Theory-Guided Machine Learning Finds Geometric Structure-Property Relationships for Chemisorption on Subsurface Alloys

[1]  Ichigaku Takigawa,et al.  Frontier Molecular Orbital Based Analysis of Solid–Adsorbate Interactions over Group 13 Metal Oxide Surfaces , 2020, The Journal of Physical Chemistry C.

[2]  N. López,et al.  Performance of Metal-Catalyzed Hydrodebromination of Dibromomethane Analyzed by Descriptors Derived from Statistical Learning , 2020 .

[3]  Takashi Kamachi,et al.  Machine Learning for Catalysis Informatics: Recent Applications and Prospects , 2020 .

[4]  Graeme Henkelman,et al.  Theory-guided design of catalytic materials using scaling relationships and reactivity descriptors , 2019, Nature Reviews Materials.

[5]  Núria López,et al.  Statistical learning goes beyond the d-band model providing the thermochemistry of adsorbates on transition metals , 2019, Nature Communications.

[6]  T. Bligaard,et al.  Machine Learning for Computational Heterogeneous Catalysis , 2019, ChemCatChem.

[7]  Seoin Back,et al.  Convolutional Neural Network of Atomic Surface structures to Predict Binding Energies For High-throughput Screening of Catalysts. , 2019, The journal of physical chemistry letters.

[8]  Efthimios Kaxiras,et al.  Machine Learning Prediction of H Adsorption Energies on Ag Alloys , 2019, J. Chem. Inf. Model..

[9]  N. López,et al.  Atomically Thin Metal Films on Foreign Substrates: From Lattice Mismatch to Electrocatalytic Activity , 2019, ACS Catalysis.

[10]  M. Scheffler,et al.  Beyond Scaling Relations for the Description of Catalytic Materials , 2019, ACS Catalysis.

[11]  Shubham Sharma,et al.  Face-centered tetragonal (FCT) Fe and Co alloys of Pt as catalysts for the oxygen reduction reaction (ORR): A DFT study. , 2019, The Journal of chemical physics.

[12]  Chandan Singh,et al.  Definitions, methods, and applications in interpretable machine learning , 2019, Proceedings of the National Academy of Sciences.

[13]  M. Scheffler,et al.  Simultaneous learning of several materials properties from incomplete databases with multi-task SISSO , 2019, Journal of Physics: Materials.

[14]  Arnab Bhattacharyya,et al.  Machine Learning Constrained with Dimensional Analysis and Scaling Laws: Simple, Transferable, and Interpretable Models of Materials from Small Datasets , 2018, Chemistry of Materials.

[15]  Cynthia Rudin,et al.  Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead , 2018, Nature Machine Intelligence.

[16]  Jordi Nin,et al.  Towards Global Explanations for Credit Risk Scoring , 2018, ArXiv.

[17]  A. V. van Duin,et al.  Discovery of Descriptors for Stable Monolayer Oxide Coatings through Machine Learning , 2018, ACS Applied Energy Materials.

[18]  Zheng Li,et al.  Toward artificial intelligence in catalysis , 2018, Nature Catalysis.

[19]  Zachary W. Ulissi,et al.  Active learning across intermetallics to guide discovery of electrocatalysts for CO2 reduction and H2 evolution , 2018, Nature Catalysis.

[20]  Michael J. Janik,et al.  Interaction trends between single metal atoms and oxide supports identified with density functional theory and statistical learning , 2018, Nature Catalysis.

[21]  Christopher J. Bartel,et al.  Machine learning for heterogeneous catalyst design and discovery , 2018 .

[22]  Vladan Stevanović,et al.  Physical descriptor for the Gibbs energy of inorganic crystalline solids and temperature-dependent materials chemistry , 2018, Nature Communications.

[23]  John R. Kitchin,et al.  Machine learning in catalysis , 2018, Nature Catalysis.

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

[25]  A. Rappe,et al.  Chemical Pressure-Driven Enhancement of the Hydrogen Evolving Activity of Ni2P from Nonmetal Surface Doping Interpreted via Machine Learning. , 2018, Journal of the American Chemical Society.

[26]  Christopher J. Bartel,et al.  New tolerance factor to predict the stability of perovskite oxides and halides , 2018, Science Advances.

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

[28]  Kyle A. Barlow,et al.  Flex ddG: Rosetta ensemble-based estimation of changes in protein-protein binding affinity upon mutation , 2017, bioRxiv.

[29]  U. Nair,et al.  Quantifying O3 Impacts in Urban Areas Due to Wildfires Using a Generalized Additive Model. , 2017, Environmental science & technology.

[30]  Stefano Curtarolo,et al.  SISSO: A compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates , 2017, Physical Review Materials.

[31]  Michael Walter,et al.  The atomic simulation environment-a Python library for working with atoms. , 2017, Journal of physics. Condensed matter : an Institute of Physics journal.

[32]  Xianfeng Ma,et al.  Orbitalwise Coordination Number for Predicting Adsorption Properties of Metal Nanocatalysts. , 2017, Physical review letters.

[33]  Nagiza F. Samatova,et al.  Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data , 2016, IEEE Transactions on Knowledge and Data Engineering.

[34]  M. Boley,et al.  Uncovering structure-property relationships of materials by subgroup discovery , 2016, 1612.04307.

[35]  D. Loffreda,et al.  Why conclusions from platinum model surfaces do not necessarily lead to enhanced nanoparticle catalysts for the oxygen reduction reaction , 2016, Chemical science.

