1
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Cheng J, Ati AH, Kawazoe Y, Sun Q. Introducing Noble Gas as Space Holder under High Pressure to Design Porous Titanium Carbides with Open Metal Sites for Hydrogen Storage at Near-Ambient Conditions. J Am Chem Soc 2024. [PMID: 39172081 DOI: 10.1021/jacs.4c07772] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/23/2024]
Abstract
It has been a long-standing challenge to develop high-performance solid-state hydrogen storage materials operated under near-ambient conditions. In this work, we propose a new strategy of using noble gases for space holding to design porous titanium carbides with abundant open metal sites for hydrogen storage. By using machine learning and graph theory-assisted universal structure searching methods, we obtain 28 porous titanium carbides from three precursors (TiC dimer, C atom, and Kr atom) under 30 GPa of pressure. The stability and hydrogen storage performance of the resulting structures are further assessed and validated through density function theory and grand canonical Monte Carlo simulations with a DFT-fitted force field. Finally, p-TiC2 is identified as a promising quasi-molecular hydrogen storage material with capacity of 4.0 wt % and 106.0 g/L at 230 K and 16 bar.
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Affiliation(s)
- Jiewei Cheng
- School of Materials Science and Engineering, Peking University, Beijing 100871, China
| | - Ahmed H Ati
- School of Materials Science and Engineering, Peking University, Beijing 100871, China
| | - Yoshiyuki Kawazoe
- New Industry Creation Hatchery Center, Tohoku University, Sendai 980-8577, Japan
- School of Physics, Institute of Science, Suranaree University of Technology, 111 University Avenue, Nakhon Ratchasima 30000, Thailand
- Physics and Nanotechnology, SRM Institute of Science and Technology, Kattankurathur, Tamil Nadu 603203, India
| | - Qiang Sun
- School of Materials Science and Engineering, Peking University, Beijing 100871, China
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2
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Wang Z, Wu Y, Zhang Z, Sheng X, Fang S, Liu Y, Gong Y, Wang M, Song N, Huang F. A Pillar[5]arene-Containing Metal-Organic Framework for Rapid and Highly Capable Adsorption of a Mustard Gas Simulant. J Am Chem Soc 2024; 146:23330-23337. [PMID: 39110895 DOI: 10.1021/jacs.4c06061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/22/2024]
Abstract
Mustard gas causes irreversible damage upon inhalation or contact with the human body. Consequently, the development of adsorbents for effective interception of mustard gas at low concentrations and high flow rates is an urgent necessity. Here we report a stable porous pillar[5]arene-containing metal-organic framework (MOF) based on zirconium (EtP5-Zr-scu), demonstrating that embedding pillar[5]arene units in MOFs can provide specific binding sites for efficient adsorption of a mustard gas simulant, 2-chloroethyl ethyl sulfide (CEES). EtP5-Zr-scu achieves a higher capacity and more rapid adsorption compared to its counterpart without embedded pillar[5]arene units (H4tcpt-Zr-scu) and perethylated pillar[5]arene (EtP5) alone. Single crystal X-ray diffraction and solid-state nuclear magnetic resonance reveal that the enhanced performance of EtP5-Zr-scu is derived from the host-guest complexation between CEES and pillar[5]arene moieties. Moreover, breakthrough experiments confirmed that the interception performance of EtP5-Zr-scu against CEES (800 ppm, 120 mL/min) was significantly improved (566 min/g) compared with H4tcpt-Zr-scu (353 min/g) and EtP5 (0.873 min/g), attributed to the integration of open channels with specific recognition sites. This work marks a significant advancement in the development of macrocycle-incorporated crystalline framework materials with recognition sites for the efficient capture of guest molecules.
