Open Challenges in Developing Generalizable Large-Scale Machine-Learning Models for Catalyst Discovery

Transfer learning using attentions across atomic systems with graph neural networks (TAAG)

(2022) Adeesh Kolluru et al.   JOURNAL OF CHEMICAL PHYSICS

Open Catalyst 2020 (OC20) Dataset and Community Challenges

(2021) Lowik Chanussot et al.   ACS Catalysis

Adsorption Behavior of Congo Red onto Barium-Doped ZnO Nanoparticles: Correlation between Experimental Results and DFT Calculations

(2021) Seyfeddine Rahali et al.   LANGMUIR

Highly accurate protein structure prediction with AlphaFold

(2021) John Jumper et al.   NATURE

Machine learning in solid heterogeneous catalysis: Recent developments, challenges and perspectives

(2021) Yani Guan et al.   CHEMICAL ENGINEERING SCIENCE

AtomSets as a hierarchical transfer learning framework for small and large materials datasets

(2021) Chi Chen et al.   npj Computational Materials

Grand challenges on accelerating discovery in catalysis

(2021) Alessandra Toniato et al.   CATALYSIS TODAY

Realizing the data-driven, computational discovery of metal-organic framework catalysts

(2021) Andrew S Rosen et al.   Current Opinion in Chemical Engineering

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

Evaluating Scalable Uncertainty Estimation Methods for Deep Learning-Based Molecular Property Prediction

(2020) Gabriele Scalia et al.   Journal of Chemical Information and Modeling

The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules

(2020) Justin S. Smith et al.   Scientific Data

On-the-fly active learning of interpretable Bayesian force fields for atomistic rare events

(2020) Jonathan Vandermause et al.   npj Computational Materials

OrbNet: Deep learning for quantum chemistry using symmetry-adapted atomic-orbital features

(2020) Zhuoran Qiao et al.   JOURNAL OF CHEMICAL PHYSICS

A Machine Learning Model on Simple Features for CO2 Reduction Electrocatalysts

(2020) An Chen et al.   Journal of Physical Chemistry C

Differentiable Optimization for the Prediction of Ground State Structures (DOGSS)

(2020) Junwoong Yoon et al.   PHYSICAL REVIEW LETTERS

Beyond Scaling Relations for the Description of Catalytic Materials

(2019) Mie Andersen et al.   ACS Catalysis

GFN2-xTB—An Accurate and Broadly Parametrized Self-Consistent Tight-Binding Quantum Chemical Method with Multipole Electrostatics and Density-Dependent Dispersion Contributions

(2019) Christoph Bannwarth et al.   Journal of Chemical Theory and Computation

Low-Scaling Algorithm for Nudged Elastic Band Calculations Using a Surrogate Machine Learning Model

(2019) José A. Garrido Torres et al.   PHYSICAL REVIEW 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

Phase Transitions of Hybrid Perovskites Simulated by Machine-Learning Force Fields Trained on the Fly with Bayesian Inference

(2019) Ryosuke Jinnouchi et al.   PHYSICAL REVIEW LETTERS

Toward a Design of Active Oxygen Evolution Catalysts: Insights from Automated Density Functional Theory Calculations and Machine Learning

(2019) Seoin Back et al.   ACS Catalysis

Data‐Driven Materials Science: Status, Challenges, and Perspectives

(2019) Lauri Himanen et al.   Advanced Science

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

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

Synthesis of 2-Acylindoles via Ag- and Cu-Catalyzed anti-Michael Hydroamination of β-(2-Aminophenyl)-α,β-ynones: Experimental Results and DFT Calculations

(2018) Navnath D. Rode et al.   JOURNAL OF ORGANIC CHEMISTRY

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

(2018) Juhwan Noh et al.   Chemical Science

Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments

(2018) Fang Ren et al.   Science Advances

Dissolution and degradation of nuclear grade cationic exchange resin by Fenton oxidation combining experimental results and DFT calculations

(2018) Lejin Xu et al.   CHEMICAL ENGINEERING JOURNAL

Orbitalwise Coordination Number for Predicting Adsorption Properties of Metal Nanocatalysts

(2017) Xianfeng Ma et al.   PHYSICAL REVIEW LETTERS

ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost

(2017) J. S. Smith et al.   Chemical Science

High-throughput screening of bimetallic catalysts enabled by machine learning

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

Adsorption-energy-based activity descriptors for electrocatalysts in energy storage applications

(2017) Youwei Wang et al.   National Science Review

Machine learning of accurate energy-conserving molecular force fields

(2017) Stefan Chmiela et al.   Science Advances

Acceleration of saddle-point searches with machine learning

(2016) Andrew A. Peterson   JOURNAL OF CHEMICAL PHYSICS

Gaussian approximation potentials: A brief tutorial introduction

(2015) Albert P. Bartók et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Design of a polypyrrole MIP-SAW sensor for selective detection of flumequine in aqueous media. Correlation between experimental results and DFT calculations

(2015) N. Ktari et al.   RSC Advances

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

(2015) Scott Kirklin et al.   npj Computational Materials

Quantum chemistry structures and properties of 134 kilo molecules

(2014) Raghunathan Ramakrishnan et al.   Scientific Data

A global potential energy surface for the H2 + OH ↔ H2O + H reaction using neural networks

(2013) Jun Chen et al.   JOURNAL OF CHEMICAL PHYSICS

Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons

(2010) Albert P. Bartók et al.   PHYSICAL REVIEW LETTERS