{"title":"Reproducing the color with reformulated recipe","authors":"Jinming Fan , Chao Qian , Shaodong Zhou","doi":"10.1016/j.aichem.2023.100003","DOIUrl":"https://doi.org/10.1016/j.aichem.2023.100003","url":null,"abstract":"<div><p>A reverse molecule contribution (reMC) - molecule contribution (MC) – Machine learning (ML) protocol for disassemble and reproduce the spectrum is presented. By splitting the mixture spectrum with monochromophoric spectra in the database in a “Peeling-Onion” manner, a new recipe can be obtained. Upon comparison of the reproduced spectrum (with the forward molecular contribution - machine learning method) with the original one, the reliability of the proposed method is justified.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"1 1","pages":"Article 100003"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49817436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Machine learning modeling of the absorption properties of azobenzene molecules","authors":"Valentin Stanev , Ryota Maehashi , Yoshimi Ohta , Ichiro Takeuchi","doi":"10.1016/j.aichem.2023.100002","DOIUrl":"https://doi.org/10.1016/j.aichem.2023.100002","url":null,"abstract":"<div><p>We present a machine learning framework for modeling the absorption properties of azobenzene molecules – an important class of organic compounds with many potential photochemical applications. The framework utilizes predictors based on the chemical composition and structure of each molecule and consists of separate regression models trained to predict the absorption at distinct wavelengths, covering the UV and visible light ranges. Despite the relatively small size of the dataset (330 molecule-absorption spectrum pairs), the models were able to learn to accurately predict the absorption at fixed wavelengths, as well as the position and intensity of the maximum absorption. These predictions can be used to rapidly screen thousands of candidate molecules for a variety of potential applications, reducing the need for time-consuming and expensive experiments or first-principles computations.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"1 1","pages":"Article 100002"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49817437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Starting the new journal of “Artificial Intelligence Chemistry”","authors":"Jun Jiang , Song Wang , Shaul Mukamel","doi":"10.1016/j.aichem.2023.100001","DOIUrl":"https://doi.org/10.1016/j.aichem.2023.100001","url":null,"abstract":"","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"1 1","pages":"Article 100001"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49817438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Probing the origin of higher efficiency of terphenyl phosphine over the biaryl framework in Pd-catalyzed C-N coupling: A combined DFT and machine learning study","authors":"Qingfu Ye , Yu Zhao , Jun Zhu","doi":"10.1016/j.aichem.2023.100005","DOIUrl":"https://doi.org/10.1016/j.aichem.2023.100005","url":null,"abstract":"<div><p>The Pd-catalyzed Buchwald–Hartwig coupling reaction is important in the construction of the C-N bond due to various applications in organic synthesis. Quantum chemical calculations are widely used in understanding reaction mechanisms whereas the machine learning method is extremely popular in recognizing the relationships of data. Here, we combine density functional theory calculations with the support vector regression method to probe the origin of the higher efficiency of terphenyl phosphine ligand over the biaryl counterpart in the Buchwald–Hartwig C-N coupling reaction. By quantum chemical calculations, the turnover frequency-determining transition states are located and ligand features are calculated with high accuracy. By machine learning, the relationship between the reaction barrier and ligand features has been examined. It is found that the interplay of the charge on the metal center, the cone angle of the ligands, and the Sterimol L parameters of the ligand determines the catalytic performance of the palladium catalysts with different phosphine ligands. Our findings could help experimental chemists to design the ligands for Pd-catalyzed C-N coupling reactions with high efficiency.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"1 1","pages":"Article 100005"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49817020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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