ニューラルネットワークによる有効相互作用評価(Neural Networks for Evaluating Effective Interactions)

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2025-06-02 パシフィック・ノースウェスト国立研究所(PNNL)

ニューラルネットワークによる有効相互作用評価(Neural Networks for Evaluating Effective Interactions)
The VNet neural network can be used to rapidly generate downfolded Hamiltonians that capture electron correlation effects in small dimensionality spaces.
(Image by Ben Watson | Pacific Northwest National Laboratory)

米国エネルギー省のパシフィック・ノースウェスト国立研究所(PNNL)の研究チームは、ニューラルネットワーク(NN)を活用し、多体量子系の有効相互作用を効率的に評価する新手法「VNet」を開発しました。この手法は、従来のカップルドクラスター理論に基づくダウンフォールディング手法とNNを統合し、化学的に重要なアクティブスペース内での有効ハミルトニアンの迅速な評価を可能にします。特に、水(H₂O)やフッ化水素(HF)などの小分子系において、限られた核配置のデータから学習し、他の構造への補間・外挿が高精度で行えることを示しました。これにより、従来膨大な計算コストを要した多体相互作用の評価が大幅に効率化され、計算化学や材料科学、量子情報科学などの分野での応用が期待されます。

<関連情報>

ニューラルネットワークモデルによる低次元空間での効果的な多体相互作用 Effective many-body interactions in reduced-dimensionality spaces through neural network models

Senwei Liang, Karol Kowalski, Chao Yang, and Nicholas P. Bauman
Physical Review Research  Published: 17 December, 2024
DOI: https://doi.org/10.1103/PhysRevResearch.6.043287

Abstract

Accurately describing properties of challenging problems in physical sciences often requires complex mathematical models that are unmanageable to tackle head on. Therefore, developing reduced-dimensionality representations that encapsulate complex correlation effects in many-body systems is crucial to advance the understanding of these complicated problems. However, a numerical evaluation of these predictive models can still be associated with a significant computational overhead. To address this challenge, in this paper we discuss a combined framework that integrates recent advances in the development of active-space representations of coupled cluster (CC) downfolded Hamiltonians with neural network approaches. The primary objective of this effort is to train neural networks to eliminate the computationally expensive steps required for evaluating hundreds or thousands of Hugenholtz diagrams, which correspond to multidimensional tensor contractions necessary for evaluating a many-body form of downfolded effective Hamiltonians. Using small molecular systems (the H2⁡O and HF molecules) as examples, we demonstrate that training neural networks employing effective Hamiltonians for a few nuclear geometries of molecules can accurately interpolate or extrapolate their forms to other geometrical configurations characterized by different intensities of correlation effects. We also discuss differences between effective interactions that define CC downfolded Hamiltonians with those of bare Hamiltonians defined by Coulomb interactions in the active spaces.

1504数理・情報
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