Machine Learning Descriptors for Data‐Driven Catalysis Study
Abstract Traditional trial‐and‐error experiments and theoretical simulations have difficulty optimizing catalytic processes and developing new, better‐performing catalysts. Machine learning (ML) provides a promising approach for accelerating catalysis research due to its powerful learning and predic...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-08-01
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| Series: | Advanced Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/advs.202301020 |
| _version_ | 1827877452548931584 |
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| author | Li‐Hui Mou TianTian Han Pieter E. S. Smith Edward Sharman Jun Jiang |
| author_facet | Li‐Hui Mou TianTian Han Pieter E. S. Smith Edward Sharman Jun Jiang |
| author_sort | Li‐Hui Mou |
| collection | DOAJ |
| description | Abstract Traditional trial‐and‐error experiments and theoretical simulations have difficulty optimizing catalytic processes and developing new, better‐performing catalysts. Machine learning (ML) provides a promising approach for accelerating catalysis research due to its powerful learning and predictive abilities. The selection of appropriate input features (descriptors) plays a decisive role in improving the predictive accuracy of ML models and uncovering the key factors that influence catalytic activity and selectivity. This review introduces tactics for the utilization and extraction of catalytic descriptors in ML‐assisted experimental and theoretical research. In addition to the effectiveness and advantages of various descriptors, their limitations are also discussed. Highlighted are both 1) newly developed spectral descriptors for catalytic performance prediction and 2) a novel research paradigm combining computational and experimental ML models through suitable intermediate descriptors. Current challenges and future perspectives on the application of descriptors and ML techniques to catalysis are also presented. |
| first_indexed | 2024-03-12T17:37:38Z |
| format | Article |
| id | doaj.art-62c534a4c17f4e43aa3cc4a0018e6820 |
| institution | Directory Open Access Journal |
| issn | 2198-3844 |
| language | English |
| last_indexed | 2024-03-12T17:37:38Z |
| publishDate | 2023-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Science |
| spelling | doaj.art-62c534a4c17f4e43aa3cc4a0018e68202023-08-04T07:49:49ZengWileyAdvanced Science2198-38442023-08-011022n/an/a10.1002/advs.202301020Machine Learning Descriptors for Data‐Driven Catalysis StudyLi‐Hui Mou0TianTian Han1Pieter E. S. Smith2Edward Sharman3Jun Jiang4Hefei National Research Center for Physical Sciences at the Microscale School of Chemistry and Materials Science University of Science and Technology of China Hefei Anhui 230026 ChinaHefei JiShu Quantum Technology Co. Ltd. Hefei 230026 ChinaYDS Pharmatech ETEC 1220 Washington Ave. Albany NY 12203 USADepartment of Neurology University of California Irvine CA 92697 USAHefei National Research Center for Physical Sciences at the Microscale School of Chemistry and Materials Science University of Science and Technology of China Hefei Anhui 230026 ChinaAbstract Traditional trial‐and‐error experiments and theoretical simulations have difficulty optimizing catalytic processes and developing new, better‐performing catalysts. Machine learning (ML) provides a promising approach for accelerating catalysis research due to its powerful learning and predictive abilities. The selection of appropriate input features (descriptors) plays a decisive role in improving the predictive accuracy of ML models and uncovering the key factors that influence catalytic activity and selectivity. This review introduces tactics for the utilization and extraction of catalytic descriptors in ML‐assisted experimental and theoretical research. In addition to the effectiveness and advantages of various descriptors, their limitations are also discussed. Highlighted are both 1) newly developed spectral descriptors for catalytic performance prediction and 2) a novel research paradigm combining computational and experimental ML models through suitable intermediate descriptors. Current challenges and future perspectives on the application of descriptors and ML techniques to catalysis are also presented.https://doi.org/10.1002/advs.202301020catalytic descriptorsheterogeneous catalysishigh‐throughput experimentsmachine learningtheoretical simulations |
| spellingShingle | Li‐Hui Mou TianTian Han Pieter E. S. Smith Edward Sharman Jun Jiang Machine Learning Descriptors for Data‐Driven Catalysis Study Advanced Science catalytic descriptors heterogeneous catalysis high‐throughput experiments machine learning theoretical simulations |
| title | Machine Learning Descriptors for Data‐Driven Catalysis Study |
| title_full | Machine Learning Descriptors for Data‐Driven Catalysis Study |
| title_fullStr | Machine Learning Descriptors for Data‐Driven Catalysis Study |
| title_full_unstemmed | Machine Learning Descriptors for Data‐Driven Catalysis Study |
| title_short | Machine Learning Descriptors for Data‐Driven Catalysis Study |
| title_sort | machine learning descriptors for data driven catalysis study |
| topic | catalytic descriptors heterogeneous catalysis high‐throughput experiments machine learning theoretical simulations |
| url | https://doi.org/10.1002/advs.202301020 |
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