化学•材料最新文献

{"title":"Coexistence of Ferroelectricity and Antiferromagnetism in Manganese-Based Hybrid Organic–Inorganic Perovskites","authors":"Qian-Xia Chen, Jin-Zhu Zhao","doi":"10.1021/acs.jpcc.4c08490","DOIUrl":"https://doi.org/10.1021/acs.jpcc.4c08490","url":null,"abstract":"Hybrid organic–inorganic perovskites (HOIPs) are attractive for the application of functional materials that provide multiple degrees of freedom for ferro orders. By employing first-principles calculations, we propose the coexistence of ferroelectric and antiferromagnetic orders in AMnX₃-type hybrid organic–inorganic perovskites (HOIPs) (A = NH₄⁺, CH₃NH₃⁺, and CH₃CH₂NH₃⁺, X= Cl, Br, I), which are distinguished from conventional inorganic perovskites, where the magnetic and ferroelectric orders often suppress each other. On the one hand, we show that the antiferroelectric magnetic order originates from the Mn sublattice. On the other hand, the spontaneous ferroelectric order in AMnX₃-type HOIPs may have different origins depending on the size of the A-site organic molecules and are strongly affected by their interaction with the inorganic lattice frame. This work provides a promising strategy for subsequent investigations and applications of multiferroic materials.","PeriodicalId":61,"journal":{"name":"The Journal of Physical Chemistry C","volume":"15 1","pages":""},"PeriodicalIF":4.126,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143518224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging Galvanic Exchange for the Formation of Shaped Metal Nanoparticles Comprising Non-Noble Metals","authors":"Chathura B. Wijethunga, Melissa E. King","doi":"10.1021/acs.jpcc.4c08823","DOIUrl":"https://doi.org/10.1021/acs.jpcc.4c08823","url":null,"abstract":"Colloidally synthesized nanoparticles are typically composed of noble metals due to their ease of use, which limits the development of tailorable catalytic materials. Non-noble metals are employed effectively as catalysts at the industrial scale, and their inclusion in nanoscale catalyst development has the potential to expand relevant pairings significantly. The inclusion of non-noble, crustally abundant metals, however, presents unique challenges including self-oxidation and negative reduction potentials, which limits the utility of conventional synthetic methods. Fundamental insights are critical to the expansion of available metals used in the formation of nanoparticles to include non-noble metals. This work highlights two key protocols for the synthesis of monodisperse spherical nickel/nickel oxide nanoparticles of tailorable sizes and subsequent transformation via galvanic exchange with copper ions to produce cubic and cuboctahedral copper oxide nanoparticles as well as controllable methods for the formation of bimetallic nickel–copper nanostructures. This innovative approach takes place in nanopure water at room temperature with no surfactants as a result of the thermodynamically favorable conditions. Additionally, the formation of shaped nanoparticles from a spherical nanoparticle via galvanic exchange is significantly different from that of known syntheses. This work provides critical insights into the strategic coupling of non-noble metals and elucidates a method to leverage galvanic exchange reactions in non-noble metal systems.","PeriodicalId":61,"journal":{"name":"The Journal of Physical Chemistry C","volume":"3 1","pages":""},"PeriodicalIF":4.126,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143518228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MOF-KAN: Kolmogorov–Arnold Networks for Digital Discovery of Metal–Organic Frameworks","authors":"Xiaoyu Wu, Xianyu Song, Yifei Yue, Rui Zheng, Jianwen Jiang","doi":"10.1021/acs.jpclett.5c00211","DOIUrl":"https://doi.org/10.1021/acs.jpclett.5c00211","url":null,"abstract":"Digital discovery of functional materials, such as metal–organic frameworks (MOFs), entails accurate and data-efficient approaches to navigate complex chemical and structural space. Based on an innovative deep learning approach, namely, Kolmogorov–Arnold Networks (KANs), we introduce MOF-KAN, a state-of-the-art architecture as the first application of KANs to digital discovery of MOFs. Through meticulous fine-tuning of network architecture, we demonstrate that MOF-KAN outperforms standard multilayer perceptrons (MLPs) in predicting diverse properties for MOFs, including gas separation, electronic band gap, and thermal expansion. Furthermore, MOF-KAN excels in low-data regimes, facilitating robust performance in challenging prediction scenarios. Feature importance analysis reveals that MOF-KAN accurately captures critical features of MOFs relevant to targeted properties. MOF-KAN not only serves as a transformative tool for the rational design of functional materials but also holds broad applicability across various domains in physical sciences.","PeriodicalId":62,"journal":{"name":"The Journal of Physical Chemistry Letters","volume":"16 26 1","pages":""},"PeriodicalIF":6.475,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143518373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Palladium-Catalyzed α-Arylation of Sulfoxonium Ylides with Aryl Thianthrenium Salts via C-S and C-H Bond Activation.","authors":"Qing-Dong Wang, Xue Chen, Yuan-Shuai Wu, Chengping Miao, Jin-Ming Yang, Zhi-Liang Shen","doi":"10.1002/asia.202401873","DOIUrl":"https://doi.org/10.1002/asia.202401873","url":null,"abstract":"<p><p>Diverse α-aryl α-carbonyl sulfoxonium ylides were efficiently synthesized in yields ranging from moderate to high via a palladium-catalyzed α-arylation of sulfoxonium ylides with aryl thianthrenium salts. The reactions proceeded smoothly via C-S and C-H bond functionalization, exhibiting broad substrate scope and good compatibility to various functionalities. In addition, the scale-up synthesis could be achieved, and the one-pot protocol commencing from the use of simple arene as the precursor of aryl thianthrenium salt could also be accomplished.