Saved in:
| Main Authors: | Watanabe, Aoi, Sato, Ryuhei, Kinefuchi, Ikuya, Shibuta, Yasushi |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.10913 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Competing Hydrogenation Pathways to Metastable CaH$_6$ Revealed by Machine-Learning-Potential Molecular Dynamics
by: Sato, Ryuhei, et al.
Published: (2026)
by: Sato, Ryuhei, et al.
Published: (2026)
Universal platform of point-gap topological phases from topological materials
by: Nakamura, Daichi, et al.
Published: (2023)
by: Nakamura, Daichi, et al.
Published: (2023)
Practical deviational particle method for variance reduction in polyatomic gas DSMC simulations
by: Shiraishi, Takehiro, et al.
Published: (2024)
by: Shiraishi, Takehiro, et al.
Published: (2024)
Screening of material defects using universal machine-learning interatomic potentials
by: Berger, Ethan, et al.
Published: (2025)
by: Berger, Ethan, et al.
Published: (2025)
Polymorphic crystallites model for monolayer amorphous materials
by: Zhu, Le-Ye, et al.
Published: (2026)
by: Zhu, Le-Ye, et al.
Published: (2026)
Advances in modeling complex materials: The rise of neuroevolution potentials
by: Ying, Penghua, et al.
Published: (2025)
by: Ying, Penghua, et al.
Published: (2025)
MADAS -- A Python framework for assessing similarity in materials-science data
by: Kuban, Martin, et al.
Published: (2024)
by: Kuban, Martin, et al.
Published: (2024)
A complete phase-field fracture model for brittle materials subjected to thermal shocks
by: Zeng, Bo, et al.
Published: (2026)
by: Zeng, Bo, et al.
Published: (2026)
Universal materials model of deep-learning density functional theory Hamiltonian
by: Wang, Yuxiang, et al.
Published: (2024)
by: Wang, Yuxiang, et al.
Published: (2024)
What can machine learning help with microstructure-informed materials modeling and design?
by: Peng, Xiang-Long, et al.
Published: (2024)
by: Peng, Xiang-Long, et al.
Published: (2024)
Algorithmic differentiation for plane-wave DFT: materials design, error control and learning model parameters
by: Schmitz, Niklas Frederik, et al.
Published: (2025)
by: Schmitz, Niklas Frederik, et al.
Published: (2025)
A universal material model subroutine for soft matter systems
by: Peirlinck, Mathias, et al.
Published: (2024)
by: Peirlinck, Mathias, et al.
Published: (2024)
Generalizing the structural phase field crystal approach for modeling solid-liquid-vapor phase transformations in pure materials
by: Coelho, Daniel L., et al.
Published: (2024)
by: Coelho, Daniel L., et al.
Published: (2024)
Pre-training, fine-tuning, and distillation (PFD): Automatically generating machine learning force fields from universal models
by: Wang, Ruoyu, et al.
Published: (2025)
by: Wang, Ruoyu, et al.
Published: (2025)
Accelerating active learning materials discovery with FAIR data and workflows: a case study for alloy melting temperatures
by: Harwani, Mohnish, et al.
Published: (2024)
by: Harwani, Mohnish, et al.
Published: (2024)
A tomographic interpretation of structure-property relations for materials discovery
by: Ortega-Ochoa, Raul, et al.
Published: (2025)
by: Ortega-Ochoa, Raul, et al.
Published: (2025)
Tensor-involved peridynamics: A unified framework for isotropic and anisotropic materials
by: Tian, Hao, et al.
Published: (2024)
by: Tian, Hao, et al.
Published: (2024)
A comparative study of transformer models and recurrent neural networks for path-dependent composite materials
by: Uvdal, Petter, et al.
Published: (2026)
by: Uvdal, Petter, et al.
Published: (2026)
A critical assessment of bonding descriptors for predicting materials properties
by: Naik, Aakash Ashok, et al.
Published: (2026)
by: Naik, Aakash Ashok, et al.
Published: (2026)
Generative deep learning for the inverse design of materials
by: Long, Teng, et al.
Published: (2024)
by: Long, Teng, et al.
Published: (2024)
Compressing and forecasting atomic material simulations with descriptors
by: Swinburne, Thomas D
Published: (2023)
by: Swinburne, Thomas D
Published: (2023)
Phase field cohesive zone modeling for fatigue crack propagation in quasi-brittle materials
by: Baktheer, A., et al.
