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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.07927 |
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| _version_ | 1866913171853279232 |
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| author | Yang, Han Liu, Xixian Hu, Chenxi Zhou, Yichi Shi, Yu Liu, Chang Tan, Junfu Li, Jielan Li, Guanzhi Wang, Qian Zhu, Yu Chen, Zekun Chen, Shuizhou Thiemann, Fabian Zeni, Claudio Horton, Matthew Pinsler, Robert Fowler, Andrew Zügner, Daniel Xie, Tian Sun, Lixin Chen, Yicheng Kong, Lingyu Bai, Yeqi Gunceler, Deniz Noé, Frank Hao, Hongxia Lu, Ziheng |
| author_facet | Yang, Han Liu, Xixian Hu, Chenxi Zhou, Yichi Shi, Yu Liu, Chang Tan, Junfu Li, Jielan Li, Guanzhi Wang, Qian Zhu, Yu Chen, Zekun Chen, Shuizhou Thiemann, Fabian Zeni, Claudio Horton, Matthew Pinsler, Robert Fowler, Andrew Zügner, Daniel Xie, Tian Sun, Lixin Chen, Yicheng Kong, Lingyu Bai, Yeqi Gunceler, Deniz Noé, Frank Hao, Hongxia Lu, Ziheng |
| contents | Accurate property characterization is a major bottleneck in materials design. While first-principles methods and task-specific machine-learning models have driven important progress, they remain fundamentally limited in scalability and generalizability across the vast space of structures and properties relevant to real-world materials design. We present MatterSim-MT, a multi-task foundation model for in silico materials simulation and property characterization. The model is pretrained on over 35 million first-principles-labeled structures covering 89 elements, temperatures up to 5000 K and pressures up to 1000 GPa, and is fine-tuned on various properties including Bader charges, magnetic moments, Born effective charges, and dielectric matrices. Out of the box, MatterSim-MT not only serves as a foundation model for predicting material structure, dynamics and thermodynamics, its multi-task architecture also enables a wide range of complex simulations that cannot be captured by potential energy surfaces alone. For example, we demonstrate pressure-dependent LO-TO phonon splitting in SiC with close agreement with experiment, electric hysteresis in ferroelectric BaTiO3, and the cationic-to-anionic redox transition during delithiation of a Li-rich cathode material. Finally, we show that MatterSim-MT scales well with more data and parameters, can be efficiently fine-tuned to higher levels of theory, and can be efficiently extended to new systems via active learning. Overall, we believe this approach provides a scalable route to accurate in silico materials characterization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_07927 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MatterSim-MT: A multi-task foundation model for in silico materials characterization Yang, Han Liu, Xixian Hu, Chenxi Zhou, Yichi Shi, Yu Liu, Chang Tan, Junfu Li, Jielan Li, Guanzhi Wang, Qian Zhu, Yu Chen, Zekun Chen, Shuizhou Thiemann, Fabian Zeni, Claudio Horton, Matthew Pinsler, Robert Fowler, Andrew Zügner, Daniel Xie, Tian Sun, Lixin Chen, Yicheng Kong, Lingyu Bai, Yeqi Gunceler, Deniz Noé, Frank Hao, Hongxia Lu, Ziheng Materials Science Accurate property characterization is a major bottleneck in materials design. While first-principles methods and task-specific machine-learning models have driven important progress, they remain fundamentally limited in scalability and generalizability across the vast space of structures and properties relevant to real-world materials design. We present MatterSim-MT, a multi-task foundation model for in silico materials simulation and property characterization. The model is pretrained on over 35 million first-principles-labeled structures covering 89 elements, temperatures up to 5000 K and pressures up to 1000 GPa, and is fine-tuned on various properties including Bader charges, magnetic moments, Born effective charges, and dielectric matrices. Out of the box, MatterSim-MT not only serves as a foundation model for predicting material structure, dynamics and thermodynamics, its multi-task architecture also enables a wide range of complex simulations that cannot be captured by potential energy surfaces alone. For example, we demonstrate pressure-dependent LO-TO phonon splitting in SiC with close agreement with experiment, electric hysteresis in ferroelectric BaTiO3, and the cationic-to-anionic redox transition during delithiation of a Li-rich cathode material. Finally, we show that MatterSim-MT scales well with more data and parameters, can be efficiently fine-tuned to higher levels of theory, and can be efficiently extended to new systems via active learning. Overall, we believe this approach provides a scalable route to accurate in silico materials characterization. |
| title | MatterSim-MT: A multi-task foundation model for in silico materials characterization |
| topic | Materials Science |
| url | https://arxiv.org/abs/2605.07927 |