QMCTorch: Molecular Wavefunctions with Neural Components for Energy and Force Calculations
Fuente:
arXiv
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866918055033962496 |
|---|---|
| author | Renaud, Nicolas |
| author_facet | Renaud, Nicolas |
| contents | In this paper, we present results obtained using QMCTorch, a modular framework for real-space Quantum Monte Carlo (QMC) simulations of small molecular systems. Built on the popular deep learning library PyTorch, QMCTorch is GPU-native and enables the integration of machine learning-inspired components into the wave function ansatz, such as neural network backflow transformations and Jastrow factors, while leveraging efficient optimization algorithms. QMCTorch interfaces with two widely used quantum chemistry packages - PySCF and ADF - which provide initial values for the atomic orbital exponents and molecular orbital coefficients. In this study, we present wavefunction optimizations for four molecules: $H_2$, $LiH$, $Li_2$, and $CO$, using various wavefunction ansätze. We also compute their dissociation energy curves and the corresponding interatomic forces along these curves. Our results show good agreement with baseline calculations, recovering a significant portion of the correlation energy. QMCTorch provides a modular and extendable platform for rapidly prototyping new wavefunction ansätze, evaluating their performance, and analyzing optimization outcomes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_09743 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | QMCTorch: Molecular Wavefunctions with Neural Components for Energy and Force Calculations Renaud, Nicolas Chemical Physics In this paper, we present results obtained using QMCTorch, a modular framework for real-space Quantum Monte Carlo (QMC) simulations of small molecular systems. Built on the popular deep learning library PyTorch, QMCTorch is GPU-native and enables the integration of machine learning-inspired components into the wave function ansatz, such as neural network backflow transformations and Jastrow factors, while leveraging efficient optimization algorithms. QMCTorch interfaces with two widely used quantum chemistry packages - PySCF and ADF - which provide initial values for the atomic orbital exponents and molecular orbital coefficients. In this study, we present wavefunction optimizations for four molecules: $H_2$, $LiH$, $Li_2$, and $CO$, using various wavefunction ansätze. We also compute their dissociation energy curves and the corresponding interatomic forces along these curves. Our results show good agreement with baseline calculations, recovering a significant portion of the correlation energy. QMCTorch provides a modular and extendable platform for rapidly prototyping new wavefunction ansätze, evaluating their performance, and analyzing optimization outcomes. |
| title | QMCTorch: Molecular Wavefunctions with Neural Components for Energy and Force Calculations |
| topic | Chemical Physics |
| url | https://arxiv.org/abs/2506.09743 |