Machine learning in catalysis

Catalysis is a complex, multidimensional and multiscale field of research. Machine learning is helping to build better models, understand catalysis research and generate new knowledge about catalysis.

[1]  R. Kondor,et al.  Gaussian approximation potentials: the accuracy of quantum mechanics, without the electrons. , 2009, Physical review letters.

[2]  Nongnuch Artrith,et al.  An implementation of artificial neural-network potentials for atomistic materials simulations: Performance for TiO2 , 2016 .

[3]  Emma Strubell,et al.  Machine-learned and codified synthesis parameters of oxide materials , 2017, Scientific Data.

[4]  Ryosuke Jinnouchi,et al.  Predicting Catalytic Activity of Nanoparticles by a DFT-Aided Machine-Learning Algorithm. , 2017, The journal of physical chemistry letters.

[5]  Jörg Behler,et al.  Accurate Neural Network Description of Surface Phonons in Reactive Gas–Surface Dynamics: N2 + Ru(0001) , 2017, The journal of physical chemistry letters.

[6]  Koji Tsuda,et al.  Machine-learning prediction of the d-band center for metals and bimetals , 2016 .

[7]  Michael Fernandez,et al.  Artificial neural network analysis of the catalytic efficiency of platinum nanoparticles , 2017 .

[8]  Alán Aspuru-Guzik,et al.  Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules , 2016, ACS central science.

[9]  Jörg Behler,et al.  Constructing high‐dimensional neural network potentials: A tutorial review , 2015 .

[10]  Alireza Khorshidi,et al.  Amp: A modular approach to machine learning in atomistic simulations , 2016, Comput. Phys. Commun..

[11]  Jihan Kim,et al.  Text Mining Metal-Organic Framework Papers , 2017, J. Chem. Inf. Model..

[12]  Alexie M. Kolpak,et al.  Discovering charge density functionals and structure-property relationships with PROPhet: A general framework for coupling machine learning and first-principles methods , 2017, Scientific Reports.

[13]  Luke E K Achenie,et al.  High-throughput screening of bimetallic catalysts enabled by machine learning , 2017 .

[14]  Claude Mirodatos,et al.  The development of descriptors for solids: teaching "catalytic intuition" to a computer. , 2004, Angewandte Chemie.

[15]  Heather J Kulik,et al.  Predicting electronic structure properties of transition metal complexes with neural networks† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c7sc01247k , 2017, Chemical science.

[16]  Nongnuch Artrith,et al.  Understanding the composition and activity of electrocatalytic nanoalloys in aqueous solvents: a combination of DFT and accurate neural network potentials. , 2014, Nano letters.

[17]  Christopher J. Shallue,et al.  Identifying Exoplanets with Deep Learning: A Five-planet Resonant Chain around Kepler-80 and an Eighth Planet around Kepler-90 , 2017, 1712.05044.

[18]  Zachary W. Ulissi,et al.  To address surface reaction network complexity using scaling relations machine learning and DFT calculations , 2017, Nature Communications.

[19]  Subhashini Venugopalan,et al.  Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. , 2016, JAMA.

[20]  José M. Serra,et al.  Integrating high-throughput characterization into combinatorial heterogeneous catalysis: unsupervised construction of quantitative structure/property relationship models , 2005 .

[21]  D. Lu,et al.  Supervised Machine-Learning-Based Determination of Three-Dimensional Structure of Metallic Nanoparticles. , 2017, The journal of physical chemistry letters.

[22]  Alexie M. Kolpak,et al.  Grand canonical molecular dynamics simulations of Cu–Au nanoalloys in thermal equilibrium using reactive ANN potentials , 2015 .

[23]  Li Li,et al.  Bypassing the Kohn-Sham equations with machine learning , 2016, Nature Communications.

[24]  John R. Kitchin,et al.  New solid-state table: estimating d-band characteristics for transition metal atoms , 2010 .

[25]  Michael Gastegger,et al.  Machine learning molecular dynamics for the simulation of infrared spectra† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c7sc02267k , 2017, Chemical science.

[26]  Zheng Li,et al.  Feature engineering of machine-learning chemisorption models for catalyst design , 2017 .