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most citedTowards Universal Neural Network Potential for Material Discovery Applicable to Arbitrary Combination of 45 Elements

450 citations · 452 across the 4 of their papers we have counts for

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physics.chem-ph2026★ 1 cited

Matlantis-PFP v8: Universal Machine Learning Interatomic Potential with Better Experimental Agreements via r2SCAN Functional

Chikashi Shinagawa, So Takamoto, Daiki Shintani +7

Universal Machine Learning Interatomic Potentials (uMLIPs) enable atomistic simulations and high-throughput screening at scales far beyond those accessible with density functional…