mlsanityData to 14 Oct 2026

Predicting Properties of Periodic Systems from Cluster Data: A Case Study of Liquid Water

arXiv 2312.01414 ↗First posted 3 Dec 2023physics.comp-ph cond-mat.mtrl-sci stat.MLcited by 0

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Abstract (arXiv)

The accuracy of the training data limits the accuracy of bulk properties from machine-learned potentials. For example, hybrid functionals or wave-function-based quantum chemical methods are readily available for cluster data but effectively out-of-scope for periodic structures. We show that local, atom-centred descriptors for machine-learned potentials enable the prediction of bulk properties from cluster model training data, agreeing reasonably well with predictions from bulk training data. We demonstrate such transferability by studying structural and dynamical properties of bulk liquid water with density functional theory and have found an excellent agreement with experimental as well as theoretical counterparts.

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References · 14

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  12. 2021Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Momentscited by 9
2 more
  1. 2019Automated Fitting of Neural Network Potentials at Coupled Cluster Accuracy: Protonated Water Clusters as Testing Groundcited by 8
  2. 2020Deep Learning for UV Absorption Spectra with SchNarc: First Steps Towards Transferability in Chemical Compound Spacecited by 4

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