318 citations · 583 across the 6 of their papers we have counts for
6 papers
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…
Linearized machine-learning interatomic potentials for non-magnetic elemental metals: Limitation of pairwise descriptors and trend of predictive power
Akira Takahashi, Atsuto Seko, Isao Tanaka
Machine-learning interatomic potential (MLIP) has been of growing interest as a useful method to describe the energetics of systems of interest. In the present study, we examine th…
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…
Representation of compounds for machine-learning prediction of physical properties
Atsuto Seko, Hiroyuki Hayashi, Keita Nakayama +2
The representations of a compound, called "descriptors" or "features", play an essential role in constructing a machine-learning model of its physical properties. In this study, we…
First-principles interatomic potentials for ten elemental metals via compressed sensing
Atsuto Seko, Akira Takahashi, Isao Tanaka
Interatomic potentials have been widely used in atomistic simulations such as molecular dynamics. Recently, frameworks to construct accurate interatomic potentials that combine a s…
A sparse representation for potential energy surface
Atsuto Seko, Akira Takahashi, Isao Tanaka
We propose a simple scheme to estimate potential energy surface (PES) with which the accuracy can be easily controlled and improved up to the level of the density functional theory…