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Affiliation(s)
- Zeju Wang
- Stoddart Institute of Molecular Science, Department of Chemistry, Zhejiang University, Hangzhou 310058, P. R. China
- Zhejiang-Israel Joint Laboratory of Self-Assembling Functional Materials, ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou 311215, P. R. China
| | - Yitao Wu
- Stoddart Institute of Molecular Science, Department of Chemistry, Zhejiang University, Hangzhou 310058, P. R. China
| | - Zhenguo Zhang
- Stoddart Institute of Molecular Science, Department of Chemistry, Zhejiang University, Hangzhou 310058, P. R. China
| | - Xinru Sheng
- Stoddart Institute of Molecular Science, Department of Chemistry, Zhejiang University, Hangzhou 310058, P. R. China
| | - Shuai Fang
- Stoddart Institute of Molecular Science, Department of Chemistry, Zhejiang University, Hangzhou 310058, P. R. China
| | - Yang Liu
- Stoddart Institute of Molecular Science, Department of Chemistry, Zhejiang University, Hangzhou 310058, P. R. China
| | - Yide Gong
- Stoddart Institute of Molecular Science, Department of Chemistry, Zhejiang University, Hangzhou 310058, P. R. China
| | - Mengbin Wang
- College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, P. R. China
| | - Nan Song
- College of Chemistry and Chemical Engineering, Lanzhou University, Lanzhou 730000, P. R. China
| | - Feihe Huang
- Stoddart Institute of Molecular Science, Department of Chemistry, Zhejiang University, Hangzhou 310058, P. R. China
- Zhejiang-Israel Joint Laboratory of Self-Assembling Functional Materials, ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou 311215, P. R. China
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3
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Su Y, Wang X, Ye Y, Xie Y, Xu Y, Jiang Y, Wang C. Automation and machine learning augmented by large language models in a catalysis study. Chem Sci 2024; 15:12200-12233. [PMID: 39118602 PMCID: PMC11304797 DOI: 10.1039/d3sc07012c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Accepted: 06/21/2024] [Indexed: 08/10/2024] Open
Abstract
Recent advancements in artificial intelligence and automation are transforming catalyst discovery and design from traditional trial-and-error manual mode into intelligent, high-throughput digital methodologies. This transformation is driven by four key components, including high-throughput information extraction, automated robotic experimentation, real-time feedback for iterative optimization, and interpretable machine learning for generating new knowledge. These innovations have given rise to the development of self-driving labs and significantly accelerated materials research. Over the past two years, the emergence of large language models (LLMs) has added a new dimension to this field, providing unprecedented flexibility in information integration, decision-making, and interacting with human researchers. This review explores how LLMs are reshaping catalyst design, heralding a revolutionary change in the fields.
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Affiliation(s)
- Yuming Su
- iChem, State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Xiamen University Xiamen 361005 P. R. China
- Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM) Xiamen 361005 P. R. China
| | - Xue Wang
- iChem, State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Xiamen University Xiamen 361005 P. R. China
| | - Yuanxiang Ye
- Institute of Artificial Intelligence, Xiamen University Xiamen 361005 P. R. China
| | - Yibo Xie
- Institute of Artificial Intelligence, Xiamen University Xiamen 361005 P. R. China
| | - Yujing Xu
- iChem, State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Xiamen University Xiamen 361005 P. R. China
| | - Yibin Jiang
- Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM) Xiamen 361005 P. R. China
| | - Cheng Wang
- iChem, State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Xiamen University Xiamen 361005 P. R. China
- Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM) Xiamen 361005 P. R. China
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4
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Zhang Q, Hu Y, Yan J, Zhang H, Xie X, Zhu J, Li H, Niu X, Li L, Sun Y, Hu W. Large-Language-Model-Based AI Agent for Organic Semiconductor Device Research. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2405163. [PMID: 38816034 DOI: 10.1002/adma.202405163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Revised: 05/23/2024] [Indexed: 06/01/2024]
Abstract
Large language models (LLMs) have attracted widespread attention recently, however, their application in specialized scientific fields still requires deep adaptation. Here, an artificial intelligence (AI) agent for organic field-effect transistors (OFETs) is designed by integrating the generative pre-trained transformer 4 (GPT-4) model with well-trained machine learning (ML) algorithms. It can efficiently extract the experimental parameters of OFETs from scientific literature and reshape them into a structured database, achieving precision and recall rates both exceeding 92%. Combined with well-trained ML models, this AI agent can further provide targeted guidance and suggestions for device design. With prompt engineering and human-in-loop strategies, the agent extracts sufficient information of 709 OFETs from 277 research articles across different publishers and gathers them into a standardized database containing more than 10 000 device parameters. Using this database, a ML model based on Extreme Gradient Boosting is trained for device performance judgment. Combined with the interpretation of the high-precision model, the agent has provided a feasible optimization scheme that has tripled the charge transport properties of 2,6-diphenyldithieno[3,2-b:2',3'-d]thiophene OFETs. This work is an effective practice of LLMs in the field of organic optoelectronic devices and expands the research paradigm of organic optoelectronic materials and devices.
