2026\u5e744\u670826\u65e5\u301c5\u67081\u65e5\u306b\u30a2\u30e1\u30ea\u30ab\u30fb\u30cf\u30ef\u30a4\u5dde\u30db\u30ce\u30eb\u30eb\u3067\u958b\u50ac\u3055\u308c\u308b\u300c2026 MRS Spring Meeting & Exhibit\u300d\u306b\u3066\u3001Matlantis Inc. \u306e\u30e1\u30f3\u30d0\u30fc\u304c2\u4ef6\u306e\u53e3\u982d\u767a\u8868\u3092\u884c\u3044\u307e\u3059\u3002
MRS Spring Meeting\u306f\u3001Materials Research Society\uff08MRS\uff09\u304c\u4e3b\u50ac\u3059\u308b\u6750\u6599\u79d1\u5b66\u5206\u91ce\u306b\u304a\u3051\u308b\u4e16\u754c\u6700\u5927\u7d1a\u306e\u56fd\u969b\u4f1a\u8b70\u3067\u3059\u3002\u672c\u5927\u4f1a\u306f4\u670826\u65e5\u304b\u30895\u67081\u65e5\u307e\u3067\u306e6\u65e5\u9593\u3001\u30db\u30ce\u30eb\u30eb\u5e02\u51854\u30ab\u6240\u306e\u516c\u5f0f\u4f1a\u5834\u306b\u307e\u305f\u304c\u3063\u3066\u958b\u50ac\u3055\u308c\u3001\u5b66\u8853\u754c\u30fb\u7523\u696d\u754c\u30fb\u653f\u5e9c\u6a5f\u95a2\u30fb\u56fd\u7acb\u7814\u7a76\u6240\u304b\u3089\u6750\u6599\u7814\u7a76\u8005\u304c\u96c6\u7d50\u3057\u3001\u5206\u91ce\u6a2a\u65ad\u7684\u306a\u8b70\u8ad6\u3068\u7814\u7a76\u4ea4\u6d41\u3092\u884c\u3044\u307e\u3059\u3002\u4eca\u56de\u306e\u4f1a\u8b70\u3067\u306f\u3001\u6301\u7d9a\u53ef\u80fd\u306a\u88fd\u9020\u3001\u5148\u7aef\u8a08\u6e2c\u3001\u30a8\u30cd\u30eb\u30ae\u30fc\u6750\u6599\u306a\u3069\u306e\u5206\u91ce\u3067\u6700\u5148\u7aef\u306e\u6210\u679c\u304c\u767a\u8868\u3055\u308c\u308b\u4e88\u5b9a\u3067\u3059\u3002
\u5f53\u793e\u304b\u3089\u306f\u3001\u6c4e\u7528\u6a5f\u68b0\u5b66\u7fd2\u30dd\u30c6\u30f3\u30b7\u30e3\u30eb\uff08uMLIP\uff09\u300cPFP\u300d\u3092\u6d3b\u7528\u3057\u305f\u89e6\u5a92\u30fb\u96fb\u6c60\u6750\u6599\u958b\u767a\u306e\u6700\u65b0\u7814\u7a76\u6210\u679c\u306b\u3064\u3044\u3066\u3001\u4ee5\u4e0b\u306e2\u4ef6\u306e\u767a\u8868\u3092\u884c\u3044\u307e\u3059\u3002<\/p>\n\n\n\n
Accelerating Discovery of High Entropy Alloy Catalysts for Nitrate Reduction to Ammonia with a Universal Machine Learning Interatomic Potential<\/strong><\/p>\n\n\n\n Ammonia is critical as a fertilizer feedstock and an emerging hydrogen carrier, but the energy-intensive, carbon-heavy Haber-Bosch process has driven interest in alternatives such as electrocatalytic nitrate reduction (NO\u2083RR), which also helps mitigate nitrate pollution in water. High-entropy alloys (HEAs) are attractive catalyst candidates due to their diverse active-site environments, yet their vast compositional space is intractable for conventional DFT-based screening. In this work, we use a universal machine learning interatomic potential to rapidly map Gibbs free energies along the NO\u2083RR pathway across thousands of HEA surfaces (e.g., FeCoNiCuZn), and then leverage graph-neural-network latent embeddings as descriptors to train ML models that predict nitrate and ammonia binding energies and identify compositions with optimal reactant\u2013product binding balance\u2014an approach readily extensible to other challenging electrocatalytic reactions such as CO\u2082 reduction.<\/p>\n\n\n\n Date & Time (local):<\/strong> Sunday, April 26, 2026, 8:30 AM Accelerating Discovery of High Entropy Alloy Catalysts for Nitrate Reduction to Ammonia with a Universal Machine Learning Interatomic Potential<\/strong><\/p>\n\n\n\n Discovering solid electrolytes for all-solid-state batteries is bottlenecked by the fact that high ionic conductivity arises from an intricate coupling of structural and vibrational properties, which single-modality machine learning models struggle to capture. To address this, we introduce a multimodal deep learning framework that integrates several physically motivated representations\u2014long-range crystal structure, local atomic environments, and lattice dynamics\u2014within a single architecture, learning cross-modal correlations to build a richer, more physically grounded material description. The approach outperforms single-modality baselines while offering interpretable insights into the factors governing ionic mobility, providing a practical route to accelerate solid electrolyte screening and uncover the structure\u2013dynamics\u2013property relationships that guide rational design of next-generation energy storage materials.<\/p>\n\n\n\n Date & Time (local):<\/strong> Tuesday, April 28, 2026, 8:45 AM <\/p>\n\n\n\n \u30a4\u30d9\u30f3\u30c8\u8a73\u7d30\u30fb\u304a\u7533\u8fbc\u307f
Session:<\/strong> EN02.01 \u2014 ML- and AI-Enabled Design of Sustainable Materials
Symposium:<\/strong> EN02 \u2014 Emergent Materials Paradigms\u2014Catalytic, Photonic and Energy Conversion Technologies for a Sustainable Future
Venue:<\/strong> Sheraton Waikiki, Lanai Room, 2nd Floor
Presenter:<\/strong> Joshua Young (Matlantis, Inc.)<\/p>\n\n\n\n\u767a\u88682<\/h2>\n\n\n\n
Session:<\/strong> MT01.03 \u2014 Physics-Informed, Expert Guided AI-Driven Experiments
Symposium:<\/strong> MT01 \u2014 Interfacing AI with Prior Knowledge and Human Expertise for Data-Efficient Autonomous Materials Research
Venue:<\/strong> Hilton Hawaiian Village, Tapa Conference Center, Iolani Suite VI, 2nd Floor
Presenter:<\/strong> Qingjie Li (Matlantis, Inc.)<\/p>\n\n\n\n
<\/strong>\u30a4\u30d9\u30f3\u30c8\u306e\u8a73\u7d30\u304a\u3088\u3073\u304a\u7533\u8fbc\u307f\u306f\u3001\u4e0b\u8a18\u30ea\u30f3\u30af\u3088\u308a\u3054\u78ba\u8a8d\u304f\u3060\u3055\u3044\u3002
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