Artificial-Intelligence Driven Precision Chemistry

InfoMetricsFiguresRef. Precision ChemistryASAPArticle This publication is Open Access under the license indicated. Learn More CiteCitationCitation and abstractCitation and referencesMore citation options ShareShare onFacebookX (Twitter)WeChatLinkedInRedditEmailJump toExpandCollapse EditorialNovember 5, 2024Artificial-Intelligence Driven Precision ChemistryClick to copy article linkArticle link copied!Zhenyu Li*Zhenyu LiKey Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China, Hefei, Anhui 230026, China*Email: [email protected]More by Zhenyu Lihttps://orcid.org/0000-0003-2112-9834Open PDFPrecision ChemistryCite this: Precis. Chem. 2024, XXXX, XXX, XXX-XXXClick to copy citationCitation copied!https://pubs.acs.org/doi/10.1021/prechem.4c00086https://doi.org/10.1021/prechem.4c00086Published November 5, 2024 Publication History Received 29 October 2024Published online 5 November 2024editorialCo-published 2024 by University of Science and Technology of China and American Chemical Society. This publication is licensed under CC-BY-NC-ND 4.0 . License Summary*You are free to share (copy and redistribute) this article in any medium or format within the parameters below:Creative Commons (CC): This is a Creative Commons license.Attribution (BY): Credit must be given to the creator.Non-Commercial (NC): Only non-commercial uses of the work are permitted. No Derivatives (ND): Derivative works may be created for non-commercial purposes, but sharing is prohibited. View full license*DisclaimerThis summary highlights only some of the key features and terms of the actual license. It is not a license and has no legal value. Carefully review the actual license before using these materials. This publication is licensed underCC-BY-NC-ND 4.0 . License Summary*You are free to share(copy and redistribute) this article in any medium or format within the parameters below: Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator.Non-Commercial (NC): Only non-commercial uses of the work are permitted. No Derivatives (ND): Derivative works may be created for non-commercial purposes, but sharing is prohibited. View full license *DisclaimerThis summary highlights only some of the key features and terms of the actual license. It is not a license and has no legal value. Carefully review the actual license before using these materials. License Summary*You are free to share(copy and redistribute) this article in any medium or format within the parameters below: Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Non-Commercial (NC): Only non-commercial uses of the work are permitted. No Derivatives (ND): Derivative works may be created for non-commercial purposes, but sharing is prohibited. View full license *DisclaimerThis summary highlights only some of the key features and terms of the actual license. It is not a license and has no legal value. Carefully review the actual license before using these materials. License Summary*You are free to share(copy and redistribute) this article in any medium or format within the parameters below: Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Non-Commercial (NC): Only non-commercial uses of the work are permitted. No Derivatives (ND): Derivative works may be created for non-commercial purposes, but sharing is prohibited. View full license *DisclaimerThis summary highlights only some of the key features and terms of the actual license. It is not a license and has no legal value. Carefully review the actual license before using these materials. License Summary*You are free to share(copy and redistribute) this article in any medium or format within the parameters below: Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Non-Commercial (NC): Only non-commercial uses of the work are permitted. No Derivatives (ND): Derivative works may be created for non-commercial purposes, but sharing is prohibited. View full license *DisclaimerThis summary highlights only some of the key features and terms of the actual license. It is not a license and has no legal value. Carefully review the actual license before using these materials. License Summary*You are free to share(copy and redistribute) this article in any medium or format within the parameters below: Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Non-Commercial (NC): Only non-commercial uses of the work are permitted. No Derivatives (ND): Derivative works may be created for non-commercial purposes, but sharing is prohibited. View full license *DisclaimerThis summary highlights only some of