Convergence of variational Monte Carlo simulation and scale-invariant pre-training
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2023
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866915183380660224 |
|---|---|
| author | Abrahamsen, Nilin Ding, Zhiyan Goldshlager, Gil Lin, Lin |
| author_facet | Abrahamsen, Nilin Ding, Zhiyan Goldshlager, Gil Lin, Lin |
| contents | We provide theoretical convergence bounds for the variational Monte Carlo (VMC) method as applied to optimize neural network wave functions for the electronic structure problem. We study both the energy minimization phase and the supervised pre-training phase that is commonly used prior to energy minimization. For the energy minimization phase, the standard algorithm is scale-invariant by design, and we provide a proof of convergence for this algorithm without modifications. The pre-training stage typically does not feature such scale-invariance. We propose using a scale-invariant loss for the pretraining phase and demonstrate empirically that it leads to faster pre-training. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_11602 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Convergence of variational Monte Carlo simulation and scale-invariant pre-training Abrahamsen, Nilin Ding, Zhiyan Goldshlager, Gil Lin, Lin Machine Learning Computational Physics We provide theoretical convergence bounds for the variational Monte Carlo (VMC) method as applied to optimize neural network wave functions for the electronic structure problem. We study both the energy minimization phase and the supervised pre-training phase that is commonly used prior to energy minimization. For the energy minimization phase, the standard algorithm is scale-invariant by design, and we provide a proof of convergence for this algorithm without modifications. The pre-training stage typically does not feature such scale-invariance. We propose using a scale-invariant loss for the pretraining phase and demonstrate empirically that it leads to faster pre-training. |
| title | Convergence of variational Monte Carlo simulation and scale-invariant pre-training |
| topic | Machine Learning Computational Physics |
| url | https://arxiv.org/abs/2303.11602 |