Efficient machine learning approach for accurate free-energy profiles and kinetic rates.

The computational exploration of reactive processes is challenging due to the requirement of thorough sampling across the free energy landscape using accurate ab initio methods. To address these constraints, machine learning potentials are employed, yet their training for this kind of problem is still a laborious and tedious task. In this study, we present an efficient approach to train these potentials by cleverly using a single batch of unbiased trajectories that avoid the pitfalls of trajectories artificially biased along a suboptimal collective variable. This strategy, when integrated with current enhanced sampling techniques, allows to obtain free energy profiles and kinetic rates of ab initio quality, yet dramatically reducing the computational cost.

A New Route to the Prebiotic Synthesis of Glycine via Ab Initio-Based Machine Learning Calculations.

In this work, we study the synthesis of glycine, the simplest amino acid, using ab initio molecular dynamics and enhanced sampling techniques to explore and quantify novel potential pathways. Our protocol integrates state-of-the-art machine learning approaches, allowing us to sample relevant chemical spaces more efficiently. We discover a novel "oxyglycolate path", distinct from the "standard" Strecker mechanism, identify new intermediates, and provide a full thermodynamic characterization of all reaction steps. This alternative pathway aligns better with meteoritic and experimental observations, paving the way for further investigations. Integrating quantum accuracy and machine learning in prebiotic chemistry represents a methodological milestone advancing the exploration of life's prebiotic origins.

Critical Assessment of Data-Driven versus Heuristic Reaction Coordinates in Solution Chemistry.

Reaction coordinates are an essential ingredient of theoretical studies of rare events in chemistry and physics because they carry information about reaction mechanism and allow the computation of free-energy landscapes and kinetic rates. We present a critical assessment of the merits and disadvantages of heuristic reaction coordinates, largely employed today, with respect to coordinates optimized on the basis of reliable transition-path sampling data. We take as a test bed multinanosecond ab initio molecular dynamics simulations of chloride SN2 substitution on methyl chloride in explicit water. The computational protocol we devise allows the unsupervised optimization of agnostic coordinates able to account for solute and solvent contributions, yielding a free-energy reconstruction of quality comparable to the best heuristic coordinates without requiring chemical intuition.

General Density-Based Index to Analyze Charge Transfer Phenomena: From Models to Butterfly Molecules.

We present a new formula and implementation for a descriptor enabling quantification of the electron-hole distance associated with a charge transfer of an optical transition, on the basis of the knowledge of the densities of the electronic ground and excited states. This index is able to define a charge-transfer length even for systems that would be otherwise difficult to treat, like symmetric molecules, while maintaining a very low computational cost and the possibility to be coupled to any method providing ground and excited state electron densities. After a benchmark of its performance on a series of push-pull molecules, the index has been applied to a set of large symmetric luminophores, the so-called "butterfly molecules", showing promising applications in optoelectronics, to highlight its potential use in the design of new compounds.