Artificial intelligence chemistry最新文献

{"title":"SOmicsFusion: Multimodal coregistration and fusion between spatial metabolomics and biomedical imaging","authors":"Ang Guo ,&nbsp;Zhiyu Chen ,&nbsp;Yinzhong Ma ,&nbsp;Yueguang Lv ,&nbsp;Huanhuan Yan ,&nbsp;Fang Li ,&nbsp;Yao Xing ,&nbsp;Qian Luo ,&nbsp;Hairong Zheng","doi":"10.1016/j.aichem.2024.100058","DOIUrl":"https://doi.org/10.1016/j.aichem.2024.100058","url":null,"abstract":"<div><p>We present SOmicsFusion, a software toolbox for ’fusing’ spatial omics with classical biomedical imaging modalities, capitalizing on their inherent correspondences and complementarity when characterizing the same subject. By augmenting radiological and histological images with spatially resolved molecular profiling, this fusion offers a panoramic characterization of the biochemical perturbations underlying pathological conditions, thereby advancing our understanding of diseases like brain disorders and cancers. The cornerstone of SOmicsFusion is a coregistration tool that leverages an innovative two-stage machine learning pipeline to tackle the longstanding challenge of spatially aligning data from fundamentally different modalities, priming them for subsequent fusion analysis that often requires precise pixel-wise correspondence between the datasets. Specifically, the pipeline utilizes an original dimension reduction algorithm for representational domain alignment, followed by a Deep Learning-based method for spatial domain alignment. SOmicsFusion is demonstrated using mass spectrometry imaging (MSI)-mediated spatial metabolomics and four other modalities: magnetic resonance imaging (MRI), microscopy, brain atlas, and spatial transcriptomics. By reducing coregistration errors by 38–69% compared to existing pipelines, SOmicsFusion enhances the precision of associating molecule distribution with anatomy and pathology features, ultimately leading to more statistically robust findings. Furthermore, SOmicsFusion incorporates various downstream analysis tools, including overlay visualization, spatial correlation/co-expression analysis, pansharpening, and automated anatomy annotation. These tools facilitate the extraction of biological insights that would be unattainable through individual modalities alone. For instance, the coregistration and correlation between MSI and in vivo MRI datasets unveil that the spatial heterogeneity in metabolites stems from the temporal heterogeneity in the development of cerebral ischemia-reperfusion injury.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100058"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000162/pdfft?md5=963d89fc26dbcda572405e5ce54d24ee&pid=1-s2.0-S2949747724000162-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140134391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Size dependent lithium-ion conductivity of solid electrolytes in machine learning molecular dynamics simulations","authors":"Yixi Zhang ,&nbsp;Jin-Da Luo ,&nbsp;Hong-Bin Yao ,&nbsp;Bin Jiang","doi":"10.1016/j.aichem.2024.100051","DOIUrl":"10.1016/j.aichem.2024.100051","url":null,"abstract":"<div><p>Solid-state electrolytes are key ingredients in next-generation devices for energy storage and release. Machine learning molecular dynamics (MLMD) has shown great promise in studying the diffusivity of mobile ions in solid-state electrolytes, with much higher efficiency than conventional ab initio molecular dynamics (AIMD). In this work, we combine an efficient embedded atom neural network (EANN) approach and an uncertainty-driven active learning algorithm that optimally selects data points from high-temperature AIMD trajectories to construct ML potentials for solid-state electrolytes and validate this strategy in a benchmark system, Li₃YCl₆, for which several controversy theoretical results exist. Through systematic MLMD simulations, we find that a typically used small supercell in AIMD simulations fails to predict the supersonic transition at a critical temperature, leading to a significant overestimation of the Li⁺ conductivity in Li₃YCl₆ at room temperature. Fortunately, thanks to the scalability of the EANN potential, extended MLMD simulations in a sufficiently large cell does yield a notable change of temperature-dependence in conductivity at ∼420 K and a much lower room-temperature conductivity in excellent with experiment. Interestingly, our results are all based on a semi-local PBE density functional, which was argued unable to predict the superionic transition. We analyze possible reasons of the seemingly inconsistent MLMD results reported in literature with different ML potentials. This work paves the way of simply using high-temperature AIMD data to generate more reliable MLMD results of low-temperature ionic conductivities in solid-state electrolytes.