[36]  James M. Rondinelli,et al.  Theory-Guided Machine Learning in Materials Science , 2016, Front. Mater..

[37]  Johannes Gehrke,et al.  Intelligible Models for HealthCare: Predicting Pneumonia Risk and Hospital 30-day Readmission , 2015, KDD.

[38]  J. Vybíral,et al.  Big data of materials science: critical role of the descriptor. , 2014, Physical review letters.

[39]  J. Nørskov,et al.  Fundamental Concepts in Heterogeneous Catalysis , 2014 .

[40]  J. M. García‐Lastra,et al.  Fast prediction of adsorption properties for platinum nanocatalysts with generalized coordination numbers. , 2014, Angewandte Chemie.

[41]  Florian Gossenberger,et al.  Halide adsorption on close-packed metal electrodes. , 2014, Physical chemistry chemical physics : PCCP.

[42]  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.

[43]  P. Sabatier.  La Catalyse en chimie organique , 2013 .

[44]  Christiaan Heij,et al.  Stability , 1973, Introduction to Mathematical Systems Theory.

[45]  Johannes Gehrke,et al.  Accurate intelligible models with pairwise interactions , 2013, KDD.

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

[47]  Bin Yu,et al.  Estimation Stability With Cross-Validation (ESCV) , 2013, 1303.3128.

[48]  Johannes Gehrke,et al.  Intelligible models for classification and regression , 2012, KDD.

[49]  H. Xin,et al.  Electronic Structure Engineering in Heterogeneous Catalysis: Identifying Novel Alloy Catalysts Based on Rapid Screening for Materials with Desired Electronic Properties , 2012, Topics in Catalysis.

[50]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[51]  H. Xin,et al.  Communications: Exceptions to the d-band model of chemisorption on metal surfaces: The dominant role of repulsion between adsorbate states and metal d-states. , 2010, The Journal of chemical physics.

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

[53]  H. Xin,et al.  Establishing Relationships Between the Geometric Structure and Chemical Reactivity of Alloy Catalysts Based on Their Measured Electronic Structure , 2010 .

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

[55]  M. Mavrikakis,et al.  Atomic and molecular adsorption on Pd(111) , 2005 .

[56]  J. Nørskov,et al.  Role of strain and ligand effects in the modification of the electronic and chemical properties of bimetallic surfaces. , 2004, Physical review letters.

[57]  J. G. Chen,et al.  Modification of the surface electronic and chemical properties of Pt(111) by subsurface 3d transition metals. , 2004, The Journal of chemical physics.

[58]  William D. Callister,et al.  Fundamentals of Materials Science and Engineering: An Integrated Approach, 2nd Edition , 2004 .

[59]  Manos Mavrikakis,et al.  Atomic and molecular adsorption on Rh(111) , 2002 .

[60]  G. Kresse,et al.  From ultrasoft pseudopotentials to the projector augmented-wave method , 1999 .

[61]  J. Nørskov,et al.  Effect of Strain on the Reactivity of Metal Surfaces , 1998 .

[62]  K. Burke,et al.  Generalized Gradient Approximation Made Simple [Phys. Rev. Lett. 77, 3865 (1996)] , 1997 .

[63]  Burke,et al.  Generalized Gradient Approximation Made Simple. , 1996, Physical review letters.

[64]  Kresse,et al.  Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. , 1996, Physical review. B, Condensed matter.

[65]  G. Kresse,et al.  Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set , 1996 .

[66]  J. Nørskov,et al.  CO chemisorption at metal surfaces and overlayers. , 1996, Physical review letters.

[67]  Hafner,et al.  Ab initio molecular dynamics for liquid metals. , 1995, Physical review. B, Condensed matter.

[68]  J. Nørskov,et al.  Why gold is the noblest of all the metals , 1995, Nature.

[69]  Blöchl,et al.  Projector augmented-wave method. , 1994, Physical review. B, Condensed matter.

[70]  Hafner,et al.  Ab initio molecular-dynamics simulation of the liquid-metal-amorphous-semiconductor transition in germanium. , 1994, Physical review. B, Condensed matter.

[71]  E. Shustorovich.  Chemisorption theory: in search of the elephant , 1988 .

[72]  T. Hastie,et al.  Generalized Additive Models , 2014 .

[73]  E. Shustorovich.  Activation barrier for adsorbate surface diffusion, heat of chemisorption, and adsorbate registry: theoretical interrelations , 1984 .

[74]  D. Pettifor.  Theory of the Heats of Formation of Transition-Metal Alloys , 1979 .

[75]  H. Monkhorst,et al.  SPECIAL POINTS FOR BRILLOUIN-ZONE INTEGRATIONS , 1976 .

[76]  M. Mavrikakis,et al.  Atomic and Molecular Adsorption on Ag(111) , 2018 .

[77]  S. Linic,et al.  Electrochemical Oxygen Reduction Reaction on Ag Nanoparticles of Different Shapes , 2016 .

[78]  H. Xin,et al.  Predictive Structure–Reactivity Models for Rapid Screening of Pt-Based Multimetallic Electrocatalysts for the Oxygen Reduction Reaction , 2012 .

[79]  M. Mavrikakis,et al.  Atomic and molecular adsorption on Ir(111) , 2004 .

[80]  B. Trout,et al.  Chemistry of sulfur oxides on transition metals. II. Thermodynamics of sulfur oxides on platinum(111) , 2004 .

[81]  R. F. Vines.  The Platinum Metals And Their Alloys , 1941 .

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