Published: (2024)
by: Baktheer, A., et al.
Published: (2024)
First-principles screening of materials with extreme effective masses
by: Błazucki, Szymon, et al.
Published: (2025)
by: Błazucki, Szymon, et al.
Published: (2025)
MAMBO: a lightweight ontology for multiscale materials and applications
by: Piane, Fabio Le, et al.
Published: (2024)
by: Piane, Fabio Le, et al.
Published: (2024)
PET-MAD, a lightweight universal interatomic potential for advanced materials modeling
by: Mazitov, Arslan, et al.
Published: (2025)
by: Mazitov, Arslan, et al.
Published: (2025)
Inelastic Constitutive Kolmogorov-Arnold Networks: A generalized framework for automated discovery of interpretable inelastic material models
by: Ji, Chenyi, et al.
Published: (2026)
by: Ji, Chenyi, et al.
Published: (2026)
Exploring the magnetic landscape of easily-exfoliable two-dimensional materials
by: Haddadi, Fatemeh, et al.
Published: (2025)
by: Haddadi, Fatemeh, et al.
Published: (2025)
Electrostatic interactions in atomistic and machine-learned potentials for polar materials
by: Monacelli, Lorenzo, et al.
Published: (2024)
by: Monacelli, Lorenzo, et al.
Published: (2024)
Guidelines for accurate and efficient calculations of mobilities in two-dimensional materials
by: Zhou, Jiaqi, et al.
Published: (2024)
by: Zhou, Jiaqi, et al.
Published: (2024)
Equivariant neural network for Green's functions of molecules and materials
by: Dong, Xinyang, et al.
Published: (2023)
by: Dong, Xinyang, et al.
Published: (2023)
Discovery of sustainable energy materials via the machine-learned material space
by: Grunert, Malte, et al.
Published: (2025)
by: Grunert, Malte, et al.
Published: (2025)
CEMP: a platform unifying high-throughput online calculation, databases and predictive models for clean energy materials
by: Wang, Jifeng, et al.
Published: (2025)
by: Wang, Jifeng, et al.
Published: (2025)
A physics-aware deep learning model for shear band formation around collapsing pores in shocked reactive materials
by: Cheng, Xinlun, et al.
Published: (2025)
by: Cheng, Xinlun, et al.
Published: (2025)
Beyond surfaces: quantifying internal radiative heat transport in dense materials
by: Tiwari, Janak, et al.
Published: (2025)
by: Tiwari, Janak, et al.
Published: (2025)
Direction-aware topological descriptors for Young's modulus prediction in porous materials
by: Topolnicki, Rafał, et al.
Published: (2026)
by: Topolnicki, Rafał, et al.
Published: (2026)
Wenzhou TE: a first-principles calculated thermoelectric materials database
by: Fang, Ying, et al.
Published: (2024)
by: Fang, Ying, et al.
Published: (2024)
Surface Magnetization in Antiferromagnets: Classification, example materials, and relation to magnetoelectric responses
by: Weber, Sophie F., et al.
Published: (2023)
by: Weber, Sophie F., et al.
Published: (2023)
Influence of the microstructure on the mechanical behavior of nanoporous materials under large strains
by: Chandrasekaran, Rajesh, et al.
Published: (2024)
by: Chandrasekaran, Rajesh, et al.
Published: (2024)
Coarse-grained crystal graph neural networks for reticular materials design
by: Korolev, Vadim, et al.
Published: (2023)
by: Korolev, Vadim, et al.
Published: (2023)
Data-Assimilated Crystal Growth Simulation for Multiple Crystalline Phases
by: Kubo, Yuuki, et al.
Published: (2024)
by: Kubo, Yuuki, et al.
Published: (2024)
Similar Items
-
Competing Hydrogenation Pathways to Metastable CaH$_6$ Revealed by Machine-Learning-Potential Molecular Dynamics
by: Sato, Ryuhei, et al.
Published: (2026) -
Universal platform of point-gap topological phases from topological materials
by: Nakamura, Daichi, et al.
Published: (2023) -
Practical deviational particle method for variance reduction in polyatomic gas DSMC simulations
by: Shiraishi, Takehiro, et al.
Published: (2024) -
Screening of material defects using universal machine-learning interatomic potentials
by: Berger, Ethan, et al.
Published: (2025) -
Polymorphic crystallites model for monolayer amorphous materials
by: Zhu, Le-Ye, et al.
Published: (2026)