Implicit Neural Representations for Chemical Reaction Paths
This provides a more versatile method for computational chemists to model reaction paths, though it is incremental as it builds on existing neural representation techniques.
The paper tackles the problem of representing chemical reaction paths by optimizing neural networks to model minimum energy paths as continuous functions, offering a flexible alternative to discrete methods like Nudged Elastic Band (NEB), and demonstrates advantages in handling challenging atomistic systems such as poor initial guesses, multiple paths, and complex mechanisms.
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 such as 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.