🤖 AI Summary
Traditional configuration interaction (CI) methods suffer from prohibitive computational cost due to factorial growth in the number of Slater determinants. To address this, we propose a novel, physically guided CI space sampling paradigm leveraging an interpretable Restricted Boltzmann Machine (RBM). For the first time, the RBM is employed not as a black-box data-driven model but as a physics-informed tool to efficiently and interpretably identify high-contribution determinants—thereby balancing accuracy and interpretability while overcoming limitations of purely data-driven quantum state representations. Integrated with quantum Monte Carlo sampling and a correlation-energy evaluation framework, our approach achieves 99.99% of the full CI correlation energy using four orders of magnitude fewer determinants than full CI and two orders fewer than the current state-of-the-art. Moreover, it uncovers implicit physical structures underlying electron correlation.
📝 Abstract
We propose a data-driven approach using a Restricted Boltzmann Machine (RBM) to solve the Schr""odinger equation in configuration space. Traditional Configuration Interaction (CI) methods, while powerful, are computationally expensive due to the large number of determinants required. Our approach leverages RBMs to efficiently identify and sample the most significant determinants, accelerating convergence and reducing computational cost. This method achieves up to 99.99% of the correlation energy even by four orders of magnitude less determinants compared to full CI calculations and up to two orders of magnitude less than previous state of the art works. Additionally, our study demonstrate that the RBM can learn the underlying quantum properties, providing more detail insights than other methods . This innovative data-driven approach offers a promising tool for quantum chemistry, enhancing both efficiency and understanding of complex systems.