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Affiliation(s)
- Qian Zhang
- Key Laboratory of Organic Integrated Circuits, Ministry of Education and Tianjin Key Laboratory of Molecular Optoelectronic Sciences, Department of Chemistry, School of Science, Tianjin University, Tianjin, 300072, China
- Haihe Lab of ITAI, Tianjin, 300051, China
| | - Yongxu Hu
- Key Laboratory of Organic Integrated Circuits, Ministry of Education and Tianjin Key Laboratory of Molecular Optoelectronic Sciences, Department of Chemistry, School of Science, Tianjin University, Tianjin, 300072, China
| | - Jiaxin Yan
- Key Laboratory of Organic Integrated Circuits, Ministry of Education and Tianjin Key Laboratory of Molecular Optoelectronic Sciences, Department of Chemistry, School of Science, Tianjin University, Tianjin, 300072, China
- Haihe Lab of ITAI, Tianjin, 300051, China
| | - Hengyue Zhang
- Key Laboratory of Organic Integrated Circuits, Ministry of Education and Tianjin Key Laboratory of Molecular Optoelectronic Sciences, Department of Chemistry, School of Science, Tianjin University, Tianjin, 300072, China
- Haihe Lab of ITAI, Tianjin, 300051, China
| | - Xinyi Xie
- Key Laboratory of Organic Integrated Circuits, Ministry of Education and Tianjin Key Laboratory of Molecular Optoelectronic Sciences, Department of Chemistry, School of Science, Tianjin University, Tianjin, 300072, China
| | - Jie Zhu
- Key Laboratory of Organic Integrated Circuits, Ministry of Education and Tianjin Key Laboratory of Molecular Optoelectronic Sciences, Department of Chemistry, School of Science, Tianjin University, Tianjin, 300072, China
- Haihe Lab of ITAI, Tianjin, 300051, China
| | - Huchao Li
- Key Laboratory of Organic Integrated Circuits, Ministry of Education and Tianjin Key Laboratory of Molecular Optoelectronic Sciences, Department of Chemistry, School of Science, Tianjin University, Tianjin, 300072, China
| | - Xinxin Niu
- Key Laboratory of Organic Integrated Circuits, Ministry of Education and Tianjin Key Laboratory of Molecular Optoelectronic Sciences, Department of Chemistry, School of Science, Tianjin University, Tianjin, 300072, China
- Haihe Lab of ITAI, Tianjin, 300051, China
| | - Liqiang Li
- Key Laboratory of Organic Integrated Circuits, Ministry of Education and Tianjin Key Laboratory of Molecular Optoelectronic Sciences, Department of Chemistry, School of Science, Tianjin University, Tianjin, 300072, China
| | - Yajing Sun
- Key Laboratory of Organic Integrated Circuits, Ministry of Education and Tianjin Key Laboratory of Molecular Optoelectronic Sciences, Department of Chemistry, School of Science, Tianjin University, Tianjin, 300072, China
- Haihe Lab of ITAI, Tianjin, 300051, China
| | - Wenping Hu
- Key Laboratory of Organic Integrated Circuits, Ministry of Education and Tianjin Key Laboratory of Molecular Optoelectronic Sciences, Department of Chemistry, School of Science, Tianjin University, Tianjin, 300072, China
- Joint School of National University of Singapore and Tianjin University, Fuzhou, Fujian, 350207, China
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5
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Kim S, Jung Y, Schrier J. Large Language Models for Inorganic Synthesis Predictions. J Am Chem Soc 2024; 146:19654-19659. [PMID: 38991051 DOI: 10.1021/jacs.4c05840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/13/2024]
Abstract
We evaluate the effectiveness of pretrained and fine-tuned large language models (LLMs) for predicting the synthesizability of inorganic compounds and the selection of precursors needed to perform inorganic synthesis. The predictions of fine-tuned LLMs are comparable to─and sometimes better than─recent bespoke machine learning models for these tasks but require only minimal user expertise, cost, and time to develop. Therefore, this strategy can serve both as an effective and strong baseline for future machine learning studies of various chemical applications and as a practical tool for experimental chemists.