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100051"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000095/pdfft?md5=ff9758425c151a024cd1c50e2503eb45&pid=1-s2.0-S2949747724000095-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139635555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning advancements in organic synthesis: A focused exploration of artificial intelligence applications in chemistry","authors":"Rizvi Syed Aal E Ali ,&nbsp;Jiaolong Meng ,&nbsp;Muhammad Ehtisham Ibraheem Khan ,&nbsp;Xuefeng Jiang","doi":"10.1016/j.aichem.2024.100049","DOIUrl":"https://doi.org/10.1016/j.aichem.2024.100049","url":null,"abstract":"<div><p>Artificial intelligence (AI) is driving a revolution in chemistry, reshaping the landscape of molecular design. This review explores AI’s pivotal roles in the field of organic synthesis applications. AI accurately predicts reaction outcomes, controls chemical selectivity, simplifies synthesis planning, accelerates catalyst discovery, and fuels material innovation and so on. It seamlessly integrates data-driven algorithms with chemical intuition to redefine molecular design. As AI chemistry advances, it promises accelerated research, sustainability, and innovative solutions to chemistry’s pressing challenges. The fusion of AI and chemistry is poised to shape the field’s future profoundly, offering new horizons in precision and efficiency. This review encapsulates the transformation of AI in chemistry, marking a pivotal moment where algorithms and data converge to revolutionize the world of molecules.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100049"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000071/pdfft?md5=ca6a79f1c6ae5ed3980ec0ff3589b022&pid=1-s2.0-S2949747724000071-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139549589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Applying graph neural network models to molecular property prediction using high-quality experimental data","authors":"Chen Qu,&nbsp;Barry I. Schneider,&nbsp;Anthony J. Kearsley,&nbsp;Walid Keyrouz,&nbsp;Thomas C. Allison","doi":"10.1016/j.aichem.2024.100050","DOIUrl":"https://doi.org/10.1016/j.aichem.2024.100050","url":null,"abstract":"<div><p>Graph neural networks have been successfully applied to machine learning models related to molecules and crystals, due to the similarity between a molecule/crystal and a graph. In this paper, we present three models that are trained with high-quality experimental data to predict three molecular properties (Kováts retention index, normal boiling point, and mass spectrum), using the same GNN architecture. We show that graph representations of molecules, combined with deep learning methodologies and high-quality data sets, lead to accurate machine learning models to predict molecular properties.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100050"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000083/pdfft?md5=d755fd2f616c83e07982edec2890d06c&pid=1-s2.0-S2949747724000083-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139549590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the energy landscape of graphynes for noble gas adsorption using swarm intelligence","authors":"Megha Rajeevan,&nbsp;Rotti Srinivasamurthy Swathi","doi":"10.1016/j.aichem.2024.100048","DOIUrl":"https://doi.org/10.1016/j.aichem.2024.100048","url":null,"abstract":"<div><p>Gas adsorption on one-atom-thick membranes is a growing technology for separation applications owing to its excellent energy efficiency. Herein, we investigate the adsorption of the noble gases, Ne, Ar and Kr on graphynes (GYs), a novel class of one-atom-thick carbon membranes using a swarm intelligence technique, namely particle swarm optimization (PSO). Modeling the adsorption of noble gas clusters on two-dimensional substrates requires a thorough examination of the energy landscape. The high dimensionality of the problem makes it tricky to employ ab initio methods for such studies, necessitating the use of a metaheuristic global optimization technique such as PSO. We explored the adsorption of 1–30 atoms of Ne, Ar and Kr on α-, β-, γ- and rhombic-GYs to predict the most suitable form of GY for the adsorption of each of the gases. Employing the dispersion-corrected density functional theory (DFT-D) data for the adsorption of single gas atoms as the reference data, we parametrized two empirical pairwise potentials, namely, Lennard-Jones (LJ) and improved Lennard-Jones (ILJ) potentials. We then analyzed the growth pattern as well as the energetics of adsorption using the parametrized potentials, in combination with the PSO technique, which enabled us to predict the best possible membrane for the adsorption of the noble gases: α-GY for Ne and γ-GY for Ar and Kr. The accuracy of our modeling approach is further validated against DFT-D computations thereby establishing that PSO, when combined with the ILJ potential, can serve as a computationally feasible approach for modeling gas adsorption on GYs.