the key features and terms of the actual license. It is not a license and has no legal value. Carefully review the actual license before using these materials. ACS PublicationsCo-published 2024 by University of Science and Technology of China and American Chemical SocietySubjectswhat are subjectsArticle subjects are automatically applied from the ACS Subject Taxonomy and describe the scientific concepts and themes of the article.Machine learningMaterialsMoleculesNeural networksOptimizationPrecision chemistry represents an ultimate goal of chemical research. For example, it is highly desirable to precisely control the properties of molecules and materials. If the target compounds are known, then we should aim at synthesizing them with 100% yield and 100% selectivity and avoid the production of waste, in an economical, safe, resource-efficient, energy-efficient and environmentally benign way. (1) In addition to controlling properties and reactions, another aspect of precision chemistry is obtaining precise and accurate chemical data.For both aspects of precision chemistry, artificial intelligence can play an important role. On the precise data side, machine learning models can be used to represent complex many-body wave functions to facilitate the accurate solution of the Schrödinger equation. (2) By using machine learning potential to speed up molecular simulation, accurate thermodynamic properties of a molecular system become more easily accessible. Artificial intelligence can also be used in data analysis and noise reduction in the characterization of chemical properties. On the precise control side, artificial intelligence helps with exploring high-dimensional chemical space, speeding up optimization of chemical structures and synthesis conditions.Therefore, precision chemistry and intelligent chemistry are closely related. Artificial intelligence helps to generate precise and accurate chemical data, which then can be used to generate more intelligent chemical models enabling better descriptions of complex chemical spaces. With complexed chemical correlation grasped, precise control of properties and reactions becomes easier. Such a loop fron intelligence to precision and then back to intelligence and precision represents a new paradigm of chemical research. Some universities have already established new institutions to promote research in this direction, such as the Key Laboratory of Precision and Intelligent Chemistry at University of Science and Technology of China (USTC). (3)This Virtual Collection in Precision Chemistry titled "Artificial-Intelligence Driven Precision Chemistry" features the latest research and perspectives from leading scientists in this rapidly developing field of precision and intelligent chemistry, highlighting neural network potentials, data analysis and spectrum interpretation with artificial-intelligence models, materials design and reaction optimization, and robotic chemists.Xie et al. (4) developed the LASP software combining advanced neural network potentials with efficient global optimization methods to achieve high accuracy and efficiency in atomic simulations. Neural network potentials were also used by Cheng et al. (5) to identify active sites and enhancing the understanding of catalytic processes and also by Jia et al. (6) to investigate interfacial proton transfer mechanisms at the SnO2(110)/H2O interface, revealing the role of the solvation environment in proton conduction.Yang et al. (7) explored the application of artificial intelligence in single-molecule bioelectronic sensing. It is demonstrated that artificial intelligence can enhance signal processing and data analysis to improve accuracy and reliability of biological characterization. Wu et al. (8) proposed a support vector machine model-based protocol combined with first-principles 13C chemical shift calculations to identify the structural and stereochemical assignments of complex organic compounds with high confidence.In the review article by Chavalekvirat et al., (9) the importance of data science and machine learning for optimization and novel discovery of 2D materials was emphasized. Fan et al. (10) presented a theory-guided machine learning framework combining the Lewis-mode group contribution method with a multistage Bayesian neural network and evolutionary algorithm to design organic nonlinear optical materials with high precision. Machine learning is also mentioned as a powerful tool in optimizing the synthesis parameters and guiding the experimental designs for water-soluble Au nanoclusters synthesis