- Home
- Publications
- Publication Search
- Publication Details
Title
Turning chemistry into information for heterogeneous catalysis
Authors
Keywords
-
Journal
INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY
Volume -, Issue -, Pages -
Publisher
Wiley
Online
2020-07-10
DOI
10.1002/qua.26382
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Performance of Metal-Catalyzed Hydrodebromination of Dibromomethane Analyzed by Descriptors Derived from Statistical Learning
- (2020) A. J. Saadun et al. ACS Catalysis
- Machine Learning for Computational Heterogeneous Catalysis
- (2019) Philomena Schlexer Lamoureux et al. ChemCatChem
- Machine Learning Accelerates the Discovery of Design Rules and Exceptions in Stable Metal-Oxo Intermediate Formation
- (2019) Aditya Nandy et al. ACS Catalysis
- First-principles-based multiscale modelling of heterogeneous catalysis
- (2019) Albert Bruix et al. Nature Catalysis
- Statistical learning goes beyond the d-band model providing the thermochemistry of adsorbates on transition metals
- (2019) Rodrigo García-Muelas et al. Nature Communications
- Machine learning meets volcano plots: Computational discovery of cross-coupling catalysts
- (2018) Benjamin Meyer et al. Chemical Science
- Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules
- (2018) Rafael Gómez-Bombarelli et al. ACS Central Science
- Accelerating the discovery of materials for clean energy in the era of smart automation
- (2018) Daniel P. Tabor et al. Nature Reviews Materials
- Machine learning for molecular and materials science
- (2018) Keith T. Butler et al. NATURE
- Machine Learning for Organic Cage Property Prediction
- (2018) Lukas Turcani et al. CHEMISTRY OF MATERIALS
- Dynamic Workflows for Routine Materials Discovery in Surface Science
- (2018) Kevin Tran et al. Journal of Chemical Information and Modeling
- Read between the Molecules: Computational Insights into Organic Semiconductors
- (2018) Ganna Gryn’ova et al. JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
- The atomic simulation environment—a Python library for working with atoms
- (2017) Ask Hjorth Larsen et al. JOURNAL OF PHYSICS-CONDENSED MATTER
- To address surface reaction network complexity using scaling relations machine learning and DFT calculations
- (2017) Zachary W. Ulissi et al. Nature Communications
- Universal fragment descriptors for predicting properties of inorganic crystals
- (2017) Olexandr Isayev 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
- A high-throughput framework for determining adsorption energies on solid surfaces
- (2017) Joseph H. Montoya et al. npj Computational Materials
- AiiDA: automated interactive infrastructure and database for computational science
- (2016) Giovanni Pizzi et al. COMPUTATIONAL MATERIALS SCIENCE
- Reproducibility in density functional theory calculations of solids
- (2016) K. Lejaeghere et al. SCIENCE
- The FAIR Guiding Principles for scientific data management and stewardship
- (2016) Mark D. Wilkinson et al. Scientific Data
- Building large microkinetic models with first-principles׳ accuracy at reduced computational cost
- (2015) Jonathan E. Sutton et al. CHEMICAL ENGINEERING SCIENCE
- FireWorks: a dynamic workflow system designed for high-throughput applications
- (2015) Anubhav Jain et al. CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
- 3Dmol.js: molecular visualization with WebGL
- (2014) N. Rego et al. BIOINFORMATICS
- Managing the Computational Chemistry Big Data Problem: The ioChem-BD Platform
- (2014) M. Álvarez-Moreno et al. Journal of Chemical Information and Modeling
- Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
- (2013) Anubhav Jain et al. APL Materials
Discover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversationAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started