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# AI-driven materials discovery

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  1. Building the materials foundation for AI: Syensqo on advanced materials and AI as a two-way driver

    This MIT Technology Review Insights conversation produced in partnership with Syensqo records the views of Mike Finelli, Syensqo's chief technology and innovation officer and chief North America officer: AI is pushing semiconductors and data centers toward physical limits, which piles up more simultaneous requirements on advanced materials, and Syensqo is responding by developing materials for high-voltage data center architectures, advanced sealing materials for semiconductor manufacturing, and thermal-management solutions including direct immersion cooling fluids, while working with Microsoft on AI agents that digitally synthesize millions of candidate molecules, predict their performance through physics-based simulation, and rank them down to roughly a hundred candidates for laboratory

  2. RSC Advances

    Artificial Intelligence-Driven Magnetic Property Prediction and Materials Discovery for Next-Generation Spintronics

    This review surveys how machine learning and artificial intelligence, combined with high-throughput first-principles calculations and experimental databases, are used to predict key spintronic magnetic properties (such as Curie temperature, magnetocrystalline anisotropy energy, magnetic moment, spin Hall conductivity, Dzyaloshinskii–Moriya interaction, coercivity, and tunneling magnetoresistance), discusses descriptor engineering, graph neural networks, and physics-informed learning, reviews AI-assisted screening and inverse design across Heusler alloys, topological spin systems, and two-dimensional magnets, and outlines a future roadmap toward physics-guided, uncertainty-aware, and autonomous closed-loop optimization.

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