Investigating the Behavior of Diffusion Models for Accelerating Electronic Structure Calculations

• URL: http://arxiv.org/abs/2311.01491v1
• Date: Thu, 2 Nov 2023 17:58:37 GMT
• Title: Investigating the Behavior of Diffusion Models for Accelerating Electronic Structure Calculations
• Authors: Daniel Rothchild, Andrew S. Rosen, Eric Taw, Connie Robinson, Joseph E. Gonzalez, Aditi S. Krishnapriyan
• Abstract summary: Investigation driven by their potential to significantly accelerate electronic structure calculations using machine learning. We show that the model learns about the first-order structure of the potential energy surface, and then later learns about higher-order structure. For structure relaxations, the model finds geometries with 10x lower energy than those produced by a classical force field for small organic molecules.
• Score: 24.116064925926914
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present an investigation into diffusion models for molecular generation, with the aim of better understanding how their predictions compare to the results of physics-based calculations. The investigation into these models is driven by their potential to significantly accelerate electronic structure calculations using machine learning, without requiring expensive first-principles datasets for training interatomic potentials. We find that the inference process of a popular diffusion model for de novo molecular generation is divided into an exploration phase, where the model chooses the atomic species, and a relaxation phase, where it adjusts the atomic coordinates to find a low-energy geometry. As training proceeds, we show that the model initially learns about the first-order structure of the potential energy surface, and then later learns about higher-order structure. We also find that the relaxation phase of the diffusion model can be re-purposed to sample the Boltzmann distribution over conformations and to carry out structure relaxations. For structure relaxations, the model finds geometries with ~10x lower energy than those produced by a classical force field for small organic molecules. Initializing a density functional theory (DFT) relaxation at the diffusion-produced structures yields a >2x speedup to the DFT relaxation when compared to initializing at structures relaxed with a classical force field.

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