Machine learning for heterogeneous catalyst design and discovery

Accelerating Chemical Discovery with Machine Learning: Simulated Evolution of Spin Crossover Complexes with an Artificial Neural Network

(2018) Jon Paul Janet et al.   Journal of Physical Chemistry Letters

A Linear Scaling Relation for CO Oxidation on CeO2-Supported Pd

(2018) Jin-Xun Liu et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY

Chemical Pressure-Driven Enhancement of the Hydrogen Evolving Activity of Ni2P from Nonmetal Surface Doping Interpreted via Machine Learning

(2018) Robert B. Wexler et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY

Metastable Structures in Cluster Catalysis from First-Principles: Structural Ensemble in Reaction Conditions and Metastability Triggered Reactivity

(2018) Geng Sun et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY

The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics

(2018) Kun Yao et al.   Chemical Science

Accelerating the discovery of materials for clean energy in the era of smart automation

(2018) Daniel P. Tabor et al.   Nature Reviews Materials

Thermochemistry of gas-phase and surface species via LASSO-assisted subgraph selection

(2018) Geun Ho Gu et al.   Reaction Chemistry & Engineering

Determination of bimetallic architectures in nanometer-scale catalysts by combining molecular dynamics simulations with x-ray absorption spectroscopy

(2017) Janis Timoshenko et al.   JOURNAL OF CHEMICAL PHYSICS

Nudged elastic band calculations accelerated with Gaussian process regression

(2017) Olli-Pekka Koistinen et al.   JOURNAL OF CHEMICAL PHYSICS

Reliable and efficient reaction path and transition state finding for surface reactions with the growing string method

(2017) Mina Jafari et al.   JOURNAL OF COMPUTATIONAL CHEMISTRY

Resolving Transition Metal Chemical Space: Feature Selection for Machine Learning and Structure–Property Relationships

(2017) Jon Paul Janet et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Modeling Segregation on AuPd(111) Surfaces with Density Functional Theory and Monte Carlo Simulations

(2017) Jacob R. Boes et al.   Journal of Physical Chemistry C

Predicting Catalytic Activity of Nanoparticles by a DFT-Aided Machine-Learning Algorithm

(2017) Ryosuke Jinnouchi et al.   Journal of Physical Chemistry Letters

Supervised Machine-Learning-Based Determination of Three-Dimensional Structure of Metallic Nanoparticles

(2017) Janis Timoshenko et al.   Journal of Physical Chemistry Letters

The atomic simulation environment—a Python library for working with atoms

(2017) Ask Hjorth Larsen et al.   JOURNAL OF PHYSICS-CONDENSED MATTER

Neural network predictions of oxygen interactions on a dynamic Pd surface

(2017) Jacob R. Boes et al.   MOLECULAR SIMULATION

Mastering the game of Go without human knowledge

(2017) David Silver et al.   NATURE

Learning physical descriptors for materials science by compressed sensing

(2017) Luca M Ghiringhelli et al.   NEW JOURNAL OF PHYSICS

Uncovering structure-property relationships of materials by subgroup discovery

(2017) Bryan R Goldsmith et al.   NEW JOURNAL OF PHYSICS

Addressing uncertainty in atomistic machine learning

(2017) Andrew A. Peterson et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

Orbitalwise Coordination Number for Predicting Adsorption Properties of Metal Nanocatalysts

(2017) Xianfeng Ma et al.   PHYSICAL REVIEW LETTERS

Structural and Electronic Descriptors of Catalytic Activity of Graphene-Based Materials: First-Principles Theoretical Analysis

(2017) S. Sinthika et al.   Small

To address surface reaction network complexity using scaling relations machine learning and DFT calculations

(2017) Zachary W. Ulissi et al.   Nature Communications

Quantum-chemical insights from deep tensor neural networks

(2017) Kristof T. Schütt et al.   Nature Communications

Bypassing the Kohn-Sham equations with machine learning

(2017) Felix Brockherde et al.   Nature Communications

Scaling-Relation-Based Analysis of Bifunctional Catalysis: The Case for Homogeneous Bimetallic Alloys

