| アイテムタイプ |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2025-12-23 |
| タイトル |
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タイトル |
Towards Improved Quantum Machine Learning for Molecular Force Fields |
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言語 |
ja |
| 言語 |
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言語 |
jpn |
| 資源タイプ |
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資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
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資源タイプ |
journal article |
| 著者 |
Couzinie Yannick
Daimon Shunsuke
Nishi Hirofumi
Ito Natsuki
Harazono Yusuke
Matsushita Yu-ichiro
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| 抄録 |
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内容記述タイプ |
Abstract |
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内容記述 |
This study explores the use of equivariant quantum neural networks (QNN) for generating molecular force fields, focusing on the rMD17 dataset. We consider a QNN architecture based on previous research and point out shortcomings in the parametrization of the atomic environments, that limits its expressivity as an interatomic potential and precludes transferability between molecules. We propose a revised QNN architecture that addresses these shortcomings. While both QNNs show promise in force prediction, with the revised architecture showing improved accuracy, they struggle with energy prediction. Further, both QNNs architectures fail to demonstrate a meaningful scaling law of decreasing errors with increasing training data. These findings highlight the challenges of scaling QNNs for complex molecular systems and emphasize the need for improved encoding strategies, regularization techniques, and hybrid quantum-classical approaches. |
| 書誌情報 |
Physical Review A
巻 112,
p. 062442,
発行日 2025-12
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| 出版者 |
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出版者 |
The American Physical Society (APS) |
| DOI |
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識別子タイプ |
DOI |
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関連識別子 |
10.1103/5s9m-h7yb |
