Efficient and Scalable Density Functional Theory Hamiltonian Prediction through Adaptive Sparsity Article Swipe
En Luo
,
Xinran Wei
,
Lin Huang
,
Yunyang Li
,
Han Yang
,
Zun Wang
,
Chang Liu
,
Zeyu Xia
,
Jia Zhang
,
Bin Shao
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2502.01171
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2502.01171
Hamiltonian matrix prediction is pivotal in computational chemistry, serving as the foundation for determining a wide range of molecular properties. While SE(3) equivariant graph neural networks have achieved remarkable success in this domain, their substantial computational cost--driven by high-order tensor product (TP) operations--restricts their scalability to large molecular systems with extensive basis sets. To address this challenge, we introduce SPHNet, an efficient and scalable equivariant network, that incorporates adaptive SParsity into Hamiltonian prediction. SPHNet employs two innovative sparse gates to selectively constrain non-critical interaction combinations, significantly reducing tensor product computations while maintaining accuracy. To optimize the sparse representation, we develop a Three-phase Sparsity Scheduler, ensuring stable convergence and achieving high performance at sparsity rates of up to 70%. Extensive evaluations on QH9 and PubchemQH datasets demonstrate that SPHNet achieves state-of-the-art accuracy while providing up to a 7x speedup over existing models. Beyond Hamiltonian prediction, the proposed sparsification techniques also hold significant potential for improving the efficiency and scalability of other SE(3) equivariant networks, further broadening their applicability and impact. Our code can be found at https://github.com/microsoft/SPHNet.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2502.01171
- https://arxiv.org/pdf/2502.01171
- OA Status
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- Related Works
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- OpenAlex ID
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https://openalex.org/W4407131484Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2502.01171Digital Object Identifier
- Title
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Efficient and Scalable Density Functional Theory Hamiltonian Prediction through Adaptive SparsityWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-02-03Full publication date if available
- Authors
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En Luo, Xinran Wei, Lin Huang, Yunyang Li, Han Yang, Zun Wang, Chang Liu, Zeyu Xia, Jia Zhang, Bin ShaoList of authors in order
- Landing page
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https://arxiv.org/abs/2502.01171Publisher landing page
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https://arxiv.org/pdf/2502.01171Direct link to full text PDF
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2502.01171Direct OA link when available
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Scalability, Density functional theory, Hamiltonian (control theory), Computer science, Statistical physics, Mathematics, Mathematical physics, Applied mathematics, Mathematical optimization, Physics, Quantum mechanics, DatabaseTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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| abstract_inverted_index.efficient | 61 |
| abstract_inverted_index.extensive | 50 |
| abstract_inverted_index.improving | 153 |
| abstract_inverted_index.introduce | 58 |
| abstract_inverted_index.molecular | 18, 47 |
| abstract_inverted_index.networks, | 162 |
| abstract_inverted_index.potential | 151 |
| abstract_inverted_index.providing | 132 |
| abstract_inverted_index.Scheduler, | 103 |
| abstract_inverted_index.broadening | 164 |
| abstract_inverted_index.challenge, | 56 |
| abstract_inverted_index.chemistry, | 7 |
| abstract_inverted_index.efficiency | 155 |
| abstract_inverted_index.foundation | 11 |
| abstract_inverted_index.high-order | 38 |
| abstract_inverted_index.innovative | 76 |
| abstract_inverted_index.prediction | 2 |
| abstract_inverted_index.remarkable | 28 |
| abstract_inverted_index.techniques | 147 |
| abstract_inverted_index.Hamiltonian | 0, 71, 142 |
| abstract_inverted_index.Three-phase | 101 |
| abstract_inverted_index.convergence | 106 |
| abstract_inverted_index.demonstrate | 125 |
| abstract_inverted_index.determining | 13 |
| abstract_inverted_index.equivariant | 22, 64, 161 |
| abstract_inverted_index.evaluations | 119 |
| abstract_inverted_index.interaction | 83 |
| abstract_inverted_index.maintaining | 91 |
| abstract_inverted_index.performance | 110 |
| abstract_inverted_index.prediction, | 143 |
| abstract_inverted_index.prediction. | 72 |
| abstract_inverted_index.properties. | 19 |
| abstract_inverted_index.scalability | 44, 157 |
| abstract_inverted_index.selectively | 80 |
| abstract_inverted_index.significant | 150 |
| abstract_inverted_index.substantial | 34 |
| abstract_inverted_index.computations | 89 |
| abstract_inverted_index.cost--driven | 36 |
| abstract_inverted_index.incorporates | 67 |
| abstract_inverted_index.non-critical | 82 |
| abstract_inverted_index.applicability | 166 |
| abstract_inverted_index.combinations, | 84 |
| abstract_inverted_index.computational | 6, 35 |
| abstract_inverted_index.significantly | 85 |
| abstract_inverted_index.sparsification | 146 |
| abstract_inverted_index.representation, | 97 |
| abstract_inverted_index.state-of-the-art | 129 |
| abstract_inverted_index.operations--restricts | 42 |
| abstract_inverted_index.https://github.com/microsoft/SPHNet. | 175 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 10 |
| citation_normalized_percentile |