(2017) Mie Andersen et al.   ACS Catalysis

The Degree of Rate Control: A Powerful Tool for Catalysis Research

(2017) Charles T. Campbell   ACS Catalysis

Beyond Ordered Materials: Understanding Catalytic Sites on Amorphous Solids

(2017) Bryan R. Goldsmith et al.   ACS Catalysis

Fluxionality of Catalytic Clusters: When It Matters and How to Address It

(2017) Huanchen Zhai et al.   ACS Catalysis

Machine-Learning Methods Enable Exhaustive Searches for Active Bimetallic Facets and Reveal Active Site Motifs for CO2 Reduction

(2017) Zachary W. Ulissi et al.   ACS Catalysis

Artificial neural network analysis of the catalytic efficiency of platinum nanoparticles

(2017) Michael Fernandez et al.   RSC Advances

High-throughput screening of bimetallic catalysts enabled by machine learning

(2017) Zheng Li et al.   Journal of Materials Chemistry A

Machine-learned and codified synthesis parameters of oxide materials

(2017) Edward Kim et al.   Scientific Data

Machine learning of accurate energy-conserving molecular force fields

(2017) Stefan Chmiela et al.   Science Advances

Towards efficient data exchange and sharing for big-data driven materials science: metadata and data formats

(2017) Luca M. Ghiringhelli et al.   npj Computational Materials

Machine learning in materials informatics: recent applications and prospects

(2017) Rampi Ramprasad et al.   npj Computational Materials

Discovering charge density functionals and structure-property relationships with PROPhet: A general framework for coupling machine learning and first-principles methods

(2017) Brian Kolb et al.   Scientific Reports

Data science: Accelerating innovation and discovery in chemical engineering

(2016) David A. C. Beck et al.   AICHE JOURNAL

Charge Transport over the Defective CeO2(111) Surface

(2016) Ya-Qiong Su et al.   CHEMISTRY OF MATERIALS

Amp : A modular approach to machine learning in atomistic simulations

(2016) Alireza Khorshidi et al.   COMPUTER PHYSICS COMMUNICATIONS

Neural network and ReaxFF comparison for Au properties

(2016) Jacob R. Boes et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Materials Data Infrastructure: A Case Study of the Citrination Platform to Examine Data Import, Storage, and Access

(2016) Jordan O’Mara et al.   JOM

ChemDataExtractor: A Toolkit for Automated Extraction of Chemical Information from the Scientific Literature

(2016) Matthew C. Swain et al.   Journal of Chemical Information and Modeling

Acceleration of saddle-point searches with machine learning

(2016) Andrew A. Peterson   JOURNAL OF CHEMICAL PHYSICS

Ensemble-Average Representation of Pt Clusters in Conditions of Catalysis Accessed through GPU Accelerated Deep Neural Network Fitting Global Optimization

(2016) Huanchen Zhai et al.   Journal of Chemical Theory and Computation

Machine Learning Force Fields: Construction, Validation, and Outlook

(2016) V. Botu et al.   Journal of Physical Chemistry C

Automated Discovery and Construction of Surface Phase Diagrams Using Machine Learning

(2016) Zachary W. Ulissi et al.   Journal of Physical Chemistry Letters

Moment Tensor Potentials: A Class of Systematically Improvable Interatomic Potentials

(2016) Alexander V. Shapeev   MULTISCALE MODELING & SIMULATION

Neural network molecular dynamics simulations of solid–liquid interfaces: water at low-index copper surfaces

(2016) Suresh Kondati Natarajan et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

Theoretical Heterogeneous Catalysis: Scaling Relationships and Computational Catalyst Design

(2016) Jeffrey Greeley   Annual Review of Chemical and Biomolecular Engineering

Nanoscale Engineering of Efficient Oxygen Reduction Electrocatalysts by Tailoring the Local Chemical Environment of Pt Surface Sites

(2016) Tim Van Cleve et al.   ACS Catalysis

Phenomena Affecting Catalytic Reactions at Solid–Liquid Interfaces

(2016) Carsten Sievers et al.   ACS Catalysis

Methane Activation at the Pd/CeO2 Interface

(2016) Thomas P. Senftle et al.   ACS Catalysis

Nickel–Gallium-Catalyzed Electrochemical Reduction of CO2 to Highly Reduced Products at Low Overpotentials

(2016) Daniel A. Torelli et al.   ACS Catalysis

Predicting the performance of oxidation catalysts using descriptor models

(2016) Neetika Madaan et al.   Catalysis Science & Technology

Machine learning scheme for fast extraction of chemically interpretable interatomic potentials

