54 citations · 54 across the 2 of their papers we have counts for
2 papers
cond-mat.mtrl-sci2026
ZEBRA-Prop: A Zero-Shot Embedding-Based Rapid and Accessible Regression Model for Materials Properties
Ryoma Yamamoto, Akira Takahashi, Kei Terayama +2
Large language models (LLMs) exhibit substantial potential across diverse scientific disciplines, including materials science. A property prediction framework, ZEBRA-Prop (Zero-Sho…
cond-mat.mtrl-sci2017★ 54 cited
Conceptual and practical bases for the high accuracy of machine learning interatomic potential
Akira Takahashi, Atsuto Seko, Isao Tanaka
Machine learning interatomic potentials (MLIPs) based on a large dataset obtained by density functional theory (DFT) calculation have been developed recently. This study gives both…