Machine learning meets volcano plots: Computational discovery of cross-coupling catalysts
Benjamin Meyer,Boodsarin Sawatlon,Stefan Heinen,Stefan Heinen,O. Anatole von Lilienfeld,O. Anatole von Lilienfeld,Clémence Corminboeuf +6 more
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The application of modern machine learning to challenges in atomistic simulation is gaining attraction and the potential for innovation in this area is being explored.Abstract:
The application of modern machine learning to challenges in atomistic simulation is gaining attraction. We present new machine learning models that can predict the energy of the oxidative addition process between a transition metal complex and a substrate for C–C cross-coupling reactions. In turn, this quantity can be used as a descriptor to estimate the activity of homogeneous catalysts using molecular volcano plots. The versatility of this approach is illustrated for vast libraries of organometallic catalysts based on Pt, Pd, Ni, Cu, Ag, and Au combined with 91 ligands. Out-of-sample machine learning predictions were made on a total of 18 062 compounds leading to 557 catalyst candidates falling into the ideal thermodynamic window. This number was further refined by searching for candidates with an estimated price lower than 10 US$ per mmol. The 37 catalyst finalists are dominated by palladium phosphine ligand combinations but also include the earth abundant transition metal (Cu) with less common ligands. Our results indicate that modern statistical learning techniques can be applied to the computational discovery of readily available and promising catalyst candidates.read more
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Machine learning and semi-empirical calculations: a synergistic approach to rapid, accurate, and mechanism-based reaction barrier prediction
TL;DR: In this paper , a synergistic semi-empirical quantum mechanical (SQM) and machine learning (ML) approach was proposed to predict DFT-quality reaction barriers in minutes.
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Machine Learning Prediction of Structure‐Performance Relationship in Organic Synthesis
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Volcano Plots of Reaction Yields in Cross-Coupling Catalysis.
TL;DR: This work combines the steric and electronic effects of phosphine ligands into a descriptor, %Vbur (min) - 3·HOMO-LUMO gap (eV) where %VBur (Min) is the minimum percent buried volume and the Boltzmann averaged gap is used.
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Generalizing Performance Equations in Heterogeneous Catalysis from Hybrid Data and Statistical Learning
Xiaoyu Li,Murat Altunkaynak[1] Umut Gül[2] Latife Canatan[3] İbrahim Daşkın[4] Gürsel Kütük[5] +1 more
TL;DR: In this article , the authors have merged experimental activity and selectivity presented as a function of chemical descriptors from Density Functional Theory for the catalyzed hydrodehalogenation of CH2X2 (for X = Br, Cl) leading to three main products.
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Machine Learning Models Predict Calculation Outcomes with the Transferability Necessary for Computational Catalysis
TL;DR: In this article , a convolutional neural network is used to monitor geometry optimizations on the fly, and exploit its good performance and transferability in identifying geometry optimization failures for catalyst design.
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