Chemistrees: Data-Driven Identification of Reaction Pathways via Machine Learning
TLDR
In this article, a decision tree-based approach is proposed to identify the predominant geometric features which correlate with trajectories that transition between two arbitrarily defined states, without the bias of human "chemical intuition".Abstract:
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.read more
Citations
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Thin film breakage in oil–in–water emulsions, a multidisciplinary study
TL;DR: In this article , a combined experimental and simulation study was conducted to investigate the thin film prior to its rupture in a coalescence event, and they identified a potential relationship between the disruption of the electrical double layer and the formation of nanocrystals with thin film breakage times and depletion forces.
Journal ArticleDOI
Thin film breakage in oil--in--water emulsions, a multidisciplinary study
TL;DR: In this paper, a combined experimental and simulation study was conducted to investigate the thin film prior to its rupture in a coalescence event, and they identified a potential relationship between the disruption of the electrical double layer and the formation of nanocrystals with thin film breakage times and depletion forces.
Journal ArticleDOI
Challenges for Kinetics Predictions via Neural Network Potentials: A Wilkinson’s Catalyst Case
TL;DR: In this paper , the applicability of Neural Network Potentials (NNP) to accelerate ab initio kinetic studies is investigated, and a novel theoretical study of ethylene hydrogenation with a transition metal complex inspired by Wilkinson's catalyst is reported.
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Computing Surface Reaction Rates by Adaptive Multilevel Splitting Combined with Machine Learning and Ab Initio Molecular Dynamics.
Thomas Pigeon,Gabriel Stoltz,Manuel Corral-Valero,Ani Anciaux-Sedrakian,Maxime Moreaud,Tony Lelièvre,Pascal Raybaud +6 more
TL;DR: In this article , an adaptive multilevel splitting (AMS) method is proposed to compute the rate constants for catalytic events occurring at the surface of a given material, which is based on a combination of the rare event sampling method and ab initio molecular dynamics.
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