(2016) Pavel E. Dolgirev et al.   AIP Advances

Grand canonical molecular dynamics simulations of Cu–Au nanoalloys in thermal equilibrium using reactive ANN potentials

(2015) Nongnuch Artrith et al.   COMPUTATIONAL MATERIALS SCIENCE

Understanding machine-learned density functionals

(2015) Li Li et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Machine learning for quantum mechanics in a nutshell

(2015) Matthias Rupp   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Single atom catalysts on amorphous supports: A quenched disorder perspective

(2015) Baron Peters et al.   JOURNAL OF CHEMICAL PHYSICS

Single-ended transition state finding with the growing string method

(2015) Paul M. Zimmerman   JOURNAL OF COMPUTATIONAL CHEMISTRY

Global minimization of gold clusters by combining neural network potentials and the basin-hopping method

(2015) Runhai Ouyang et al.   Nanoscale

Big–deep–smart data in imaging for guiding materials design

(2015) Sergei V. Kalinin et al.   NATURE MATERIALS

Big Data of Materials Science: Critical Role of the Descriptor

(2015) Luca M. Ghiringhelli et al.   PHYSICAL REVIEW LETTERS

Finding optimal surface sites on heterogeneous catalysts by counting nearest neighbors

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

Knowledge extraction for water gas shift reaction over noble metal catalysts from publications in the literature between 2002 and 2012

(2014) Çağla Odabaşı et al.   INTERNATIONAL JOURNAL OF HYDROGEN ENERGY

An automated method to find transition states using chemical dynamics simulations

(2014) Emilio Martínez-Núñez   JOURNAL OF COMPUTATIONAL CHEMISTRY

CO- and NO-Induced Disintegration and Redispersion of Three-Way Catalysts Rhodium, Palladium, and Platinum: An ab Initio Thermodynamics Study

(2014) Bryan R. Goldsmith et al.   Journal of Physical Chemistry C

Navigating molecular space for reaction mechanisms: an efficient, automated procedure

(2014) Paul M. Zimmerman   MOLECULAR SIMULATION

Understanding the Composition and Activity of Electrocatalytic Nanoalloys in Aqueous Solvents: A Combination of DFT and Accurate Neural Network Potentials

(2014) Nongnuch Artrith et al.   NANO LETTERS

Heterogeneous catalyst discovery using 21st century tools: a tutorial

(2014) Erik-Jan Ras et al.   RSC Advances

Pattern Recognition Correlating Materials Properties of the Elements to Their Kinetics for the Hydrogen Evolution Reaction

(2013) Kevin C. Leonard et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY

On representing chemical environments

(2013) Albert P. Bartók et al.   PHYSICAL REVIEW B

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

(2013) Anubhav Jain et al.   APL Materials

Review of Pt-Based Bimetallic Catalysis: From Model Surfaces to Supported Catalysts

(2012) Weiting Yu et al.   CHEMICAL REVIEWS

Bimetallic catalysts for upgrading of biomass to fuels and chemicals

(2012) David Martin Alonso et al.   CHEMICAL SOCIETY REVIEWS

Activity Descriptors for CO2 Electroreduction to Methane on Transition-Metal Catalysts

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

Force reversed method for locating transition states

(2012) Keju Sun et al.   THEORETICAL CHEMISTRY ACCOUNTS

New tricks by very old dogs: predicting the catalytic hydrogenation of HMF derivatives using Slater-type orbitals

(2012) Erik-Jan Ras et al.   Catalysis Science & Technology

Predictive modeling in homogeneous catalysis: a tutorial

(2010) Ana G. Maldonado et al.   CHEMICAL SOCIETY REVIEWS

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

An overview on subgroup discovery: foundations and applications

(2010) Franciso Herrera et al.   KNOWLEDGE AND INFORMATION SYSTEMS

Understanding Catalytic Biomass Conversion Through Data Mining

(2010) Erik-Jan Ras et al.   TOPICS IN CATALYSIS

Towards the computational design of solid catalysts

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

Data mining in catalysis: Separating knowledge from garbage

(2008) G ROTHENBERG   CATALYSIS TODAY

Challenges in Modeling Materials Properties Without Experimental Input

(2008) E. A. Carter   SCIENCE