{"title":"用于二氧化碳还原反应的配体保护铜纳米簇的共形主动学习辅助筛选","authors":"Diptendu Roy, Amitabha Das and Biswarup Pathak","doi":"10.1039/D4TA03728F","DOIUrl":null,"url":null,"abstract":"<p >In this study, we propose a conformal active learning (CAL) method to screen ligand-protected atomically precise Cu-nanoclusters for the CO<small><sub>2</sub></small> reduction reaction. We investigate the roles of core metals and protecting ligands in product selectivity. We demonstrate a unique machine learning model that accurately accelerates the screening of nanoclusters, calculating prediction uncertainty for unknown datasets with a rigorous coverage guarantee. This approach results in reduced error and uncertainty (mean prediction interval) in the overall dataset predictions, effectively balancing uncertainty, coverage guarantee, and prediction error for all the considered descriptors important for a catalytic reaction. Furthermore, through feature importance analysis, we determine the major influences from the considered features. By applying optimal criteria to the predicted results of each descriptor, we can identify the best selective catalysts for C<small><sub>1</sub></small> product formation. This CAL-based method will certainly open a new direction in the field of high-throughput screening of catalysts.</p>","PeriodicalId":10,"journal":{"name":"ACS Central Science","volume":null,"pages":null},"PeriodicalIF":12.7000,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Conformal active learning-aided screening of ligand-protected Cu-nanoclusters for CO2 reduction reactions†\",\"authors\":\"Diptendu Roy, Amitabha Das and Biswarup Pathak\",\"doi\":\"10.1039/D4TA03728F\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p >In this study, we propose a conformal active learning (CAL) method to screen ligand-protected atomically precise Cu-nanoclusters for the CO<small><sub>2</sub></small> reduction reaction. We investigate the roles of core metals and protecting ligands in product selectivity. We demonstrate a unique machine learning model that accurately accelerates the screening of nanoclusters, calculating prediction uncertainty for unknown datasets with a rigorous coverage guarantee. This approach results in reduced error and uncertainty (mean prediction interval) in the overall dataset predictions, effectively balancing uncertainty, coverage guarantee, and prediction error for all the considered descriptors important for a catalytic reaction. Furthermore, through feature importance analysis, we determine the major influences from the considered features. By applying optimal criteria to the predicted results of each descriptor, we can identify the best selective catalysts for C<small><sub>1</sub></small> product formation. This CAL-based method will certainly open a new direction in the field of high-throughput screening of catalysts.</p>\",\"PeriodicalId\":10,\"journal\":{\"name\":\"ACS Central Science\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":12.7000,\"publicationDate\":\"2024-09-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Central Science\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://pubs.rsc.org/en/content/articlelanding/2024/ta/d4ta03728f\",\"RegionNum\":1,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Central Science","FirstCategoryId":"88","ListUrlMain":"https://pubs.rsc.org/en/content/articlelanding/2024/ta/d4ta03728f","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
摘要
在这项研究中,我们提出了一种保形主动学习(CAL)方法,用于筛选保护配体的原子精确铜纳米团簇,以用于二氧化碳还原反应。在此,我们研究了核心金属和保护配体对产物选择性的作用。我们在此证明,这种独特的机器学习模型可以准确地加速纳米簇的筛选,在此基础上,可以计算未知数据集的预测不确定性,并提供严格的覆盖保证,从而减少整体数据集预测的误差和不确定性(平均预测区间)。因此,该模型有效地平衡了不确定性、覆盖保证和对催化反应非常重要的所有描述符的预测误差。此外,通过特征重要性分析,我们确定了所考虑特征的主要影响因素。最后,通过对每个描述符的所有预测结果进行筛选,并设定一些最佳标准,我们可以筛选出 C1 产物形成的最佳选择性催化剂。这种基于 CAL 的方法必将为催化剂的高通量筛选领域开辟一个新的方向。
Conformal active learning-aided screening of ligand-protected Cu-nanoclusters for CO2 reduction reactions†
In this study, we propose a conformal active learning (CAL) method to screen ligand-protected atomically precise Cu-nanoclusters for the CO2 reduction reaction. We investigate the roles of core metals and protecting ligands in product selectivity. We demonstrate a unique machine learning model that accurately accelerates the screening of nanoclusters, calculating prediction uncertainty for unknown datasets with a rigorous coverage guarantee. This approach results in reduced error and uncertainty (mean prediction interval) in the overall dataset predictions, effectively balancing uncertainty, coverage guarantee, and prediction error for all the considered descriptors important for a catalytic reaction. Furthermore, through feature importance analysis, we determine the major influences from the considered features. By applying optimal criteria to the predicted results of each descriptor, we can identify the best selective catalysts for C1 product formation. This CAL-based method will certainly open a new direction in the field of high-throughput screening of catalysts.
期刊介绍:
ACS Central Science publishes significant primary reports on research in chemistry and allied fields where chemical approaches are pivotal. As the first fully open-access journal by the American Chemical Society, it covers compelling and important contributions to the broad chemistry and scientific community. "Central science," a term popularized nearly 40 years ago, emphasizes chemistry's central role in connecting physical and life sciences, and fundamental sciences with applied disciplines like medicine and engineering. The journal focuses on exceptional quality articles, addressing advances in fundamental chemistry and interdisciplinary research.