Machine learned features from density of states for accurate adsorption energy prediction

A Critical Review of Machine Learning of Energy Materials

(2020) Chi Chen et al.   Advanced Energy Materials

Progress in Computational and Machine-Learning Methods for Heterogeneous Small-Molecule Activation

(2020) Geun Ho Gu et al.   ADVANCED MATERIALS

Accelerated discovery of CO2 electrocatalysts using active machine learning

(2020) Miao Zhong et al.   NATURE

An Adaptive Machine Learning Strategy for Accelerating Discovery of Perovskite Electrocatalysts

(2020) Zheng Li et al.   ACS Catalysis

Prediction by Convolutional Neural Networks of CO 2 /N 2 Selectivity in Porous Carbons from N 2 Adsorption Isotherm at 77 K

(2020) Song Wang et al.   ANGEWANDTE CHEMIE-INTERNATIONAL EDITION

Solving the electronic structure problem with machine learning

(2019) Anand Chandrasekaran et al.   npj Computational Materials

Beyond Scaling Relations for the Description of Catalytic Materials

(2019) Mie Andersen et al.   ACS Catalysis

A complete catalogue of high-quality topological materials

(2019) M. G. Vergniory et al.   NATURE

Machine Learning for Computational Heterogeneous Catalysis

(2019) Philomena Schlexer Lamoureux et al.   ChemCatChem

Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals

(2019) Chi Chen et al.   CHEMISTRY OF MATERIALS

Predicting Adsorption Properties of Catalytic Descriptors on Bimetallic Nanoalloys with Site-Specific Precision

(2019) Tej S. Choksi et al.   Journal of Physical Chemistry Letters

High-throughput calculations of catalytic properties of bimetallic alloy surfaces

(2019) Osman Mamun et al.   Scientific Data

Convolutional Neural Network of Atomic Surface Structures To Predict Binding Energies for High-Throughput Screening of Catalysts

(2019) Seoin Back et al.   Journal of Physical Chemistry Letters

Identifying Active Sites for CO2 Reduction on Dealloyed Gold Surfaces by Combining Machine Learning with Multiscale Simulations

(2019) Yalu Chen et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY

Unfolding adsorption on metal nanoparticles: Connecting stability with catalysis

(2019) James Dean et al.   Science Advances

Statistical learning goes beyond the d-band model providing the thermochemistry of adsorbates on transition metals

(2019) Rodrigo García-Muelas et al.   Nature Communications

Theory-guided design of catalytic materials using scaling relationships and reactivity descriptors

(2019) Zhi-Jian Zhao et al.   Nature Reviews Materials

Machine learning for heterogeneous catalyst design and discovery

(2018) Bryan R. Goldsmith et al.   AICHE JOURNAL

Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

(2018) Tian Xie et al.   PHYSICAL REVIEW LETTERS

Active learning with non-ab initio input features toward efficient CO2 reduction catalysts

(2018) Juhwan Noh et al.   Chemical Science

Machine learning hydrogen adsorption on nanoclusters through structural descriptors

(2018) Marc O. J. Jäger et al.   npj Computational Materials

Inverse molecular design using machine learning: Generative models for matter engineering

(2018) Benjamin Sanchez-Lengeling et al.   SCIENCE

High-throughput computational screening of layered and two-dimensional materials

(2018) Xu Zhang et al.   Wiley Interdisciplinary Reviews-Computational Molecular Science

Machine-Learning Prediction of CO Adsorption in Thiolated, Ag-Alloyed Au Nanoclusters

(2018) Gihan Panapitiya et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY

Defect Genome of Cubic Perovskites for Fuel Cell Applications

(2017) Janakiraman Balachandran et al.   Journal of Physical Chemistry C

General Structure–Reactivity Relationship for Oxygen on Transition-Metal Oxides

(2017) Victor Fung et al.   Journal of Physical Chemistry Letters

Orbitalwise Coordination Number for Predicting Adsorption Properties of Metal Nanocatalysts

(2017) Xianfeng Ma et al.   PHYSICAL REVIEW LETTERS

Solar fuels photoanode materials discovery by integrating high-throughput theory and experiment

(2017) Qimin Yan et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

LOBSTER: A tool to extract chemical bonding from plane-wave based DFT

(2016) Stefan Maintz et al.   JOURNAL OF COMPUTATIONAL CHEMISTRY

Theory of surface chemistry and reactivity of reducible oxides

(2015) Rutger A. van Santen et al.   CATALYSIS TODAY

Constructing high-dimensional neural network potentials: A tutorial review

(2015) Jörg Behler   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Machine-Learning-Augmented Chemisorption Model for CO2 Electroreduction Catalyst Screening

(2015) Xianfeng Ma et al.   Journal of Physical Chemistry Letters

Introducing structural sensitivity into adsorption–energy scaling relations by means of coordination numbers

(2015) Federico Calle-Vallejo et al.   Nature Chemistry

Finding optimal surface sites on heterogeneous catalysts by counting nearest neighbors

(2015) F. Calle-Vallejo et al.   SCIENCE

Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)

(2013) James E. Saal et al.   JOM

The high-throughput highway to computational materials design

(2013) Stefano Curtarolo et al.   NATURE MATERIALS

Commentary: The Materials Project: A materials genome approach to accelerating materials innovation

(2013) Anubhav Jain et al.   APL Materials

The Active Phase of Palladium during Methane Oxidation

(2012) A. Hellman et al.   Journal of Physical Chemistry Letters

A high-throughput infrastructure for density functional theory calculations

(2011) Anubhav Jain et al.   COMPUTATIONAL MATERIALS SCIENCE

Density functional theory in surface chemistry and catalysis

(2011) J. K. Norskov et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

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) Hongliang Xin et al.   JOURNAL OF CHEMICAL PHYSICS

Towards the computational design of solid catalysts

(2009) J. K. Nørskov et al.   Nature Chemistry

Correlations in coverage-dependent atomic adsorption energies on Pd(111)

(2009) John R. Kitchin   PHYSICAL REVIEW B