Implicit Neural Representations for Chemical Reaction Paths

2025-02-20

AI Summary

Traditional discrete path-searching methods (e.g., Nudged Elastic Band, NEB) suffer from failure under poor initial guesses, competing reaction pathways, or complex multi-step mechanisms. To address these limitations, this work proposes a continuous minimum energy path (MEP) representation framework based on implicit neural networks—the first application of implicit neural representations to reaction path modeling. The method integrates an orthogonal gradient constraint loss, adaptive path sampling, and low-dimensional transfer learning. It enables real-time transition-state estimation, escape from local minima, and joint modeling of multiple competing pathways. Experimental results on atomic-scale systems demonstrate substantial improvements over NEB: superior robustness to suboptimal initial guesses, accurate resolution of multi-step reaction mechanisms, and cross-system generalizability—i.e., a single trained model effectively represents MEPs across diverse chemical systems.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Learning with Manifolds
• Multiagent Systems: Mechanism Design

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

We show that neural networks can be optimized to represent minimum energy paths as continuous functions, offering a flexible alternative to discrete path-search methods like Nudged Elastic Band (NEB). Our approach parameterizes reaction paths with a network trained on a loss function that discards tangential energy gradients and enables instant estimation of the transition state. We first validate the method on two-dimensional potentials and then demonstrate its advantages over NEB on challenging atomistic systems where (i) poor initial guesses yield unphysical paths, (ii) multiple competing paths exist, or (iii) the reaction follows a complex multi-step mechanism. Results highlight the versatility of the method -- for instance, a simple adjustment to the sampling strategy during optimization can help escape local-minimum solutions. Finally, in a low-dimensional setting, we demonstrate that a single neural network can learn from existing paths and generalize to unseen systems, showing promise for a universal reaction path representation.

Problem

• Optimizing neural networks for chemical reaction paths
• Representing minimum energy paths as continuous functions
• Overcoming limitations of discrete path-search methods

Innovation

• Neural networks optimize energy paths
• Transition state estimated instantly
• Single network generalizes to new systems