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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Quantum-mechanical transition-state model combined with machine learning provides catalyst design features for selective Cr olefin oligomerization.
Steven M. Maley,Doo-Hyun Kwon,Nick Rollins,Johnathan C. Stanley,Orson L. Sydora,Steven M. Bischof,Daniel H. Ess +6 more
TL;DR: This work reports the unique combination of an experimentally verified DFT-transition-state model with a random forest machine learning model in a campaign to design new molecular Cr phosphine imine (Cr(P,N) catalysts for selective ethylene oligomerization, specifically to increase 1-octene selectivity.
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Data-Driven Acceleration of the Coupled-Cluster Singles and Doubles Iterative Solver.
TL;DR: A novel approach to predict the converged coupled-Cluster singles and doubles (CCSD) amplitudes, thus the coupled-cluster wave function, is explored by using machine learning and electronic structure properties inherent to the MP2 level.
Journal ArticleDOI
Recent advances in knowledge discovery for heterogeneous catalysis using machine learning
M. Erdem Günay,Ramazan Yildirim +1 more
TL;DR: This communication aims to review the works involving knowledge discovery in catalysis using ML techniques to see patterns, develop models for prediction and deduce heuristic rules for the future.
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In silico high throughput screening of bimetallic and single atom alloys using machine learning and ab initio microkinetic modelling
TL;DR: In this paper, the authors used gradient boosting regression (GBR) to predict the binding energies of bimetallic alloys and single atom alloys (SAAs) using the features of metals that are readily available in the periodic table.
Journal ArticleDOI
Roadmap on Machine learning in electronic structure
Heather J. Kulik,Thomas Hammerschmidt,Jonathan Schmidt,Silvana Botti,Miguel A. L. Marques,Mario Boley,Matthias Scheffler,Milica Todorović,Patrick Rinke,Corey Oses,Andriy Smolyanyuk,Stefano Curtarolo,Alexandre Tkatchenko,Albert P. Bartók,Sergei Manzhos,Manabu Ihara,Tucker Carrington,Jörg Behler,Olexandr Isayev,Max Veit,Andrea Grisafi,Jigyasa Nigam,Michele Ceriotti,Kristof T. Schütt,Julia Westermayr,Michael Gastegger,Reinhard J. Maurer,Bhupalee Kalita,Kieron Burke,Ryotaro Nagai,Ryosuke Akashi,Osamu Sugino,Jan Hermann,Frank Noé,Sebastiano Pilati,Claudia Draxl,Martin Kubáň,Santiago Rigamonti,Markus Scheidgen,Marco Esters,David Hicks,Cormac Toher,Prasanna V. Balachandran,Isaac Tamblyn,Stephen Whitelam,Colin Bellinger,Luca M. Ghiringhelli +46 more
TL;DR: This Roadmap article, consisting of multiple contributions from experts across the field, discusses the use of machine learning in materials science, and share perspectives on current and future challenges in problems as diverse as the prediction of materials properties, the construction of force-fields, the development of exchange correlation functionals for density-functional theory, the solution of the many-body problem, and more.
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