- Active Learning Accelerated Discovery of Stable Iridium Oxide Polymorphs for the Oxygen Evolution Reaction CHEMISTRY OF MATERIALS 2020; 32 (13): 5854–63
- Machine Learning for Computational Heterogeneous Catalysis CHEMCATCHEM 2019; 11 (16): 3579–99
Catalysis-Hub.org, an open electronic structure database for surface reactions.
2019; 6 (1): 75
We present a new open repository for chemical reactions on catalytic surfaces, available at https://www.catalysis-hub.org . The featured database for surface reactions contains more than 100,000 chemisorption and reaction energies obtained from electronic structure calculations, and is continuously being updated with new datasets. In addition to providing quantum-mechanical results for a broad range of reactions and surfaces from different publications, the database features a systematic, large-scale study of chemical adsorption and hydrogenation on bimetallic alloy surfaces. The database contains reaction specific information, such as the surface composition and reaction energy for each reaction, as well as the surface geometries andcalculational parameters, essential for data reproducibility. By providing direct access via the web-interface as well as a Python API, we seek to accelerate the discovery of catalytic materials for sustainable energy applications by enabling researchers to efficiently use the data as a basis for new calculations and model generation.
View details for DOI 10.1038/s41597-019-0081-y
View details for PubMedID 31138816
High-throughput calculations of catalytic properties of bimetallic alloy surfaces.
2019; 6 (1): 76
A comprehensive database of chemical properties on a vast set of transition metal surfaces has the potential to accelerate the discovery of novel catalytic materials for energy and industrial applications. In this data descriptor, we present such an extensive study of chemisorption properties of important adsorbates - e.g., C, O, N, H, S, CHx, OH, NH, and SH - on 2,035 bimetallic alloy surfaces in 5 different stoichiometric ratios, i.e., 0%, 25%, 50%, 75%, and 100%. To our knowledge, it is the first systematic study to compile the adsorption properties of such a well-defined, large chemical space of catalytic interest. We propose that a collection of catalytic properties of this magnitude can assist with the development of machine learning enabled surrogate models in theoretical catalysis research to design robust catalysts with high activity for challenging chemical transformations. This database is made publicly available through the platform www.Catalysis-hub.org for easy retrieval of the data for further scientific analysis.
View details for DOI 10.1038/s41597-019-0080-z
View details for PubMedID 31138814
Catalysis-hub.org: An open electronic structure database for surface reactions and catalytic materials
AMER CHEMICAL SOC. 2019
View details for Web of Science ID 000478860501774
Graph Theory Approach to High-Throughput Surface Adsorption Structure Generation.
The journal of physical chemistry. A
We present a methodology for graph based enumeration of surfaces and unique chemical adsorption structures bonded to those surfaces. Utilizing the graph produced from a bulk structure, we create a unique graph representation for any general slab cleave and further extend that representation to include a large variety of catalytically relevant adsorbed molecules. We also demonstrate simple geometric procedures to generate 3D initial guesses of these enumerated structures. While generally useful for generating a wide variety of structures used in computational surface science and heterogeneous catalysis, these techniques are also key to facilitating an informatics approach to the high-throughput search for more effective catalysts.
View details for PubMedID 30802053