Research graph
References from Machine Learning in Catalysis, From Proposal to Practicing. Local targets link to admitted publications; unresolved targets remain external evidence.
Machine Learning: Trends, Perspectives, and Prospects
10.1126/science.aaa8415 · 2015 · External reference
Search for Catalysts by Inverse Design: Artificial Intelligence, Mountain Climbers, and Alchemists
10.1021/acs.chemrev.8b00759 · 2019 · External reference
Machine Learning in Catalysis
10.1038/s41929-018-0056-y · 2018 · External reference
Toward Artificial Intelligence in Catalysis
10.1038/s41929-018-0150-1 · 2018 · External reference
10.1007/978-0-387-98135-2
10.1007/978-0-387-98135-2 · 2009 · External reference
Artificial Intelligence: The Future for Organic Chemistry?
10.1021/acsomega.8b01773 · 2018 · External reference
Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry
10.1021/acs.inorgchem.9b00109 · 2019 · External reference
Deep Learning in Chemistry
10.1021/acs.jcim.9b00266 · 2019 · External reference
Catalytic Activities of Bis(pentamethylene)pyridyl (Fe/Co) Complex Analogues in Ethylene Polymerization by Modeling Method
10.1021/acs.jpca.8b09121 · 2018 · External reference
3D-QSAR as a Tool for Understanding and Improving Single Site Polymerization Catalysts: A Review
10.1021/om400721v · 2014 · External reference
Catalytic Performance of Bis(imino)pyridylmetal Precatalysts in Ethylene Polymerization by 2D-/3D-QSPR Modeling
10.1002/jcc.25792 · 2019 · External reference
Effect of Cycloalkyl-fused Ring on the Catalytic Performance of Bis(imino)pyridine Metal Complexes by QSPR Modeling
10.1016/j.catcom.2019.105820 · 2019 · External reference
Machine Learning Meets Volcano Plots: Computational Discovery of Cross-Coupling Catalysts
10.1039/c8sc01949e · 2018 · External reference
Predicting Reaction Performance in C–N Cross-coupling using Machine Learning
10.1126/science.aar5169 · 2018 · External reference
Machine Learning for Predicting Product Distributions in Catalytic Regioselective Reactions
10.1039/c8cp03141j · 2018 · External reference
Holistic Prediction of Enantioselectivity in Asymmetric Catalysis
10.1038/s41586-019-1384-z · 2019 · External reference
Towards Operando Computational Modeling in Heterogeneous Catalysis
10.1039/c8cs00398j · 2018 · External reference
Machine Learning for Heterogeneous Catalyst Design and Discovery
10.1002/aic.16198 · 2018 · External reference
High-Throughput Screening of Bimetallic Catalysts Enabled by Machine Learning
10.1039/c7ta01812f · 2017 · External reference
Machine-Learning Methods Enable Exhaustive Searches for Active Bimetallic Facets and Reveal Active Site Motifs for CO2 Reduction
10.1021/acscatal.7b01648 · 2017 · External reference
Predicting Catalytic Activity of Nanoparticles by a DFT-Aided Machine-Learning Algorithm
10.1021/acs.jpclett.7b02010 · 2017 · External reference
Chemical Pressure-Driven Enhancement of the Hydrogen Evolving Activity of Ni2P from Nonmetal Surface Doping Interpreted via Machine Learning
10.1021/jacs.8b00947 · 2018 · External reference
Rapid Estimation of Activation Energy in Heterogeneous Catalytic Reactions via Machine Learning
10.1002/jcc.25567 · 2018 · External reference
Predicting Chemical Reaction Barriers with a Machine Learning Model
10.1007/s10562-019-02705-x · 2019 · External reference
Machine Learning and Molecular Descriptors Enalbe Rational Sovent Selection in Asymmetric Catalysis
10.1039/c9sc01844a · 2019 · External reference