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Affiliation(s)
- Seongmin Kim
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291, Daehak-ro, Yuseong-gu, Daejeon 34141, Korea
| | - Yousung Jung
- Department of Chemical and Biological Engineering (BK21 four), Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea
- Institute of Chemical Processes, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea
- Interdisciplinary Program in Artificial Intelligence, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea
- Institute of Engineering Research, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea
| | - Joshua Schrier
- Department of Chemistry and Biochemistry, Fordham University, 441 East Fordham Road, The Bronx, New York 10458, United States
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6
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Wahab MF, Handlovic TT, Roy S, Burk RJ, Armstrong DW. Solving Advanced Task-Specific Problems in Measurement Sciences with Generative AI. Anal Chem 2024. [PMID: 39017630 DOI: 10.1021/acs.analchem.4c01734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/18/2024]
Abstract
The Generative Pre-Trained Transformer known as ChatGPT-4 has undergone extensive pretraining on a diverse data set, enabling it to generate coherent and contextually relevant text based on the input it receives. This capability allows it to perform tasks from answering questions and has attracted significant interest in material sciences, synthetic chemistry, and drug discovery. In this work, we posed four advanced task-specific problems to ChatGPT, which were recently published in leading journals for topics in analytical chemistry, spectroscopy, bioimage super-resolution, and electrochemistry. ChatGPT-4 successfully implemented the four ideas after assigning the "persona" to the AI and posing targeted questions. We show two cases where "unguided" ChatGPT could complete the assignments with minimal human direction. The construction of a microwave spectrum from a free induction curve and super-resolution of bioimages was accomplished using this approach. Two other specific tasks, correcting a complex baseline with morphological operations of set theory and estimating the diffusion current, required additional input, e.g., equations and specific directions from the user. In each case, the MATLAB code was eventually generated by ChatGPT-4 even when the original authors did not provide any code themselves. We show that a validation test must be implemented to detect and correct mistakes made by ChatGPT-4, followed by feedback correction. These approaches will pave the way for open and transparent science and eliminate the black boxes in measurement science when it comes to advanced data processing.
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Affiliation(s)
- M Farooq Wahab
- Department of Chemistry & Biochemistry, University of Texas at Arlington, Arlington, Texas 76019, United States
| | - Troy T Handlovic
- Department of Chemistry & Biochemistry, University of Texas at Arlington, Arlington, Texas 76019, United States
| | - Souvik Roy
- Department of Mathematics, University of Texas at Arlington, Arlington, Texas 76019, United States
| | - Ryan Jacob Burk
- Department of Chemistry & Biochemistry, University of Texas at Arlington, Arlington, Texas 76019, United States
| | - Daniel W Armstrong
- Department of Chemistry & Biochemistry, University of Texas at Arlington, Arlington, Texas 76019, United States
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7
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Bai X, Xie Y, Zhang X, Han H, Li JR. Evaluation of Open-Source Large Language Models for Metal-Organic Frameworks Research. J Chem Inf Model 2024; 64:4958-4965. [PMID: 38529913 DOI: 10.1021/acs.jcim.4c00065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/27/2024]
Abstract
Along with the development of machine learning, deep learning, and large language models (LLMs) such as GPT-4 (GPT: Generative Pre-Trained Transformer), artificial intelligence (AI) tools have been playing an increasingly important role in chemical and material research to facilitate the material screening and design. Despite the exciting progress of GPT-4 based AI research assistance, open-source LLMs have not gained much attention from the scientific community. This work primarily focused on metal-organic frameworks (MOFs) as a subdomain of chemistry and evaluated six top-rated open-source LLMs with a comprehensive set of tasks including MOFs knowledge, basic chemistry knowledge, in-depth chemistry knowledge, knowledge extraction, database reading, predicting material property, experiment design, computational scripts generation, guiding experiment, data analysis, and paper polishing, which covers the basic units of MOFs research. In general, these LLMs were capable of most of the tasks. Especially, Llama2-7B and ChatGLM2-6B were found to perform particularly well with moderate computational resources. Additionally, the performance of different parameter versions of the same model was compared, which revealed the superior performance of higher parameter versions.
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Affiliation(s)
- Xuefeng Bai
- Beijing Key Laboratory for Green Catalysis and Separation and Department of Chemical Engineering, College of Materials Science & Engineering, Beijing University of Technology, Beijing 100124, P. R. China
| | - Yabo Xie
- Beijing Key Laboratory for Green Catalysis and Separation and Department of Chemical Engineering, College of Materials Science & Engineering, Beijing University of Technology, Beijing 100124, P. R. China
| | - Xin Zhang
- Beijing Key Laboratory for Green Catalysis and Separation and Department of Chemical Engineering, College of Materials Science & Engineering, Beijing University of Technology, Beijing 100124, P. R. China
| | - Honggui Han
- Engineering Research Center of Digital Community, Ministry of Education, Beijing Laboratory for Urban Mass Transit and Beijing Key Laboratory of Computational Intelligence and Intelligence System, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, P. R. China
| | - Jian-Rong Li
- Beijing Key Laboratory for Green Catalysis and Separation and Department of Chemical Engineering, College of Materials Science & Engineering, Beijing University of Technology, Beijing 100124, P. R. China
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8
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Pan Y, Shan X, Cai F, Gao H, Xu J, Zhou M. Accelerating the Discovery of Oxygen Reduction Electrocatalysts: High-Throughput Screening of Element Combinations in Pt-Based High-Entropy Alloys. Angew Chem Int Ed Engl 2024:e202407116. [PMID: 38934207 DOI: 10.1002/anie.202407116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Revised: 06/25/2024] [Accepted: 06/26/2024] [Indexed: 06/28/2024]
Abstract
The vast number of element combinations and the explosive growth of composition space pose significant challenges to the development of high-entropy alloys (HEAs). Here, we propose a procedural research method aimed at accelerating the discovery of efficient electrocatalysts for oxygen reduction reaction (ORR) based on Pt-based quinary HEAs. The method begins with an element library provided by a large language model (LLM), combined with microscale precursor printing and pulse high-temperature synthesis techniques to prepare multi-element combination HEA array in one step. Through high-throughput measurement using scanning electrochemical cell microscopy (SECCM), precise identification of highly active HEA element combinations and exploration of composition space for a specific combination are achieved. Advantageous element combinations are further validated in practical electrocatalytic evaluations. The contributions of individual element sites and the synergistic effects among elements of such HEAs in enhancing reaction activity are elucidated via density functional theory (DFT) calculations. This method integrates high-throughput experiments, practical catalyst validation, and DFT calculations, providing a new pathway for accelerating the discovery of efficient multi-element materials in the field of energy catalysis.
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Affiliation(s)
- Yiyang Pan
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, 130022, Changchun, Jilin, China
- School of Applied Chemistry and Engineering, University of Science and Technology of China, 230026, Hefei, Anhui, China
| | - Xiangyi Shan
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, 130022, Changchun, Jilin, China
- School of Applied Chemistry and Engineering, University of Science and Technology of China, 230026, Hefei, Anhui, China
| | - Furong Cai
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, 130022, Changchun, Jilin, China
| | - Han Gao
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, 130022, Changchun, Jilin, China
| | - Jianan Xu
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, 130022, Changchun, Jilin, China
| | - Min Zhou
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, 130022, Changchun, Jilin, China
- School of Applied Chemistry and Engineering, University of Science and Technology of China, 230026, Hefei, Anhui, China
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9
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Hardiagon A, Coudert FX. Multiscale Modeling of Physical Properties of Nanoporous Frameworks: Predicting Mechanical, Thermal, and Adsorption Behavior. Acc Chem Res 2024; 57:1620-1632. [PMID: 38752454 DOI: 10.1021/acs.accounts.4c00161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/18/2024]
Abstract
ConspectusNanoporous frameworks are a large and diverse family of supramolecular materials, whose chemical building units (organic, inorganic, or both) are assembled into a 3D architecture with well-defined connectivity and topology, featuring intrinsic porosity. These materials play a key role in various industrial processes and applications, such as energy production and conversion, fluid separation, gas storage, water harvesting, and many more. The performance and suitability of nanoporous materials for each specific application are directly related to both their physical and chemical properties, and their determination is crucial for process engineering and optimization of performances. In this Account, we focus on some recent developments in the multiscale modeling of physical properties of nanoporous frameworks, highlighting the latest advances in three specific areas: mechanical properties, thermal properties, and adsorption.In the study of the mechanical behavior of nanoporous materials, the past few years have seen a rapid acceleration of research. For example, computational resources have been pooled to create a public large-scale database of elastic constants as part of the Materials Project initiative to accelerate innovation in materials research: those can serve as a basis for data-based discovery of materials with targeted properties, as well as the training of machine learning predictor models.The large-scale prediction of thermal behavior, in comparison, is not yet routinely performed at such a large scale. Tentative databases have been assembled at the DFT level on specific families of materials, such as zeolites, but prediction at larger scale currently requires the use of transferable classical force fields, whose accuracy can be limited.Finally, adsorption is naturally one of the most studied physical properties of nanoporous frameworks, as fluid separation or storage is often the primary target for these materials. We highlight the recent achievements and open challenges for adsorption prediction at a large scale, focusing in particular on the accuracy of computational models and the reliability of comparisons with experimental data available. We detail some recent methodological improvements in the prediction of adsorption-related properties: in particular, we describe the recent research efforts to go beyond the study of thermodynamic quantities (uptake, adsorption enthalpy, and thermodynamic selectivity) and predict transport properties using data-based methods and high-throughput computational schemes. Finally, we stress the importance of data-based methods of addressing all sources of uncertainty.The Account concludes with some perspectives about the latest developments and open questions in data-based approaches and the integration of computational and experimental data together in the materials discovery loop.
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Affiliation(s)
- Arthur Hardiagon
- Chimie ParisTech, PSL University, CNRS, Institut de Recherche de Chimie Paris, 75005 Paris, France
| | - François-Xavier Coudert
- Chimie ParisTech, PSL University, CNRS, Institut de Recherche de Chimie Paris, 75005 Paris, France
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10
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Isari AA, Ghaffarkhah A, Hashemi SA, Wuttke S, Arjmand M. Structural Design for EMI Shielding: From Underlying Mechanisms to Common Pitfalls. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2310683. [PMID: 38467559 DOI: 10.1002/adma.202310683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 02/11/2024] [Indexed: 03/13/2024]
Abstract
Modern human civilization deeply relies on the rapid advancement of cutting-edge electronic systems that have revolutionized communication, education, aviation, and entertainment. However, the electromagnetic interference (EMI) generated by digital systems poses a significant threat to the society, potentially leading to a future crisis. While numerous efforts are made to develop nanotechnological shielding systems to mitigate the detrimental effects of EMI, there is limited focus on creating absorption-dominant shielding solutions. Achieving absorption-dominant EMI shields requires careful structural design engineering, starting from the smallest components and considering the most effective electromagnetic wave attenuating factors. This review offers a comprehensive overview of shielding structures, emphasizing the critical elements of absorption-dominant shielding design, shielding mechanisms, limitations of both traditional and nanotechnological EMI shields, and common misconceptions about the foundational principles of EMI shielding science. This systematic review serves as a scientific guide for designing shielding structures that prioritize absorption, highlighting an often-overlooked aspect of shielding science.
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Affiliation(s)
- Ali Akbar Isari
- Nanomaterials and Polymer Nanocomposites Laboratory, School of Engineering, University of British Columbia, Kelowna, BC, V1V 1V7, Canada
| | - Ahmadreza Ghaffarkhah
- Nanomaterials and Polymer Nanocomposites Laboratory, School of Engineering, University of British Columbia, Kelowna, BC, V1V 1V7, Canada
| | - Seyyed Alireza Hashemi
- Nanomaterials and Polymer Nanocomposites Laboratory, School of Engineering, University of British Columbia, Kelowna, BC, V1V 1V7, Canada
| | - Stefan Wuttke
- Basque Centre for Materials, Applications and Nanostructures (BCMaterials), Bld. Martina Casiano, 3rd. Floor UPV/EHU Science Park Barrio Sarriena s/n, Leioa, 48940, Spain
- IKERBASQUE, Basque Foundation for Science, Bilbao, 48013, Spain
| | - Mohammad Arjmand
- Nanomaterials and Polymer Nanocomposites Laboratory, School of Engineering, University of British Columbia, Kelowna, BC, V1V 1V7, Canada
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11
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Prieto T, Ponte C, Guntermann R, Medina DD, Salonen LM. Synthetic Strategies to Extended Aromatic Covalent Organic Frameworks. Chemistry 2024:e202401344. [PMID: 38771916 DOI: 10.1002/chem.202401344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Revised: 05/17/2024] [Accepted: 05/20/2024] [Indexed: 05/23/2024]
Abstract
π-Conjugated materials are highly attractive owing to their unique optical and electronic properties. Covalent organic frameworks (COFs) offer a great opportunity for precise arrangement of building units in a π-conjugated crystalline matrix and tuning of the properties through choice of functionalities or post-synthetic modification. With this review, we aim at summarizing both the most representative as well as emerging strategies for the synthesis of π-conjugated COFs. We give examples of direct synthesis using large, π-extended building blocks. COFs featuring fully conjugated linkages such as vinylene, pyrazine, and azole are discussed. Then, post-synthetic modification methods that result in the extension of the COF π-system are reviewed. Throughout, mechanistic insights are presented when available. In the context of their utilization as film devices, we conduct a concise survey of the prominent COF layer deposition techniques reported and their aptness for the deposition of fused aromatic systems.
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Affiliation(s)
- Tania Prieto
- CINBIO, Universidade de Vigo, Department of Organic Chemistry, 36310, Vigo, Spain
| | - Clara Ponte
- Nanochemistry Research Group, International Iberian Nanotechnology Laboratory (INL), Av. Mestre José Veiga, 4715-330, Braga, Portugal
- CICECO-Aveiro Institute of Materials, University of Aveiro, Campus Universitário de Santiago, 3810-193, Aveiro, Portugal
| | - Roman Guntermann
- Department of Chemistry and Center for NanoScience (CeNS), Ludwig Maximilians University (LMU), Butenandtstraße 11 (E), 81377, Munich, Germany
| | - Dana D Medina
- Department of Chemistry and Center for NanoScience (CeNS), Ludwig Maximilians University (LMU), Butenandtstraße 11 (E), 81377, Munich, Germany
| | - Laura M Salonen
- CINBIO, Universidade de Vigo, Department of Organic Chemistry, 36310, Vigo, Spain
- Nanochemistry Research Group, International Iberian Nanotechnology Laboratory (INL), Av. Mestre José Veiga, 4715-330, Braga, Portugal
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12
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Lu M, Gao F, Tang X, Chen L. Analysis and prediction in SCR experiments using GPT-4 with an effective chain-of-thought prompting strategy. iScience 2024; 27:109451. [PMID: 38523781 PMCID: PMC10960113 DOI: 10.1016/j.isci.2024.109451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 01/26/2024] [Accepted: 03/06/2024] [Indexed: 03/26/2024] Open
Abstract
This study explores the use of large language models (LLMs) in interpreting and predicting experimental outcomes based on given experimental variables, leveraging the human-like reasoning and inference capabilities of LLMs, using selective catalytic reduction of NOx with NH3 as a case study. We implement the chain of thought (CoT) concept to formulate logical steps for uncovering connections within the data, introducing an "Ordered-and-Structured" CoT (OSCoT) prompting strategy. We compare the OSCoT strategy with the more conventional "One-Pot" CoT (OPCoT) approach and with human experts. We demonstrate that GPT-4, equipped with this new OSCoT prompting strategy, outperforms the other two settings and accurately predicts experimental outcomes and provides intuitive reasoning for its predictions.
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Affiliation(s)
- Muyu Lu
- School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, P.R. China
| | - Fengyu Gao
- School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, P.R. China
| | - Xiaolong Tang
- School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, P.R. China
| | - Linjiang Chen
- School of Chemistry and School of Computer Science, University of Birmingham, Birmingham B15 2TT, UK
- Key Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China, Hefei, Anhui 230026, China
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13
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Wang Y, Jiang ZJ, Wang DR, Lu W, Li D. Machine Learning-Assisted Discovery of Propane-Selective Metal-Organic Frameworks. J Am Chem Soc 2024; 146:6955-6961. [PMID: 38422479 DOI: 10.1021/jacs.3c14610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/02/2024]
Abstract
Machine learning is gaining momentum in the prediction and discovery of materials for specific applications. Given the abundance of metal-organic frameworks (MOFs), computational screening of the existing MOFs for propane/propylene (C3H8/C3H6) separation could be equally important for developing new MOFs. Herein, we report a machine learning-assisted strategy for screening C3H8-selective MOFs from the CoRE MOF database. Among the four algorithms applied in machine learning, the random forest (RF) algorithm displays the highest degree of accuracy. We experimentally verified the identified top-performing MOF (JNU-90) with its benchmark selectivity and separation performance of directly producing C3H6. Considering its excellent hydrolytic stability, JNU-90 shows great promise in the energy-efficient separation of C3H8/C3H6. This work may accelerate the development of MOFs for challenging separations.
