5 papers
Conserved quantities and ensemble measure for Martyna--Tobias--Klein barostats with restricted cell degrees of freedom
Kohei Shinohara
We derive the conserved energy-like quantity and ensemble measure for Martyna--Tobias--Klein (MTK) barostats in which only a restricted subset of the cell degrees of freedom are ac…
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…
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…
Systematic Magnetic Structure Generation Based on Oriented Spin Space Groups: Formulation, Applications, and High-Throughput First-Principles Calculations
Takuya Nomoto, Kohei Shinohara, Hikaru Watanabe +1
We propose a framework for generating magnetic structures, inspired by the concept of oriented spin space groups (SSGs): magnetic structures are first generated as totally symmetri…
Symmetry-aware Conditional Generation of Crystal Structures Using Diffusion Models
Takanori Ishii, Kaoru Hisama, Kohei Shinohara
The application of generative models in crystal structure prediction (CSP) has gained significant attention. Conditional generation--particularly the generation of crystal structur…