Monte Carlo-Simulated Annealing and Machine Learning-Based Funneled Approach for Finding the Global Minimum Structure of Molecular Clusters

Michal Roth; Yoni Toker; Dan T Major

doi:10.1021/acsomega.3c07600

Monte Carlo-Simulated Annealing and Machine Learning-Based Funneled Approach for Finding the Global Minimum Structure of Molecular Clusters

ACS Omega. 2023 Dec 19;9(1):1298-1309. doi: 10.1021/acsomega.3c07600. eCollection 2024 Jan 9.

Authors

Michal Roth^{1

2}, Yoni Toker^{1

2}, Dan T Major^{3

2}

Affiliations

¹ Department of Physics, Bar-Ilan University, Ramat-Gan 5290002, Israel.
² Institute of Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat-Gan 5290002, Israel.
³ Department of Chemistry, Bar-Ilan University, Ramat-Gan 5290002, Israel.

Abstract

Understanding the physical underpinnings and geometry of molecular clusters is of great importance in many fields, ranging from studying the beginning of the universe to the formation of atmospheric particles. To this end, several approaches have been suggested, yet identifying the most stable cluster geometry (i.e., global potential energy minimum) remains a challenge, especially for highly symmetric clusters. Here, we suggest a new funneled Monte Carlo-based simulated annealing (SA) approach, which includes two key steps: generation of symmetrical clusters and classification of the clusters according to their geometry using machine learning (MCSA-ML). We demonstrate the merits of the MCSA-ML method in comparison to other approaches on several Lennard-Jones (LJ) clusters and four molecular clusters-Ser₈(Cl^-)₂, H⁺(H₂O)₆, Ag⁺(CO₂)₈, and Bet₄Cl^-. For the latter of these clusters, the correct structure is unknown, and hence, we compare the experimental and simulated fragmentation patterns, and the fragmentation of the proposed global minimum matches experiments closely. Additionally, based on the fragmentation of the predicted betaine cluster, we were able to identify hitherto unknown neutral fragmentation channels. In comparison to results obtained with other methods, we demonstrated a superior ability of MCSA-ML to predict clusters with high symmetry and similar abilities to predict clusters with asymmetrical structures.