Copertina

Globally-stable and metastable crystal structure enumeration using polynomial machine learning potentials in elemental As, Bi, Ga, In, La, P, Sb, Sn, and Te

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autore principale:	Seko, Atsuto
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Materials Science Computational Physics
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Global structure searches under varying temperatures and pressures using polynomial machine learning potentials: A case study on silicon
di: Wakai, Hayato, et al.
Pubblicazione: (2025)

Systematic global structure search of bismuth-based binary systems under pressure using machine learning potentials
di: Wakai, Hayato, et al.
Pubblicazione: (2025)

Polynomial machine learning potential and its application to global structure search in the ternary Cu-Ag-Au alloy
di: Seko, Atsuto
Pubblicazione: (2024)

On-the-fly training of polynomial machine learning potentials in computing lattice thermal conductivity
di: Togo, Atsushi, et al.
Pubblicazione: (2024)

Derivative structure enumeration using binary decision diagram
di: Shinohara, Kohei, et al.
Pubblicazione: (2020)

Predictive power of polynomial machine learning potentials for liquid states in 22 elemental systems
di: Wakai, Hayato, et al.
Pubblicazione: (2024)

Projector-based efficient estimation of force constants
di: Seko, Atsuto, et al.
Pubblicazione: (2024)

Study of vacancy ordering and the boson peak in metastable cubic Ge-Sb-Te using machine learning potentials
di: Choi, Young-Jae, et al.
Pubblicazione: (2023)

Finding well-optimized special quasirandom structures with decision diagram
di: Shinohara, Kohei, et al.
Pubblicazione: (2021)

Shotgun crystal structure prediction using machine-learned formation energies
di: Liu, Chang, et al.
Pubblicazione: (2023)

Accurate and efficient machine learning interatomic potentials for finite temperature modeling of molecular crystals
di: Della Pia, Flaviano, et al.
Pubblicazione: (2025)

General-purpose machine-learned potential for 16 elemental metals and their alloys
di: Song, Keke, et al.
Pubblicazione: (2023)

Nine-element machine-learned interatomic potentials for multiphase refractory alloys
di: Byggmästar, Jesper, et al.
Pubblicazione: (2026)

Electronic properties of metamorphic GaSbBi films on GaAs
di: Hilska, Joonas, et al.
Pubblicazione: (2024)

Screening of material defects using universal machine-learning interatomic potentials
di: Berger, Ethan, et al.
Pubblicazione: (2025)

Ultra-fast interpretable machine-learning potentials
di: Xie, Stephen R., et al.
Pubblicazione: (2021)

Search for stable and low-energy Ce-Co-Cu ternary compounds using machine learning
di: Xia, Weiyi, et al.
Pubblicazione: (2025)

Accelerating global search of adsorbate molecule position using machine-learning interatomic potentials with active learning
di: Klimanova, Olga, et al.
Pubblicazione: (2024)

Deep learning generative model for crystal structure prediction
di: Luo, Xiaoshan, et al.
Pubblicazione: (2024)

Electrostatic interactions in atomistic and machine-learned potentials for polar materials
di: Monacelli, Lorenzo, et al.
Pubblicazione: (2024)

Electronic structure and defect properties of Bi-doped GaN: origins of photoluminescence and optical absorption
di: Liu, Yujie, et al.
Pubblicazione: (2025)

Machine learning supported annealing for prediction of grand canonical crystal structures
di: Couzinie, Yannick, et al.
Pubblicazione: (2024)

Iterative learning scheme for crystal structure prediction with anharmonic lattice dynamics
di: Gao, Hao, et al.
Pubblicazione: (2025)

Insight into the effect of force error on the thermal conductivity from machine-learned potentials
di: Zhou, Wenjiang, et al.
Pubblicazione: (2024)

On the ambient conditions crystal structure of AgSbTe2
di: Sun, Baihong, et al.
Pubblicazione: (2025)

Accelerating material melting temperature predictions by implementing machine learning potentials in the SLUSCHI package
di: CampBell, Audrey, et al.
Pubblicazione: (2024)

Accelerating point defect photo-emission calculations with machine learning interatomic potentials
di: Sharma, Kartikeya, et al.
Pubblicazione: (2025)

Bi2Te3-Sb2Te3-Bi2Te3 Lateral Heterostructures Grown by Molecular Beam Epitaxy
di: Guha, Puspendu, et al.
Pubblicazione: (2025)

Classification of magnetic order from electronic structure by using machine learning
di: Jang, Yerin, et al.
Pubblicazione: (2023)

Electronic structures of crystalline and amorphous GeSe and GeSbTe compounds using machine learning empirical pseudopotentials
di: Kang, Sungmo, et al.
Pubblicazione: (2025)

PAL -- Parallel active learning for machine-learned potentials
di: Zhou, Chen, et al.
Pubblicazione: (2024)

Fast and accurate machine-learned interatomic potentials for large-scale simulations of Cu, Al and Ni
di: Fellman, Aslak, et al.
Pubblicazione: (2024)

Fine-tuning of universal machine-learning interatomic potentials for 2D high-entropy alloys
di: Zhou, Chun, et al.
Pubblicazione: (2026)

Molecular dynamics simulations of heat transport using machine-learned potentials: A mini review and tutorial on GPUMD with neuroevolution potentials
di: Dong, Haikuan, et al.
Pubblicazione: (2024)

Scalable solution chemical synthesis and comprehensive analysis of Bi2Te3 and Sb2Te3
di: Hamawandi, Bejan, et al.
Pubblicazione: (2025)

Classification and enumeration of solid-solid phase transition mechanisms
di: Wang, Fang-Cheng, et al.
Pubblicazione: (2025)

Deformation mechanisms and compressive response of NbTaTiZr alloy via machine learning potentials
di: Liu, Hongyang, et al.
Pubblicazione: (2026)

Effects of GaAs Buffer Layer on Structural, Magnetic, and Transport Properties of Magnetic Topological Insulators Cr$_y$(Bi$_x$Sb$_{1-x}$)$_{2-y}$Te$_3$ and V$_y$(Bi$_x$Sb$_{1-x}$)$_{2-y}$Te$_3$ Films
di: Nakazawa, Yusuke, et al.
Pubblicazione: (2025)

Probing the state of hydrogen in $δ$-AlOOH at mantle conditions with machine learning potential
di: Luo, Chenxing, et al.
Pubblicazione: (2023)

On phase separation and crystallization of Ge-rich GeSbTe alloys from atomistic simulations with a machine learning interatomic potential
di: Kheir, Omar Abou El, et al.
Pubblicazione: (2026)