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Transferable neural wavefunctions for solids
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DOI：10.1038/s43588-025-00872-z.png)
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Abstract
En 中文
Deep-learning-based variational Monte Carlo has emerged as a highly accurate method for solving the many-electron Schrödinger equation. Despite favorable scaling with the number of electrons,
$${\mathcal{O}}({{n}_{{\rm{el}}}}^{4})$$
, the practical value of deep-learning-based variational Monte Carlo is limited by the high cost of optimizing the neural network weights for every system studied. Recent research has proposed optimizing a single neural network across multiple systems, reducing the cost per system. Here we extend this approach to solids, which require numerous calculations across different geometries, boundary conditions and supercell sizes. We demonstrate that optimization of a single ansatz across these variations significantly reduces optimization steps. Furthermore, we successfully transfer a network trained on 2 × 2 × 2 supercells of LiH, to 3 × 3 × 3 supercells, reducing the number of optimization steps required to simulate the large system by a factor of 50 compared with previous work. Investigating crystalline materials often requires calculations for many variations of a system, substantially increasing the computational burden. By training a transferable neural wavefunction across these variations, the cost can be reduced by approximately 50-fold for systems such as graphene and lithium hydride.
Keywords:
deep-learning-based variational Monte Carlo
neural wavefunctions
solids
transferable models
computational cost reduction
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