The Rise of Catalyst Informatics: Towards Catalyst Genomics

Catalysis research is on the verge of experiencing a paradigm shift regarding how catalysts are designed and characterized due to the rise of catalyst informatics. The details of catalyst informatics are reviewed where the following three key concepts are proposed: catalyst data, catalyst data to catalyst design via data science, and catalyst platform. Additionally, progress and opportunities within catalyst informatics are explored and introduced. If the field of catalyst informatics grows in the appropriate manner, the methods and approaches taken for catalyst design would be fundamentally altered, leading towards great advancement within catalysis research.

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

[2]  Kristin A. Persson,et al.  Commentary: The Materials Project: A materials genome approach to accelerating materials innovation , 2013 .

[3]  Forest Rohwer,et al.  FastGroupII: A web-based bioinformatics platform for analyses of large 16S rDNA libraries , 2006, BMC Bioinformatics.

[4]  Tadashi Hattori,et al.  Neural network as a tool for catalyst development , 1995 .

[5]  Anubhav Jain,et al.  A high-throughput infrastructure for density functional theory calculations , 2011 .

[6]  Itsuki Miyazato,et al.  Rapid estimation of activation energy in heterogeneous catalytic reactions via machine learning , 2018, J. Comput. Chem..

[7]  H. Grönbeck,et al.  Methane Oxidation over PdO(101) Revealed by First-Principles Kinetic Modeling. , 2015, Journal of the American Chemical Society.

[8]  A. Goesmann,et al.  A review of bioinformatics platforms for comparative genomics. Recent developments of the EDGAR 2.0 platform and its utility for taxonomic and phylogenetic studies. , 2017, Journal of biotechnology.

[9]  Alexis S. Ivanov,et al.  Bioinformatics Platform Development , 2006 .

[10]  Jens K Nørskov,et al.  The catalyst genome. , 2013, Angewandte Chemie.

[11]  A. McCallum,et al.  Materials Synthesis Insights from Scientific Literature via Text Extraction and Machine Learning , 2017 .

[12]  Michael Shevlin,et al.  Practical High-Throughput Experimentation for Chemists , 2017, ACS medicinal chemistry letters.

[13]  Rama Vasudevan,et al.  Deep Learning of Atomically Resolved Scanning Transmission Electron Microscopy Images: Chemical Identification and Tracking Local Transformations. , 2017, ACS nano.

[14]  J. Nørskov,et al.  Towards the computational design of solid catalysts. , 2009, Nature chemistry.

[15]  A. Choudhary,et al.  Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science , 2016 .

[16]  Ichigaku Takigawa,et al.  Toward Effective Utilization of Methane: Machine Learning Prediction of Adsorption Energies on Metal Alloys , 2018 .

[17]  Anubhav Jain,et al.  Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis , 2012 .

[18]  Matthew Horton,et al.  Atomate: A high-level interface to generate, execute, and analyze computational materials science workflows , 2017 .

[19]  J. Nørskov,et al.  CatApp: a web application for surface chemistry and heterogeneous catalysis. , 2012, Angewandte Chemie.

[20]  J. Nørskov,et al.  Universal transition state scaling relations for (de)hydrogenation over transition metals. , 2011, Physical chemistry chemical physics : PCCP.

[21]  Anubhav Jain,et al.  Finding Nature’s Missing Ternary Oxide Compounds Using Machine Learning and Density Functional Theory , 2010 .

[22]  H. Hiller In: Ullmann''''s Encyclopedia of Industrial Chemistry , 1989 .

[23]  Itsuki Miyazato,et al.  Unveiling Hidden Catalysts for the Oxidative Coupling of Methane based on Combining Machine Learning with Literature Data , 2018, ChemCatChem.

[24]  B. Hoff,et al.  Is literature data useful for identifying enzyme catalysts for new substrates? A case study on reduction of 1-aryl-2-alkanoates. , 2017, Bioorganic chemistry.

[25]  Martin Holena,et al.  Statistical Analysis of Past Catalytic Data on Oxidative Methane Coupling for New Insights into the Composition of High‐Performance Catalysts , 2011 .

[26]  Yuzuru Tanaka,et al.  Materials informatics: a journey towards material design and synthesis. , 2016, Dalton transactions.

[27]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[28]  Paul Raccuglia,et al.  Machine-learning-assisted materials discovery using failed experiments , 2016, Nature.

[29]  Satoshi Maeda,et al.  A scaled hypersphere search method for the topography of reaction pathways on the potential energy surface , 2004 .

[30]  W. H. Weinberg,et al.  High-Throughput Synthesis and Screening of Combinatorial Heterogeneous Catalyst Libraries. , 1999, Angewandte Chemie.

[31]  Daniel W. Davies,et al.  Machine learning for molecular and materials science , 2018, Nature.

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

[34]  Raynald Gauvin,et al.  Application of machine learning methods for the prediction of crystal system of cathode materials in lithium-ion batteries , 2016 .

[35]  Itsuki Miyazato,et al.  Redesigning the Materials and Catalysts Database Construction Process Using Ontologies , 2018, J. Chem. Inf. Model..

[36]  M. Hedhili,et al.  A high-throughput reactor system for optimization of Mo–V–Nb mixed oxide catalyst composition in ethane ODH , 2015 .

[37]  C. Snively,et al.  High-throughput heterogeneous catalytic science. , 2005, Chemistry.