474 citations · 2.3k across the 36 of their papers we have counts for
37 papers
Scalable construction of force-constant basis sets for large-scale anharmonic lattice-dynamical calculations
Atsuto Seko, Atsushi Togo
A projector-based formulation of force constants in crystalline materials provides a systematic framework for constructing force-constant bases and determining force constants from…
AI4X Roadmap: Artificial Intelligence for the advancement of scientific pursuit and its future directions
Stephen G. Dale, Nikita Kazeev, Alastair J. A. Price +65
Artificial intelligence and machine learning are reshaping how we approach scientific discovery, not by replacing established methods but by extending what researchers can probe, p…
Systematic global structure search of bismuth-based binary systems under pressure using machine learning potentials
Hayato Wakai, Shintaro Ishiwata, Atsuto Seko
Machine learning potentials (MLPs) have significantly advanced global crystal structure prediction by enabling efficient and accurate property evaluations. In this study, global st…
Global structure searches under varying temperatures and pressures using polynomial machine learning potentials: A case study on silicon
Hayato Wakai, Atsuto Seko, Isao Tanaka
Polynomial machine learning potentials (MLPs) based on polynomial rotational invariants have been systematically developed for various systems and applied to efficiently predict cr…
Polynomial machine learning potential and its application to global structure search in the ternary Cu-Ag-Au alloy
Atsuto Seko
Machine learning potentials (MLPs) have become indispensable for performing accurate large-scale atomistic simulations and predicting crystal structures. This study introduces the…
Projector-based efficient estimation of force constants
Atsuto Seko, Atsushi Togo
Estimating force constants for crystal structures is crucial for calculating various phonon-related properties. However, this task becomes particularly challenging when dealing wit…
Globally-stable and metastable crystal structure enumeration using polynomial machine learning potentials in elemental As, Bi, Ga, In, La, P, Sb, Sn, and Te
Atsuto Seko
Machine learning potentials (MLPs) have become indispensable for conducting accurate large-scale atomistic simulations and for the efficient prediction of crystal structures. Polyn…
On-the-fly training of polynomial machine learning potentials in computing lattice thermal conductivity
Atsushi Togo, Atsuto Seko
The application of first-principles calculations for predicting lattice thermal conductivity (LTC) in crystalline materials, in conjunction with the linearized phonon Boltzmann equ…
Predictive power of polynomial machine learning potentials for liquid states in 22 elemental systems
Hayato Wakai, Atsuto Seko, Hirosato Izuta +2
The polynomial machine learning potentials (MLPs) described by polynomial rotational invariants have been systematically developed for various systems and used in diverse applicati…
Systematic development of polynomial machine learning potentials for metallic and alloy systems
Atsuto Seko
Machine learning potentials (MLPs) developed from extensive datasets constructed from density functional theory (DFT) calculations have become increasingly appealing for many resea…
Finding well-optimized special quasirandom structures with decision diagram
Kohei Shinohara, Atsuto Seko, Takashi Horiyama +1
The advanced data structure of the zero-suppressed binary decision diagram (ZDD) enables us to efficiently enumerate nonequivalent substitutional structures. Not only can the ZDD s…
Structure and lattice thermal conductivity of grain boundaries in silicon by using machine learning potential and molecular dynamics
Susumu Fujii, Atsuto Seko
In silicon, lattice thermal conductivity plays an important role in a wide range of applications such as thermoelectric and microelectronic devices. Grain boundaries (GBs) in polyc…
Machine learning potentials for multicomponent systems: The Ti-Al binary system
Atsuto Seko
Machine learning potentials (MLPs) are becoming powerful tools for performing accurate atomistic simulations and crystal structure optimizations. An approach to developing MLPs emp…
Application of machine learning potentials to predict grain boundary properties in fcc elemental metals
Takayuki Nishiyama, Atsuto Seko, Isao Tanaka
Accurate interatomic potentials are in high demand for large-scale atomistic simulations of materials that are prohibitively expensive by density functional theory (DFT) calculatio…
Machine Learning Potential Repository
Atsuto Seko
This paper introduces a machine learning potential repository that includes Pareto optimal machine learning potentials. It also shows the systematic development of accurate and fas…
Derivative structure enumeration using binary decision diagram
Kohei Shinohara, Atsuto Seko, Takashi Horiyama +3
A derivative structure is a nonequivalent substitutional atomic configuration derived from a given primitive cell. The enumeration of derivative structures plays an essential role…
Prediction of perovskite-related structures in ACuO (A Ca, Sr, Ba, Sc, Y, La) using density functional theory and Bayesian optimization
Atsuto Seko, Shintaro Ishiwata
Oxygen vacancy ordering in perovskite-type transition-metal oxides plays an important role in the emergence of exotic electronic properties, as typified by superconducting cuprates…
Group-theoretical high-order rotational invariants for structural representations: Application to linearized machine learning interatomic potential
Atsuto Seko, Atsushi Togo, Isao Tanaka
Many rotational invariants for crystal structure representations have been used to describe the structure-property relationship by machine learning. The machine learning interatomi…
Compositional descriptor-based recommender system accelerating the materials discovery
Atsuto Seko, Hiroyuki Hayashi, Isao Tanaka
Structures and properties of many inorganic compounds have been collected historically. However, it only covers a very small portion of possible inorganic crystals, which implies t…
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…
Exploring a potential energy surface by machine learning for characterizing atomic transport
Kenta Kanamori, Kazuaki Toyoura, Junya Honda +7
We propose a machine-learning method for evaluating the potential barrier governing atomic transport based on the preferential selection of dominant points for the atomic transport…
Matrix- and tensor-based recommender systems for the discovery of currently unknown inorganic compounds
Atsuto Seko, Hiroyuki Hayashi, Hisashi Kashima +1
Chemically relevant compositions (CRCs) and atomic arrangements of inorganic compounds have been collected as inorganic crystal structure databases. Machine-learning is a unique ap…
Descriptors for Machine Learning of Materials Data
Atsuto Seko, Atsushi Togo, Isao Tanaka
Descriptors, which are representations of compounds, play an essential role in machine learning of materials data. Although many representations of elements and structures of compo…
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…