Predicting Chemical Reaction Barriers with a Machine Learning Model

Machine learning-based screening of complex molecules for polymer solar cells

(2018) Peter Bjørn Jørgensen et al.   JOURNAL OF CHEMICAL PHYSICS

SISSO: A compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates

(2018) Runhai Ouyang et al.   PHYSICAL REVIEW MATERIALS

Addressing uncertainty in atomistic machine learning

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

Stochastic Neural Network Approach for Learning High-Dimensional Free Energy Surfaces

(2017) Elia Schneider et al.   PHYSICAL REVIEW LETTERS

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

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

Bypassing the Kohn-Sham equations with machine learning

(2017) Felix Brockherde et al.   Nature Communications

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

Amp : A modular approach to machine learning in atomistic simulations

(2016) Alireza Khorshidi et al.   COMPUTER PHYSICS COMMUNICATIONS

Automated Discovery and Construction of Surface Phase Diagrams Using Machine Learning

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

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

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

Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies

(2013) Katja Hansen et al.   Journal of Chemical Theory and Computation

Universal transition state scaling relations for (de)hydrogenation over transition metals

(2011) S. Wang et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS