Rémi Schlama | Searcharxiv

arXiv2026

Drift-React: One-step Generation of Reaction Pathways via SE(3) Drifting Fields

Mapping reaction pathways and transition states (TS) is fundamental to chemistry but computationally expensive at scale. The minimum energy pathway (MEP) dictates reaction rates and mechanisms, yet recovering it via electronic-structure methods requires thousands of costly force evaluations. Recent generative models accelerate TS identification but rely on iterative inference and only predict isolated saddle-point snapshots, missing the continuous reaction trajectory. We introduce Drift-React, an $\mathrm{SE}(3)$-equivariant generative framework that predicts complete reaction pathways in a single forward pass from only reactant and product geometries. By shifting distribution evolution to training via a Sinkhorn-weighted drifting field, Drift-React eliminates both the iterative force evaluations of NEB-style methods and the sequential ODE/SDE integration of diffusion and flow matching models. Evaluated on the Transition1x and Halo8 datasets, our one-step model generates physically consistent MEPs that accurately capture energetic bottlenecks and enable arbitrary-resolution sampling along the reaction coordinate. For isolated TS prediction, Drift-React matches the sub-Ångström accuracy of state-of-the-art iterative models while delivering orders-of-magnitude acceleration, clearing a major computational bottleneck for large-scale reaction network exploration.

physics.chem-ph↗

arXiv2025

Swarm Intelligence for Chemical Reaction Optimisation

Chemical reaction optimisation is essential for synthetic chemistry and pharmaceutical development, demanding the extensive exploration of many reaction parameters to achieve efficient and sustainable processes. We report $α$-PSO, a novel nature-inspired metaheuristic algorithm that augments canonical particle swarm optimisation (PSO) with machine learning (ML) for parallel reaction optimisation. Unlike black-box ML approaches that obscure decision-making processes, $α$-PSO uses mechanistically clear optimisation strategies through simple, physically intuitive swarm dynamics directly connected to experimental observables, enabling practitioners to understand the components driving each optimisation decision. We establish a theoretical framework for reaction landscape analysis using local Lipschitz constants to quantify reaction space "roughness", distinguishing between smoothly varying landscapes with predictable surfaces and rough landscapes with many reactivity cliffs. This analysis guides adaptive $α$-PSO parameter selection, optimising performance for different reaction topologies. Systematic evaluation of $α$-PSO across pharmaceutically relevant reaction benchmarks demonstrates competitive performance with state-of-the-art Bayesian optimisation methods, while two prospective high-throughput experimentation (HTE) campaigns showed that $α$-PSO identified optimal reaction conditions more rapidly than Bayesian optimisation. $α$-PSO combines the predictive capability of advanced black-box ML methods with interpretable metaheuristic procedures, offering chemists an effective framework for parallel reaction optimisation that maintains methodological clarity while achieving highly performant experimental outcomes. Alongside our open-source $α$-PSO implementation, we release $989$ new high-quality Pd-catalysed Buchwald-Hartwig and Suzuki reactions.

physics.chem-ph↗