Machine Learning for Transition-Metal-Based Hydrogen Generation Electrocatalysts

Descriptor selection for predicting interfacial thermal resistance by machine learning methods

(2021) Xiaojuan Tian et al.   Scientific Reports

Predicted Optimal Bifunctional Electrocatalysts for the Hydrogen Evolution Reaction and the Oxygen Evolution Reaction Using Chalcogenide Heterostructures Based on Machine Learning Analysis of in Silico Quantum Mechanics Based High Throughput Screening

(2020) Lei Ge et al.   Journal of Physical Chemistry Letters

Accelerating Atomic Catalyst Discovery by Theoretical Calculations‐Machine Learning Strategy

(2020) Mingzi Sun 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

Amorphous Catalysis: Machine Learning Driven High-Throughput Screening of Superior Active Site for Hydrogen Evolution Reaction

(2020) Jiawei Zhang et al.   Journal of Physical Chemistry C

High Throughput Screening of Hydrogen Evolution Reaction Catalysts in MXenes Materials

(2020) Jingnan Zheng et al.   Journal of Physical Chemistry C

Coupling a Crystal Graph Multilayer Descriptor to Active Learning for Rapid Discovery of 2D Ferromagnetic Semiconductors/Half‐Metals/Metals

(2020) Shuaihua Lu et al.   ADVANCED MATERIALS

Designing AI‐aided analysis and prediction models for nonprecious metal electrocatalyst‐based proton exchange membrane fuel cells

(2020) Rui Ding et al.   ANGEWANDTE CHEMIE-INTERNATIONAL EDITION

Recent advances in knowledge discovery for heterogeneous catalysis using machine learning

(2020) M. Erdem Günay et al.   CATALYSIS REVIEWS-SCIENCE AND ENGINEERING

Classification of platinum nanoparticle catalysts using machine learning

(2020) A. J. Parker et al.   JOURNAL OF APPLIED PHYSICS

Descriptors for Hydrogen Evolution on Single Atom Catalysts in Nitrogen-Doped Graphene

(2020) Victor Fung et al.   Journal of Physical Chemistry C

Established and Emerging Computational Tools to Study Homogeneous Catalysis—From Quantum Mechanics to Machine Learning

(2020) Ignacio Funes-Ardoiz et al.   Chem

Understanding important features of deep learning models for segmentation of high-resolution transmission electron microscopy images

(2020) James P. Horwath et al.   npj Computational Materials

Gesture recognition using a bioinspired learning architecture that integrates visual data with somatosensory data from stretchable sensors

(2020) Ming Wang et al.   Nature Electronics

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

(2019) Chi Chen et al.   CHEMISTRY OF MATERIALS

Catalysis-Hub.org, an open electronic structure database for surface reactions

(2019) Kirsten T. Winther et al.   Scientific Data

Lattice Convolutional Neural Network Modeling of Adsorbate Coverage Effects

(2019) Jonathan Lym et al.   Journal of Physical Chemistry C

Deep materials informatics: Applications of deep learning in materials science

(2019) Ankit Agrawal et al.   MRS Communications

DScribe: Library of descriptors for machine learning in materials science

(2019) Lauri Himanen et al.   COMPUTER PHYSICS COMMUNICATIONS

Machine Learning for Catalysis Informatics: Recent Applications and Prospects

(2019) Takashi Toyao et al.   ACS Catalysis

Machine learning for heterogeneous catalyst design and discovery

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

A robust model structure selection method for small sample size and multiple datasets problems

(2018) Yuanlin Gu et al.   INFORMATION SCIENCES

SchNet – A deep learning architecture for molecules and materials

(2018) K. T. Schütt et al.   JOURNAL OF CHEMICAL PHYSICS

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

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

(2018) Tian Xie et al.   PHYSICAL REVIEW LETTERS

A strategy to apply machine learning to small datasets in materials science

(2018) Ying Zhang et al.   npj Computational Materials

Machine learning hydrogen adsorption on nanoclusters through structural descriptors

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

Machine learning for molecular and materials science

(2018) Keith T. Butler et al.   NATURE

The Rise of Catalyst Informatics: Towards Catalyst Genomics

(2018) Keisuke Takahashi et al.   ChemCatChem

ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition

(2018) Dipendra Jha et al.   Scientific Reports

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

Molecular graph convolutions: moving beyond fingerprints

(2016) Steven Kearnes et al.   JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN

Automated Discovery and Construction of Surface Phase Diagrams Using Machine Learning

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

Machine-learning-assisted materials discovery using failed experiments

(2016) Paul Raccuglia et al.   NATURE

ImageNet Large Scale Visual Recognition Challenge

(2015) Olga Russakovsky et al.   INTERNATIONAL JOURNAL OF COMPUTER VISION

PubChem Substance and Compound databases

(2015) Sunghwan Kim et al.   NUCLEIC ACIDS RESEARCH

Big Data of Materials Science: Critical Role of the Descriptor

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

Machine learning: Trends, perspectives, and prospects

(2015) M. I. Jordan et al.   SCIENCE

The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies

(2015) Scott Kirklin et al.   npj Computational Materials

High-Throughput Bubble Screening Method for Combinatorial Discovery of Electrocatalysts for Water Splitting

(2013) Chengxiang Xiang et al.   ACS Combinatorial Science

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

(2013) Anubhav Jain et al.   APL Materials

Development of an O2-Sensitive Fluorescence-Quenching Assay for the Combinatorial Discovery of Electrocatalysts for Water Oxidation

(2012) James B. Gerken et al.   ANGEWANDTE CHEMIE-INTERNATIONAL EDITION

Electronic Structure Engineering in Heterogeneous Catalysis: Identifying Novel Alloy Catalysts Based on Rapid Screening for Materials with Desired Electronic Properties

(2012) Hongliang Xin et al.   TOPICS IN CATALYSIS

Atom-centered symmetry functions for constructing high-dimensional neural network potentials

(2011) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

Simple method for the rapid simultaneous screening of photocatalytic activity over multiple positions of self-cleaning films

(2009) Andreas Kafizas et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS