Open-Source Fermionic Neural Networks with Ionic Charge Initialization
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866917570468118528 |
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| author | Pranesh, Shai Zhu, Shang Viswanathan, Venkat Ramsundar, Bharath |
| author_facet | Pranesh, Shai Zhu, Shang Viswanathan, Venkat Ramsundar, Bharath |
| contents | Finding accurate solutions to the electronic Schrödinger equation plays an important role in discovering important molecular and material energies and characteristics. Consequently, solving systems with large numbers of electrons has become increasingly important. Variational Monte Carlo (VMC) methods, especially those approximated through deep neural networks, are promising in this regard. In this paper, we aim to integrate one such model called the FermiNet, a post-Hartree-Fock (HF) Deep Neural Network (DNN) model, into a standard and widely used open source library, DeepChem. We also propose novel initialization techniques to overcome the difficulties associated with the assignment of excess or lack of electrons for ions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_10287 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Open-Source Fermionic Neural Networks with Ionic Charge Initialization Pranesh, Shai Zhu, Shang Viswanathan, Venkat Ramsundar, Bharath Machine Learning Chemical Physics Finding accurate solutions to the electronic Schrödinger equation plays an important role in discovering important molecular and material energies and characteristics. Consequently, solving systems with large numbers of electrons has become increasingly important. Variational Monte Carlo (VMC) methods, especially those approximated through deep neural networks, are promising in this regard. In this paper, we aim to integrate one such model called the FermiNet, a post-Hartree-Fock (HF) Deep Neural Network (DNN) model, into a standard and widely used open source library, DeepChem. We also propose novel initialization techniques to overcome the difficulties associated with the assignment of excess or lack of electrons for ions. |
| title | Open-Source Fermionic Neural Networks with Ionic Charge Initialization |
| topic | Machine Learning Chemical Physics |
| url | https://arxiv.org/abs/2401.10287 |