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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.07155 |
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| _version_ | 1866909585439195136 |
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| author | Yin, Bangchen Wang, Jiaao Du, Weitao Wang, Pengbo Ying, Penghua Jia, Haojun Zhang, Zisheng Du, Yuanqi Gomes, Carla P. Duan, Chenru Henkelman, Graeme Xiao, Hai |
| author_facet | Yin, Bangchen Wang, Jiaao Du, Weitao Wang, Pengbo Ying, Penghua Jia, Haojun Zhang, Zisheng Du, Yuanqi Gomes, Carla P. Duan, Chenru Henkelman, Graeme Xiao, Hai |
| contents | Molecular dynamics simulations demand an unprecedented combination of accuracy and scalability to tackle grand challenges in catalysis and materials design. To bridge this gap, we present AlphaNet, a local-frame-based equivariant model that simultaneously improves computational efficiency and predictive precision for interatomic interactions. By constructing equivariant local frames with learnable geometric transitions, AlphaNet encodes atomic environments with enhanced representational capacity, achieving state-of-the-art accuracy in energy and force predictions. Extensive benchmarks on large-scale datasets spanning molecular reactions, crystal stability, and surface catalysis (Matbench Discovery and OC2M) demonstrate its superior performance over existing neural network interatomic potentials while ensuring scalability across diverse system sizes with varying types of interatomic interactions. The synergy of accuracy, efficiency, and transferability positions AlphaNet as a transformative tool for modeling multiscale phenomena, decoding dynamics in catalysis and functional interfaces, with direct implications for accelerating the discovery of complex molecular systems and functional materials. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_07155 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AlphaNet: Scaling Up Local-frame-based Atomistic Interatomic Potential Yin, Bangchen Wang, Jiaao Du, Weitao Wang, Pengbo Ying, Penghua Jia, Haojun Zhang, Zisheng Du, Yuanqi Gomes, Carla P. Duan, Chenru Henkelman, Graeme Xiao, Hai Machine Learning Molecular dynamics simulations demand an unprecedented combination of accuracy and scalability to tackle grand challenges in catalysis and materials design. To bridge this gap, we present AlphaNet, a local-frame-based equivariant model that simultaneously improves computational efficiency and predictive precision for interatomic interactions. By constructing equivariant local frames with learnable geometric transitions, AlphaNet encodes atomic environments with enhanced representational capacity, achieving state-of-the-art accuracy in energy and force predictions. Extensive benchmarks on large-scale datasets spanning molecular reactions, crystal stability, and surface catalysis (Matbench Discovery and OC2M) demonstrate its superior performance over existing neural network interatomic potentials while ensuring scalability across diverse system sizes with varying types of interatomic interactions. The synergy of accuracy, efficiency, and transferability positions AlphaNet as a transformative tool for modeling multiscale phenomena, decoding dynamics in catalysis and functional interfaces, with direct implications for accelerating the discovery of complex molecular systems and functional materials. |
| title | AlphaNet: Scaling Up Local-frame-based Atomistic Interatomic Potential |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2501.07155 |