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100048"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S294974772400006X/pdfft?md5=13e8fd3ef313b8180bab9c56f7c85352&pid=1-s2.0-S294974772400006X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139504245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unveiling the impact of axial ligands on Fe-N-C complexes through DFT simulation and machine learning analysis","authors":"Hong-Yi Wang,&nbsp;Jirui Jin,&nbsp;Mingjie Liu","doi":"10.1016/j.aichem.2023.100041","DOIUrl":"https://doi.org/10.1016/j.aichem.2023.100041","url":null,"abstract":"<div><p>Single-atom catalysts (SACs), featuring isolated metal atoms embedded in graphitic carbon materials, have attracted considerable research interest due to their cost-effectiveness, high catalytic activity, and customizable functionality across various catalytic reactions. Among SACs, the Fe-N₄-C class has garnered significant attention. Tailoring the properties of Fe-N₄ sites through localized chemical modifications stands as a key strategy for catalyst engineering. Recent experimental and computational investigations have underscored the distinct influence of axial ligands on Fe in modulating the oxygen reduction reaction (ORR) activity. However, the precise quantitative structure-property relationship between ligands and the catalytic properties of the Fe center remains elusive. In this study, we combined the density functional theory (DFT) simulations and machine learning (ML) models to unravel the relationship between the ligand properties and the oxo binding energy. This energy pertains to the binding of an oxygen atom to the Fe center, a fundamental step in ORR. Through the design of 33 ligands and 5 molecular complexes that accommodate the Fe-N₄ moiety, we screened a total of 278 oxo binding energies across an array of ligands and host complexes. Harnessing the power of ML models, we achieved an accurate prediction of these oxo binding energies using features collected from DFT simulations. Notably, the predominant features contributing to the oxo binding energy prediction primarily derived from complexes with attached ligands, rather than isolated ligand properties. We formulated an approach that leverages these critical features and identified the isolated ligand properties capable of effectively predicting these features. This methodology can potentially be applied to investigate other ORR intermediates and a comprehensive understanding of the ligand effect for the ORR activity in SACs can be achieved.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100041"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747723000416/pdfft?md5=a1922204ca0ef56a175357a9c2778026&pid=1-s2.0-S2949747723000416-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139107525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Empowering research in chemistry and materials science through intelligent algorithms","authors":"Jinglong Lin ,&nbsp;Fanyang Mo","doi":"10.1016/j.aichem.2023.100035","DOIUrl":"https://doi.org/10.1016/j.aichem.2023.100035","url":null,"abstract":"<div><p>In this review, we explore the integration of intelligent algorithms in chemistry and materials science.We begin by delineating the core principles of Machine Learning, Deep Learning, and optimization algorithms, highlighting their bespoke adaptation to these scientific domains. The focus then shifts to the critical processes of data management, including collection, refinement, and feature engineering, alongside strategies for efficient data mining from targeted databases and literatures. Subsequently, we present a concise overview of the diverse applications of these algorithms, emphasizing their transformative impact in both fields. Finally, this review explores the future prospects and challenges of these emerging algorithms.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100035"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747723000350/pdfft?md5=f73da155cd3c387fc723aa1852c198dc&pid=1-s2.0-S2949747723000350-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138838795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning for determination of activity of water and activity coefficients of electrolytes in binary solutions","authors":"Guillaume Zante","doi":"10.1016/j.aichem.2024.100069","DOIUrl":"https://doi.org/10.1016/j.aichem.2024.100069","url":null,"abstract":"<div><p>Activity of water and electrolytes in aqueous solutions is of utmost importance for multiple industrial applications. However, experimental determination of such values is time-consuming, while calculation of activity coefficients using numerical methods is challenging. By training neural networks models on literature data, one could predict activity of water and electrolytes easily, without requiring any experiment. In this paper, multiple descriptors (or features) are compared to predict activity coefficients of electrolytes and activity of water in electrolyte solutions. A neural network based on the Levenberg-Marquardt algorithm (LM-NN) showed high accuracy to calculate values, despite the small size of the training datasets. Both activity coefficients of electrolytes and activity of water in electrolyte solutions can be predicted accurately even on unseen data, using simple descriptors such as electrolyte concentration, ion sizes and charges. However, some discrepancies were observed due to the lack of representativeness of the training dataset. This could be solved by selecting training data sets that are similar (<em>e.g.