% success rate in identifying compounds within 100 meV/atom of thermodynamic stability, with a threefold improvement over prev…

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