in a time-saving manner. (11)In the comment by Luo, (12) the impact of robotic artificial-intelligence chemist by automating experiments and theoretical calculations was discussed. While in another perspective article, Zhu (13) discussed the uniform of chemical theory, simulation, and experiments in metaverse technology by merging data from various sources.In summary, this Virtual Collection of Precision Chemistry highlights the critical role of artificial intelligence in precision chemistry. It enables efficient chemical simulation, accurate chemical characterization, and precise molecule design and reaction control, which ultimately leads to a new chemical research paradigm.Author InformationClick to copy section linkSection link copied!Corresponding AuthorZhenyu Li, Key Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China, Hefei, Anhui 230026, China, https://orcid.org/0000-0003-2112-9834, Email: [email protected]NotesViews expressed in this editorial are those of the author and not necessarily the views of the ACS.ReferencesClick to copy section linkSection link copied! This article references 13 other publications. 1Noyori, R. synthesizing our future. Nat. Chem. 2009, 1, 5– 6, DOI: 10.1038/nchem.143 Google ScholarThere is no corresponding record for this reference.2Hermann, J.; Spencer, J.; Choo, K.; Mezzacapo, A.; Foulkes, W. M. C.; Pfau, D.; Carleo, G.; Noé, F. Ab initio quantum chemistry with neural-network wavefunctions. Nat. Rev. Chem. 2023, 7, 692– 709, DOI: 10.1038/s41570-023-00516-8 Google ScholarThere is no corresponding record for this reference.3https://pichem.ustc.edu.cn/There is no corresponding record for this reference.4Xie, X.-T.; Yang, Z.-X.; Chen, D.-X.; Shi, Y.-F.; Kang, P.-L.; Ma, S.-C.; Li, Y.-F.; Shang, C.; Liu, Z.-P. LASP to the future of atomic simulation: intelligence and automation. Precis. Chem. 2024, DOI: 10.1021/prechem.4c00060 Google ScholarThere is no corresponding record for this reference.5Cheng, X.-R.; Wu, C.-Y.; Xu, J.-Y.; Han, Y.-L.; Xie, W.-B.; Hu, P. Leveraging machine learning potentials for in-situ searching of active sites in heterogeneous catalysis. Precis. Chem. 2024, DOI: 10.1021/prechem.4c00051 Google ScholarThere is no corresponding record for this reference.6Jia, M.; Zhuang, Y.-B.; Wang, F.; Zhang, C.; Cheng, J. Water-mediated proton hopping mechanisms at the SnO2(110)/H2O interface from ab initio deep potential molecular dynamics. Precis. Chem. 2024, DOI: 10.1021/prechem.4c00056 Google ScholarThere is no corresponding record for this reference.7Yang, Y.-X.; Li, Y.-Q.; Tang, L.-H.; Li, J.-H. Single-molecule bioelectronic sensors with AI-aided data analysis: convergence and challenges. Precis. Chem. 2024, 2, 518– 538, DOI: 10.1021/prechem.4c00048 Google ScholarThere is no corresponding record for this reference.8Wu, A.; Ye, Q.; Zhuang, X.-W.; Chen, Q.-W.; Zhang, J.-K.; Wu, J.-M.; Xu, X. Elucidating structures of complex organic compounds using a machine learning model based on the 13C NMR chemical shifts. Precis. Chem. 2023, 1, 57– 68, DOI: 10.1021/prechem.3c00005 Google ScholarThere is no corresponding record for this reference.9Chavalekvirat, P.; Hirunpinyopas, W.; Deshsorn, K.; Jitapunkul, K.; Iamprasertkun, P. Liquid phase exfoliation of 2D materials and its electrochemical applications in the data-driven future. Precis. Chem. 2024, 2, 300– 329, DOI: 10.1021/prechem.3c00119 Google ScholarThere is no corresponding record for this reference.10Fan, J.-M.; Yuan, B.-W.; Qian, C.; Zhou, S.-D. Group contribution method supervised neural network for precise design of organic nonlinear optical materials. Precis. Chem. 2024, 2, 263– 272, DOI: 10.1021/prechem.4c00015 Google ScholarThere is no corresponding record for this reference.11Yan, Q.; Yuan, Z.-T.; Wu, Y.-T.; Zhou, C.-M.; Dai, Y.-H.; Wan, X.-Y.; Yang, D.; Liu, X.; Xue, N.-H.; Zhu, Y.; Yang, Y.-H. Atomically precise water-soluble gold nanoclusters: synthesis and biomedical application. Precis. Chem. 2023, 1, 468– 479, DOI: 10.1021/prechem.3c00036 Google ScholarThere is no corresponding record for this reference.12Luo, Y. Chemistry in the era of artificial intelligence. Precis. Chem. 2023, 1, 127– 128, DOI: 10.1021/prechem.3c00038 Google ScholarThere is no corresponding record for this reference.13Zhu, X. Toward the uniform of chemical theory, simulation, and experiments in Metaverse technology. Precis. Chem. 2023, 1, 192– 198, DOI: 10.1021/prechem.3c00045 Google ScholarThere is no corresponding record for this reference.Cited By Click to copy section linkSection link copied!This article has not yet been cited by other publications.Download PDFFiguresReferences Get e-AlertsGet e-AlertsPrecision ChemistryCite this: Precis. Chem. 2024, XXXX, XXX, XXX-XXXClick to copy citationCitation copied!https://doi.org/10.1021/prechem.4c00086Published November 5, 2024 Publication History Received 29 October 2024Published online 5 November 2024Co-published 2024 by University of Science and Technology of China and American Chemical Society. This publication is licensed under CC-BY-NC-ND 4.0 . License Summary*You are free to share (copy and redistribute) this article in any medium or format within the parameters below:Creative Commons (CC): This is a Creative Commons license.Attribution (BY): Credit must be given to the creator.Non-Commercial (NC): Only non-commercial uses of the work are permitted. No Derivatives (ND): Derivative works may be created for non-commercial purposes, but sharing is prohibited. View full license*DisclaimerThis summary highlights only some of the key features and terms of the actual license. It is not a license and has no legal value. Carefully review the actual license before using these materials. Article Views-Altmetric-Citations-Learn about these metrics closeArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated.Recommended Articles FiguresReferencesThis publication has no figures.References This article references 13 other publications. 1Noyori, R. synthesizing our future. Nat. Chem. 2009, 1, 5– 6, DOI: 10.1038/nchem.143 There is no corresponding record for this reference.2Hermann, J.; Spencer, J.; Choo, K.; Mezzacapo, A.; Foulkes, W. M. C.; Pfau, D.; Carleo, G.; Noé, F. Ab initio quantum chemistry with neural-network wavefunctions. Nat. Rev. Chem. 2023, 7, 692– 709, DOI: 10.1038/s41570-023-00516-8 There is no corresponding record for this reference.3https://pichem.ustc.edu.cn/There is no corresponding record for this reference.4Xie, X.-T.; Yang, Z.-X.; Chen, D.-X.; Shi, Y.-F.; Kang, P.-L.; Ma, S.-C.; Li, Y.-F.; Shang, C.; Liu, Z.-P. LASP to the future of atomic simulation: intelligence and automation. Precis. Chem. 2024, DOI: 10.1021/prechem.4c00060 There is no corresponding record for this reference.5Cheng, X.-R.; Wu, C.-Y.; Xu, J.-Y.; Han, Y.-L.; Xie, W.-B.; Hu, P. Leveraging machine learning potentials for in-situ searching of active sites in heterogeneous catalysis. Precis. Chem. 2024, DOI: 10.1021/prechem.4c00051 There is no corresponding record for this reference.6Jia, M.; Zhuang, Y.-B.; Wang, F.; Zhang, C.; Cheng, J. Water-mediated proton hopping mechanisms at the SnO2(110)/H2O interface from ab initio deep potential molecular dynamics. Precis. Chem. 2024, DOI: 10.1021/prechem.4c00056 There is no corresponding record for this reference.7Yang, Y.-X.; Li, Y.-Q.; Tang, L.-H.; Li, J.-H. Single-molecule bioelectronic sensors with AI-aided data analysis: convergence and challenges. Precis. Chem. 2024, 2, 518– 538, DOI: 10.1021/prechem.4c00048 There is no corresponding record for this reference.8Wu, A.; Ye, Q.; Zhuang, X.-W.; Chen, Q.-W.; Zhang, J.-K.; Wu, J.-M.; Xu, X. Elucidating structures of complex organic compounds using a machine learning model based on the 13C NMR chemical shifts. Precis. Chem. 2023, 1, 57– 68, DOI: 10.1021/prechem.3c00005 There is no corresponding record for this reference.9Chavalekvirat, P.; Hirunpinyopas, W.; Deshsorn, K.; Jitapunkul, K.; Iamprasertkun, P. Liquid phase exfoliation of 2D materials and its electrochemical applications in the data-driven future. Precis. Chem. 2024, 2, 300– 329, DOI: 10.1021/prechem.3c00119 There is no corresponding record for this reference.10Fan, J.-M.; Yuan, B.-W.; Qian, C.; Zhou, S.-D. Group contribution method supervised neural network for precise design of organic nonlinear optical materials. Precis. Chem. 2024, 2, 263– 272, DOI: 10.1021/prechem.4c00015 There is no corresponding record for this reference.11Yan, Q.; Yuan, Z.-T.; Wu, Y.-T.; Zhou, C.-M.; Dai, Y.-H.; Wan, X.-Y.; Yang, D.; Liu, X.; Xue, N.-H.; Zhu, Y.; Yang, Y.-H. Atomically precise water-soluble gold nanoclusters: synthesis and biomedical application. Precis. Chem. 2023, 1, 468– 479, DOI: 10.1021/prechem.3c00036 There is no corresponding record for this reference.12Luo, Y. Chemistry in the era of artificial intelligence. Precis. Chem. 2023, 1, 127– 128, DOI: 10.1021/prechem.3c00038 There is no corresponding record for this reference.13Zhu, X. Toward the uniform of chemical theory, simulation, and experiments in Metaverse technology. Precis. Chem. 2023, 1, 192– 198, DOI: 10.1021/prechem.3c00045 There is no corresponding record for this reference.