</em> same group of the periodic table) to the unknown values, or by including available experimental data for the salt considered. The ability of the LM-NN to solve non-linear least square curve fitting problems makes it a good candidate to fit experimental activity coefficient data, with the advantage of simplicity as compared to e-NRTL or UNIQUAC methods. This method paves the way for accurate and quick determination of thermodynamic data for electrolyte solutions (and beyond) using machine learning, without necessitating large training datasets.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100069"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000277/pdfft?md5=e459d6b44dc65480007e2791c28b76ea&pid=1-s2.0-S2949747724000277-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140825119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adsorption kinetics of H2O on graphene surface based on a new potential energy surface","authors":"Jun Chen ,&nbsp;Tan Jin ,&nbsp;Zhe-Ning Chen ,&nbsp;Chong Liu ,&nbsp;Wei Zhuang","doi":"10.1016/j.aichem.2024.100046","DOIUrl":"https://doi.org/10.1016/j.aichem.2024.100046","url":null,"abstract":"<div><p>The interaction between water and graphene is important for understanding the thermodynamic and kinetic properties of water on hydrophobic surfaces. In this study, we constructed a high-dimensional potential energy surface (PES) for the water-graphene system using the many-body expansion scheme and neural network fitting. By analyzing the landscape of the PES, we found that the water molecule exhibits a weak physisorption behavior with a binding energy of about − 1000 cm⁻¹ and a very low diffusion barrier. Furthermore, extensive molecular dynamics were performed to investigate the adsorption and diffusion dynamics of a single water on a graphene surface at temperatures ranging from 50 to 300 K. Potential-of-mean-forces were computed from the trajectories, providing a comprehensive and accurate description of the water-graphene interaction kinetics.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100046"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000046/pdfft?md5=3509ee6529315b58646877b33b98b477&pid=1-s2.0-S2949747724000046-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139433815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Synchrotron radiation data-driven artificial intelligence approaches in materials discovery","authors":"Qingmeng Li ,&nbsp;Rongchang Xing ,&nbsp;Linshan Li ,&nbsp;Haodong Yao ,&nbsp;Liyuan Wu ,&nbsp;Lina Zhao","doi":"10.1016/j.aichem.2024.100045","DOIUrl":"10.1016/j.aichem.2024.100045","url":null,"abstract":"<div><p>Synchrotron radiation technology provides high-resolution and high-sensitivity information for many fields such as material science, life science, and energy research. Synchrotron radiation data-driven methods have significantly accelerated the development of materials discovery and analysis. However, synchrotron radiation data is complex and large, requiring artificial intelligence for analysis. Artificial intelligence can efficiently process complex high-dimensional data, automate the analysis process, discover hidden patterns and associations, and build predictive models. This review provides an overview of the application and development of combining synchrotron radiation data-driven methods with artificial intelligence in the field of materials discovery. The application of the method in science is still limited by the problems of large and complex synchrotron radiation data, valuable experimental machine time, and uninterpretable artificial intelligence models. To address these problems, this review correspondingly proposes solutions for synchrotron radiation artificial intelligence data banks, standardized experiment records systems, and interpretable artificial intelligence predictive models.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100045"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000034/pdfft?md5=ce78a666fc4929f98343bbd4a363d8f9&pid=1-s2.0-S2949747724000034-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139458271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}