</p>","PeriodicalId":145,"journal":{"name":"Chemistry - An Asian Journal","volume":" ","pages":"e202401873"},"PeriodicalIF":3.5,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143522219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting Toxicity toward Nitrifiers by Attention-Enhanced Graph Neural Networks and Transfer Learning from Baseline Toxicity","authors":"Kunyang Zhang, Philippe Schwaller, Kathrin Fenner","doi":"10.1021/acs.est.4c12247","DOIUrl":"https://doi.org/10.1021/acs.est.4c12247","url":null,"abstract":"Assessing chemical environmental impacts is critical but challenging due to the time-consuming nature of experimental testing. Graph neural networks (GNNs) support superior prediction performance and mechanistic interpretation of (eco-)toxicity data, but face the risk of overfitting on the typically small experimental data sets. In contrast to purely data-driven approaches, we propose a mechanism-guided transfer learning strategy that is highly efficient and provides key insights into the underlying drivers of (eco-)toxicity. By leveraging the mechanistic link between baseline toxicity and toxicity toward nitrifiers, we pretrained a GNN on lipophilicity data (log P) and subsequently fine-tuned it on the limited data set of toxicity toward nitrifiers, achieving prediction performance comparable with pretraining on much larger but mechanistically less relevant data sets. Additionally, we enhanced GNN interpretability by adjusting multihead attentions after convolutional layers to identify key substructures, and quantified their contributions using a Shapley Value method adapted for graph-structured data with improved computational efficiency. The highlighted substructures aligned well with and effectively distinguished known structural alerts for baseline toxicity and specific modes of toxic action in nitrifiers. The proposed strategy will allow uncovering new structural alerts in other (eco)toxicity data, and thus foster new mechanistic insights to support chemical risk assessment and safe-by-design principles.","PeriodicalId":36,"journal":{"name":"环境科学与技术","volume":"26 1","pages":""},"PeriodicalIF":9.028,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143507297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transformer-Based Models for Predicting Molecular Structures from Infrared Spectra Using Patch-Based Self-Attention.","authors":"Wenjin Wu, Aleš Leonardis, Jianbo Jiao, Jun Jiang, Linjiang Chen","doi":"10.1021/acs.jpca.4c05665","DOIUrl":"10.1021/acs.jpca.4c05665","url":null,"abstract":"<p><p>Infrared (IR) spectroscopy, a type of vibrational spectroscopy, provides extensive molecular structure details and is a highly effective technique for chemists to determine molecular structures. However, analyzing experimental spectra has always been challenging due to the specialized knowledge required and the variability of spectra under different experimental conditions. Here, we propose a transformer-based model with a patch-based self-attention spectrum embedding layer, designed to prevent the loss of spectral information while maintaining simplicity and effectiveness. To further enhance the model's understanding of IR spectra, we introduce a data augmentation approach, which selectively introduces vertical noise only at absorption peaks. Our approach not only achieves state-of-the-art performance on simulated data sets but also attains a top-1 accuracy of 55% on real experimental spectra, surpassing the previous state-of-the-art by approximately 10%. Additionally, our model demonstrates proficiency in analyzing intricate and variable fingerprint regions, effectively extracting critical structural information.</p>","PeriodicalId":59,"journal":{"name":"The Journal of Physical Chemistry A","volume":" ","pages":"2077-2085"},"PeriodicalIF":2.7,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143416855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting the Electron Density of Charged Systems Using Machine Learning.","authors":"Sherif Abdulkader Tawfik, Sunil Gupta, Svetha Venkatesh","doi":"10.1021/acs.jpca.4c08583","DOIUrl":"10.1021/acs.jpca.4c08583","url":null,"abstract":"<p><p>The prediction of the electron density in molecules and crystals is a key pillar in the first-principles computation of their properties. Using machine learning to predict the electron density by using the atomic structure alone can save the computational cost of performing first-principles computations. While various machine learning approaches have been introduced for predicting the electron density, none of them predict the electron density for charged systems. This work extends a recent machine learning charge density model, DeepDFT, by including the charge of the structure as an input parameter into the model. We establish an input charge representation approach that successfully predicts the charged electron densities for several test cases, including charged defective perovskites, LiCoO₂ supercells with multiple Li vacancies, diamond-based defects, metal-organic frameworks, and molecular crystals.