15 citations · 17 across the 3 of their papers we have counts for
4 papers
AccelNet: Exact backward-compatible acceleration of polynomial angular descriptors through Cartesian moment factorization
Yuki Nagai
We present AccelNet, an exact, backward-compatible method for accelerating existing trained aenet and n2p2 neural-network potentials without retraining. For angular terms with sepa…
Target-Distribution-Guided Cross-Functional Fine-Tuning of Machine-Learning Interatomic Potentials
Yuki Nagai, Bo Thomsen, Motoyuki Shiga
Cross-functional fine-tuning of machine-learning interatomic potentials (MLIPs) is often treated as a relabeling problem, where configurations generated at one density-functional l…
Self-learning path integral hybrid Monte Carlo with mixed ab initio and machine learning potentials for modeling nuclear quantum effects in water
Bo Thomsen, Yuki Nagai, Keita Kobayashi +2
The introduction of machine learned potentials (MLPs) has greatly expanded the space available for studying Nuclear Quantum Effects computationally with ab initio path integral (PI…
Atomic diffusion due to hyperatomic fluctuation for quasicrystals
Yuki Nagai, Yutaka Iwasaki, Koichi Kitahara +3
A quasicrystal is an ordered but non-periodic structure understood as a projection from a higher dimensional periodic structure. Some physical properties of quasicrystals are diffe…