PyRETIS 3: Conquering rare and slow events without boundaries

We present and discuss the advancements made in PyRETIS 3, the third instalment of our Python library for an efficient and user-friendly rare event simulation, focused to execute molecular simulations with replica exchange transition interface sampling (RETIS) and its variations. Apart from a general rewiring of the internal code towards a more modular structure, several recently developed sampling strategies have been implemented. These include recently developed Monte Carlo moves to increase path decorrelation and convergence rate, and new ensemble definitions to handle the challenges of long-lived metastable states and transitions with unbounded reactant and product states. Additionally, the post-analysis software PyVisa is now embedded in the main code, allowing fast use of machine-learning algorithms for clustering and visualising collective variables in the simulation data.

Chemistrees: Data-Driven Identification of Reaction Pathways via Machine Learning

We propose to analyze molecular dynamics (MD) output via a supervised machine learning (ML) algorithm, the decision tree. The approach aims to identify the predominant geometric features which correlate with trajectories that transition between two arbitrarily defined states. The data-driven algorithm aims to identify these features without the bias of human “chemical intuition”. We demonstrate the method by analyzing the proton exchange reactions in formic acid solvated in small water clusters. The simulations were performed with ab initio MD combined with a method to efficiently sample the rare event, path sampling. Our ML analysis identified relevant geometric variables involved in the proton transfer reaction and how they may change as the number of solvating water molecules changes.

An Ab Initio Molecular Dynamics Study of the Hydrolysis Reaction of Sulfur Trioxide Catalyzed by a Formic Acid or Water Molecule

Ab initio molecular dynamics (AIMD) calculations have been performed to investigate the role of dynamical and steric effects in formic acid (FA) or H₂O-catalyzed gas phase hydrolysis of SO₃ to form sulfuric acid. This was done by colliding FA or H₂O with the SO₃-H₂O complex and the water dimer with the SO₃ molecule and analyzing the outcomes of 230 AIMD trajectories. Our calculations show that, within simulation times used, sulfuric acid is formed in 5% of FA collisions but is not produced when H₂O collides with the SO₃-H₂O complex or when the water dimer collides with the SO₃ molecule. We also find that FA collisions have about 2 times higher probability to form the prereactive complex than H₂O collisions. Moreover, our simulations show that the SO₃-H₂O-FA prereactive complex is more stable in time than the SO₃-H₂O-H₂O prereactive complex. These findings indicate that the FA-catalyzed mechanism is favored over the H₂O one when looking from the steric and dynamic effect point of view. Additionally, AIMD simulations starting from the optimized structure of the SO₃-H₂O-FA prereactive complex have been computed to qualitatively estimate the rate of the sulfuric acid formation. Collisional energy has been observed to promote sulfuric acid formation more effectively than thermal excitation.