DOI: 10.1002/cctc.201900595. Machine Learning for Computational Heterogeneous Catalysis

Machine Learning for Computational Heterogeneous Catalysis

10.1002/cctc.201900595

Crossref journal-article

Wiley

ChemCatChem (311)

Abstract

AbstractBig data and artificial intelligence has revolutionized science in almost every field – from economics to physics. In the area of materials science and computational heterogeneous catalysis, this revolution has led to the development of scientific data repositories, as well as data mining and machine learning tools to investigate the vast materials space. The goal of using these tools is to establish a deeper understanding of the relations between materials properties and activity, selectivity and stability – the important figures of merit in catalysis. Based on these insights, catalyst design principles can be established, which hopefully lead us to discover highly efficient catalysts to solve pressing issues for a sustainable future and the synthesis of highly functional materials, chemicals and pharmaceuticals. The inherent complexity of catalytic reactions quests for machine learning methods to efficiently navigate through the high‐dimensional hyper‐surfaces in structure optimization problems to determine relevant chemical structures and transition states. In this review, we show how cutting edge data infrastructures and machine learning methods are being used to address problems in computational heterogeneous catalysis.

Bibliography

SchlexerâLamoureux, P., Winther, K. T., GarridoâTorres, J. A., Streibel, V., Zhao, M., Bajdich, M., AbildâPedersen, F., & Bligaard, T. (2019). Machine Learning for Computational Heterogeneous Catalysis. ChemCatChem, 11(16), 3581â3601. Portico.

Authors 8

Philomena Schlexer Lamoureux (first)
Kirsten T. Winther (additional)
Jose Antonio Garrido Torres (additional)
Verena Streibel (additional)
Meng Zhao (additional)
Michal Bajdich (additional)
Frank Abild‐Pedersen (additional)
Thomas Bligaard (additional)

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Dates

Type	When
Created	6 years, 3 months ago (May 15, 2019, 1:20 p.m.)
Deposited	1 year, 1 month ago (July 17, 2024, 10:25 p.m.)
Indexed	2 weeks, 5 days ago (Aug. 2, 2025, 1:16 a.m.)
Issued	6 years, 2 months ago (June 18, 2019)
Published	6 years, 2 months ago (June 18, 2019)
Published Online	6 years, 2 months ago (June 18, 2019)
Published Print	6 years ago (Aug. 21, 2019)

Funders 1

Basic Energy Sciences 10.13039/100006151
Region: Americas
gov (National government)
Labels6
1. Office of Basic Energy Sciences
2. DOE Office of Basic Energy Sciences
3. US Department of Energy's Basic Energy Sciences
4. DOE Basic Energy Sciences
5. Department of Energy Basic Energy Sciences Program
6. BES
Awards1
1. DE-AC02-76SF00515

BibTeX

@article{Schlexer_Lamoureux_2019, title={Machine Learning for Computational Heterogeneous Catalysis}, volume={11}, ISSN={1867-3899}, url={http://dx.doi.org/10.1002/cctc.201900595}, DOI={10.1002/cctc.201900595}, number={16}, journal={ChemCatChem}, publisher={Wiley}, author={Schlexer Lamoureux, Philomena and Winther, Kirsten T. and Garrido Torres, Jose Antonio and Streibel, Verena and Zhao, Meng and Bajdich, Michal and Abild‐Pedersen, Frank and Bligaard, Thomas}, year={2019}, month=jun, pages={3581–3601} }

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