450 citations · 452 across the 4 of their papers we have counts for
4 papers
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…
Efficient Crystal Structure Prediction Using Universal Neural Network Potential with Diversity Preservation in Genetic Algorithms
Takuya Shibayama, Hideaki Imamura, Katsuhiko Nishimra +4
Crystal structure prediction (CSP) is crucial for identifying stable crystal structures in given systems and is a prerequisite for computational atomistic simulations. Recent advan…
Calculations of Real-System Nanoparticles Using Universal Neural Network Potential PFP
Gerardo Valadez Huerta, Yusuke Nanba, Iori Kurata +4
It is essential to explore the stability and activity of real-system nanoparticles theoretically. While applications of theoretical methods for this purpose can be found in literat…
Towards Universal Neural Network Potential for Material Discovery Applicable to Arbitrary Combination of 45 Elements
So Takamoto, Chikashi Shinagawa, Daisuke Motoki +19
Computational material discovery is under intense study owing to its ability to explore the vast space of chemical systems. Neural network potentials (NNPs) have been shown to be p…