Authors: Nikita Fedik | Wei Li | Nicholas Lubbers | Benjamin Nebgen | Sergei Tretiak | Ying Wai Li
Source: ChemRxiv (preprint, 2024-08-05)
Field: Materials Science — Materials Chemistry

Challenges and Opportunities for Machine Learning Potentials in Transition Path Sampling: Alanine Dipeptide and Azobenzene Studies The growing interest in machine learning (ML) tools within chemistry and material science stems from their novelty and ability to predict properties almost as accurately as underlying electronic structure calculations or experiments. Transition path sampling (TPS) offers a practical way to explore transition routes between metastable minima such as conformers and isomers on the multidimensional potential energy surface. However, TPS has historically suffered from the computational cost vs. accuracy trade-off between affordable force-field simulations and expensive high-fidelity quantum mechanical calculations. ML interatomic potentials combined with TPS offer a new approach for the exploration of transition pathways at near-quantum mechanical accuracy, while keeping the computational cost comparable to classical force fields. In this study, we employ the HIP-NN-TS and ANI-1x neural network-based ML potentials, both trained on the ANI-1x dataset of 5 million HCNO structures. We first verify the correctness of our approach by applying it to alanine dipeptide and compare the resulting energy surface and transition paths to the literature. Our findings suggest that proposed approach holds promise for conformational searches, as evidenced by the chemical accuracy (errors ≲ 1 kcal/mol) for thermal molecular dynamics trajectories of alanine dipeptide. While we were able to successfully reconstruct alanine dipeptide’s potential landscape using both HIP-NN-TS and ANI-1x frameworks, we observed that ML models with lower accuracy may still recover additional important conformations. We also find that active learning, augmenting the training data by structures taken from TPS trajectories, improved the accuracy by ~30% with small amounts of additional data. Finally, we evaluated a more intricate case, azobenzene, and reiterated that seemingly simple torsions may bear a challenge for ML potentials and limit their applications in TPS. Inability of HIP-NN-TS to correctly describe the energetics of major rotational pathway in azobenzene isomerization highlights deficiencies of the reference method in describing the electronic degrees of freedom. Our study underscores the importance of domain expertise in selecting physically meaningful pathways for benchmarking ML potentials, especially considering the intricacies of electronic structure in chemical dynamics and non-equilibrium processes. Potential energy surface ; Maxima and minima ; Dipeptide ; Computer science ; Artificial intelligence ; Force field (fiction) ; Energy landscape ; Quantum ; Molecular dynamics ; Correctness ; Potential energy ; Algorithm ; Statistical physics ; Machine learning ; Computational chemistry ; Chemistry ; Physics ; Mathematics ; Molecule ; Quantum mechanics ; Thermodynamics Machine Learning in Materials Science ; Crystallography and molecular interactions ; Advanced Chemical Physics Studies