</p>","PeriodicalId":59,"journal":{"name":"The Journal of Physical Chemistry A","volume":" ","pages":"2117-2122"},"PeriodicalIF":2.7,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143412411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modulation of Photophysical Properties of N-Confused Hexaphyrins through Carbon-Metal Bonding and Structural Modifications─A Theoretical Insight.","authors":"Longxiang Wang, Jianming Pan, Ru Feng, Hiroyuki Furuta, Yue Wang","doi":"10.1021/acs.jpca.4c07751","DOIUrl":"10.1021/acs.jpca.4c07751","url":null,"abstract":"<p><p>To understand the influence of N-confusion and C-M bonding on the absorption characteristics of expanded metalloporphyrins, the structural, electronic, and optical properties of N-confused hexaphyrin(1.1.1.1.1.1) bis-metal complexes (<b>7-PdAu</b> and <b>7-PtAu</b>) were investigated by employing density functional theory (DFT) and time-dependent DFT (TD-DFT) calculations. Our findings demonstrate that forming C-M bonds leads to a saddle-shaped hexaphyrin structure, enhancing the metal-ligand interaction compared to O-M bonds. This structural alteration results in reduced aromaticity and a narrowing of the HOMO-LUMO gap, along with a significant bathochromic shift in the electronic absorption spectrum. Notably, the <b>7-PdAu</b> and <b>7-PtAu</b> complexes exhibit pronounced absorption bands beyond 1100 nm, indicating their potential as candidates for near-infrared (NIR) phototherapeutic and optoelectronic applications. Overall, this work underscores the synergistic effects of N-confusion and carbon-metal bonding in tuning the photophysical properties of porphyrin-based systems, paving the way for advanced applications in photonics.</p>","PeriodicalId":59,"journal":{"name":"The Journal of Physical Chemistry A","volume":" ","pages":"1972-1982"},"PeriodicalIF":2.7,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143397611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Fundamental Ultrafast Spectroscopic Insight into Urocanic Acid Derivatives.","authors":"Jack Dalton, Hans Sanders, Wybren Jan Buma, Vasilios G Stavros","doi":"10.1021/acs.jpclett.5c00137","DOIUrl":"10.1021/acs.jpclett.5c00137","url":null,"abstract":"<p><p><i>trans</i>-Urocanic acid (UA) was once thought to be an ideal natural UV sunscreen filter because of its strong UV absorption and efficient nonradiative decay, in addition to its natural presence in human skin. However, its commercial use was abandoned following the discovery of the immunosuppressive properties of the <i>cis</i> isomer formed following photoexcitation. From the extensive literature accumulated over the past decades, UA serves as a perfect scaffold for developing next-generation nature-inspired UV filters by eliminating the immunosuppressive characteristics and retaining the favorable photophysics. Here, gas-phase time-resolved ion-yield and time-resolved photoelectron spectroscopy are combined to uncover the fundamental ultrafast photodynamics of three UA derivatives. We find that minor molecular adjustments between derivatives have considerable influence on the overall excited state behavior, leading to different relaxation mechanisms and lifetimes. These findings establish foundations for further molecular design and aid in the interpretation of these derivatives under complex environmental conditions.</p>","PeriodicalId":62,"journal":{"name":"The Journal of Physical Chemistry Letters","volume":" ","pages":"2016-2022"},"PeriodicalIF":4.8,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143447509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Overview of Developments in the MRCC Program System.","authors":"Dávid Mester, Péter R Nagy, József Csóka, László Gyevi-Nagy, P Bernát Szabó, Réka A Horváth, Klára Petrov, Bence Hégely, Bence Ladóczki, Gyula Samu, Balázs D Lőrincz, Mihály Kállay","doi":"10.1021/acs.jpca.4c07807","DOIUrl":"10.1021/acs.jpca.4c07807","url":null,"abstract":"<p><p>mrcc is a versatile suite of quantum chemistry programs designed for accurate <i>ab initio</i> and density functional theory (DFT) calculations. This contribution outlines the general features and recent developments of the package. The most popular features include the open-ended coupled-cluster (CC) code, state-of-the-art CC singles and doubles with perturbative triples [CCSD(T)], second-order algebraic-diagrammatic construction, and combined wave function theory-DFT approaches. Cost-reduction techniques are implemented, such as natural orbital (NO), local NO (LNO), and natural auxiliary function approximations, which significantly decrease the computational demands of these methods. This paper also details the method developments made over the past five years, including efficient schemes to approach the complete basis set limit for CCSD(T) and the extension of our LNO-CCSD(T) method to open-shell systems. Additionally, we discuss the new approximations introduced to accelerate the self-consistent field procedure and the cost-reduction techniques elaborated for analytic gradient calculations at various levels. Furthermore, embedding techniques and novel range-separated double-hybrid functionals are presented for excited-state calculations, while the extension of the theories established to describe core excitations and ionized states is also discussed. For academic purposes, the program and its source code are available free of charge, and its commercial use is also facilitated.</p>","PeriodicalId":59,"journal":{"name":"The Journal of Physical Chemistry A","volume":" ","pages":"2086-2107"},"PeriodicalIF":2.7,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143431981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}