Abstract
Regression of potential energy functions is one of the most popular applications of machine learning within the field of materials simulation since it would allow accelerating molecular dynamics simulations. Recently, graph-based architectures have been proven to be especially suitable for molecular systems. However, the construction of robust and transferable potentials, resulting in stable dynamical trajectories, still needs to be researched. In this work, we design and compare several neural architectures with different graph convolutional layers to predict the energy of water cluster anions, a system of fundamental interest in chemistry and biology. After identifying the best aggregation procedures for this problem, we have obtained accurate, fast-evaluated and easy-to-implement graph neural network models which could be employed in dynamical simulations in the future.
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Notes
- 1.
The 3D one-particle Schrödinger equation yields:
$$\begin{aligned} H\psi _{e}(\boldsymbol{r}_{1},\ldots ,\boldsymbol{r}_{N};\boldsymbol{r}_{e})=E_{0}\psi _{e}(\boldsymbol{r}_{1},\ldots ,\boldsymbol{r}_{N};\boldsymbol{r}_{e}), \end{aligned}$$where the hamiltonian operator is \(H=-\nabla ^{2}/2m_e+V_{\textrm{W}-e}(\boldsymbol{r}_{1},\ldots ,\boldsymbol{r}_{N};\boldsymbol{r}_{e})\).
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Gijón, A., Molina-Solana, M., Gómez-Romero, J. (2023). Graph Neural Network Potentials for Molecular Dynamics Simulations of Water Cluster Anions. In: Mikyška, J., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2023. ICCS 2023. Lecture Notes in Computer Science, vol 10476. Springer, Cham. https://doi.org/10.1007/978-3-031-36027-5_25
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