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  • Open Access

Large Language Model-Type Architecture for High-Dimensional Molecular Potential Energy Surfaces

Xiao Zhu and Srinivasan S. Iyengar*

  • Department of Chemistry, Department of Physics, and the Indiana University Quantum Science and Engineering Center (IU-QSEC), Indiana University, 800 East Kirkwood Avenue, Bloomington, Indiana 47405, USA

  • *Contact author: iyengar@iu.edu
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Phys. Rev. X 16, 011012 – Published 22 January, 2026

DOI: https://doi.org/10.1103/2qcy-8n8g

Abstract

Computing high-dimensional potential energy surfaces for molecular systems and materials is considered to be a great challenge in computational chemistry with potential impact in a range of areas including the fundamental prediction of reaction rates. In this paper, we design and discuss an algorithm that has similarities to large language models in generative AI and natural language processing. Specifically, we represent a molecular system as a graph which contains a set of nodes, edges, faces, etc. Interactions between these sets, which represent molecular subsystems in our case, are used to construct the potential energy surface for a reasonably sized chemical system with 51 nuclear dimensions. For this purpose, a family of neural networks that pertain to the graph-theoretically obtained subsystems get the job done for this 51 nuclear dimensional system. We then ask if this same family of lower-dimensional graph-based neural networks can be transformed to provide accurate predictions for a 186-dimensional potential energy surface. We find that our algorithm does provide accurate results for this larger-dimensional problem with sub-kcal/mol accuracy for the higher-dimensional potential energy surface problem. Indeed, as a result of these developments, here we produce efforts toward a full-dimensional potential energy surface for the protonated 21-water cluster (186 nuclear dimensions) at coupled-cluster single and double level accuracy.

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Popular Summary

Computing accurate, high-dimensional molecular potential energy surfaces is a foundational challenge in computational chemistry, essential for predicting reaction rates and designing new materials. We developed an algorithm that represents molecular systems as graphs, creating a family of decoupled neural networks to reduce the exponential complexity typical of post-Hartree-Fock calculations. Our approach shares deep architectural similarities with the attention mechanism in large language models, using geometric and topological weights rather than learnable parameters to project importance onto value. By implementing an incremental transfer learning protocol, we successfully adapted models from a 51-dimensional solvated Zundel system to provide sub-kcal/mol accuracy for a 186-dimensional protonated 21-water cluster. This framework establishes a scalable paradigm for computing accurate potential surfaces of large clusters, offering a new perspective on the interplay between molecular information and generative artificial intelligence.

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Phys. Rev. X 16, 011012– Published 22 January, 2026
  • Received 10 December 2024
  • Revised 10 September 2025
  • Accepted 5 January 2026
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