Neural network predicts molecular energy across multiple shapes
NucleoNet: A Nuclear-Aware Determinant Ansatz for Multi-Geometry Neural Variational Monte Carlo
Machine Learning
Summary
Calculating how molecules behave when their shapes change is important for understanding chemistry, but usually requires repeating complex calculations for each shape. The authors present NucleoNet, a method that trains a single neural network to handle many molecule shapes at once by treating electrons and nuclei together in a shared model. This approach simplifies predictions and can smoothly estimate energies for new shapes the model hasn’t seen before. It works well on small molecules, using fewer parameters than other methods with similar accuracy.
What this means in practice
- •For computational chemists: Model energy changes of molecules across different shapes using a single efficient neural network to reduce repeated calculations.
- •For materials simulation engineers: Generate accurate potential energy surfaces from one model to improve simulations of molecular structures under various conditions.
Authors
Qiao Gao, Yang Huang, Enze Hou, Yuzhi Liu, Jingrun Chen, Han Wang
Abstract
Neural-network variational Monte Carlo provides an accurate route to solving the electronic Schrödinger equation, but many neural wavefunctions are optimized for a single nuclear geometry, requiring separate calculations to map out a potential energy surface. We introduce NucleoNet, a nuclear-aware determinant ansatz in which electrons and nuclei form a unified particle set propagated through a shared electron--nucleus backbone that jointly evolves one-particle and two-particle representations. Nuclei carry learned representations that evolve alongside the electronic representations and condition orbital construction, while antisymmetry is imposed only by electronic determinants. On equilibrium small-molecule benchmarks, NucleoNet achieves accuracy comparable to established neural-wavefunction baselines with substantially fewer parameters. For each molecular system, a single model optimized jointly across geometries describes the N$_2$ dissociation curve, Li displacement in LiCN, and the ethane and formamide minimum-energy paths for bond dissociation. Sparse-geometry training generalizes to unseen geometries for N$_2$ and LiCN without further optimization, and N$_2$ ablations show that nuclear streams substantially improve this interpolation. These results show that a compact wavefunction with jointly evolved nuclear representations can describe the geometries of a molecular system with a single set of parameters.