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Affiliation(s)
- Ying Wang
- College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Functional Supramolecular Coordination Materials and Applications, Jinan University, Guangzhou 510632, China
| | - Zhi-Jie Jiang
- College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Functional Supramolecular Coordination Materials and Applications, Jinan University, Guangzhou 510632, China
| | - Dong-Rong Wang
- College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Functional Supramolecular Coordination Materials and Applications, Jinan University, Guangzhou 510632, China
| | - Weigang Lu
- College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Functional Supramolecular Coordination Materials and Applications, Jinan University, Guangzhou 510632, China
| | - Dan Li
- College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Functional Supramolecular Coordination Materials and Applications, Jinan University, Guangzhou 510632, China
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14
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Zheng Z, Alawadhi AH, Chheda S, Neumann SE, Rampal N, Liu S, Nguyen HL, Lin YH, Rong Z, Siepmann JI, Gagliardi L, Anandkumar A, Borgs C, Chayes JT, Yaghi OM. Shaping the Water-Harvesting Behavior of Metal-Organic Frameworks Aided by Fine-Tuned GPT Models. J Am Chem Soc 2023; 145:28284-28295. [PMID: 38090755 DOI: 10.1021/jacs.3c12086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2023]
Abstract
We construct a data set of metal-organic framework (MOF) linkers and employ a fine-tuned GPT assistant to propose MOF linker designs by mutating and modifying the existing linker structures. This strategy allows the GPT model to learn the intricate language of chemistry in molecular representations, thereby achieving an enhanced accuracy in generating linker structures compared with its base models. Aiming to highlight the significance of linker design strategies in advancing the discovery of water-harvesting MOFs, we conducted a systematic MOF variant expansion upon state-of-the-art MOF-303 utilizing a multidimensional approach that integrates linker extension with multivariate tuning strategies. We synthesized a series of isoreticular aluminum MOFs, termed Long-Arm MOFs (LAMOF-1 to LAMOF-10), featuring linkers that bear various combinations of heteroatoms in their five-membered ring moiety, replacing pyrazole with either thiophene, furan, or thiazole rings or a combination of two. Beyond their consistent and robust architecture, as demonstrated by permanent porosity and thermal stability, the LAMOF series offers a generalizable synthesis strategy. Importantly, these 10 LAMOFs establish new benchmarks for water uptake (up to 0.64 g g-1) and operational humidity ranges (between 13 and 53%), thereby expanding the diversity of water-harvesting MOFs.
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Affiliation(s)
- Zhiling Zheng
- Department of Chemistry, University of California, Berkeley, California 94720, United States
- Kavli Energy Nanoscience Institute, University of California, Berkeley, California 94720, United States
- Bakar Institute of Digital Materials for the Planet, College of Computing, Data Science, and Society, University of California, Berkeley, California 94720, United States
| | - Ali H Alawadhi
- Department of Chemistry, University of California, Berkeley, California 94720, United States
- Kavli Energy Nanoscience Institute, University of California, Berkeley, California 94720, United States
| | - Saumil Chheda
- Department of Chemistry, University of California, Berkeley, California 94720, United States
- Kavli Energy Nanoscience Institute, University of California, Berkeley, California 94720, United States
- Bakar Institute of Digital Materials for the Planet, College of Computing, Data Science, and Society, University of California, Berkeley, California 94720, United States
- Department of Chemical Engineering and Materials Science, Department of Chemistry, and Chemical Theory Center, University of Minnesota─Twin Cities, Minneapolis, Minnesota 55455, United States
| | - S Ephraim Neumann
- Department of Chemistry, University of California, Berkeley, California 94720, United States
- Kavli Energy Nanoscience Institute, University of California, Berkeley, California 94720, United States
| | - Nakul Rampal
- Department of Chemistry, University of California, Berkeley, California 94720, United States
- Kavli Energy Nanoscience Institute, University of California, Berkeley, California 94720, United States
- Bakar Institute of Digital Materials for the Planet, College of Computing, Data Science, and Society, University of California, Berkeley, California 94720, United States
| | - Shengchao Liu
- Bakar Institute of Digital Materials for the Planet, College of Computing, Data Science, and Society, University of California, Berkeley, California 94720, United States
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California 94720, United States
| | - Ha L Nguyen
- Department of Chemistry, University of California, Berkeley, California 94720, United States
- Kavli Energy Nanoscience Institute, University of California, Berkeley, California 94720, United States
| | - Yen-Hsu Lin
- Department of Chemistry, University of California, Berkeley, California 94720, United States
- Kavli Energy Nanoscience Institute, University of California, Berkeley, California 94720, United States
| | - Zichao Rong
- Department of Chemistry, University of California, Berkeley, California 94720, United States
- Kavli Energy Nanoscience Institute, University of California, Berkeley, California 94720, United States
- Bakar Institute of Digital Materials for the Planet, College of Computing, Data Science, and Society, University of California, Berkeley, California 94720, United States
| | - J Ilja Siepmann
- Department of Chemical Engineering and Materials Science, Department of Chemistry, and Chemical Theory Center, University of Minnesota─Twin Cities, Minneapolis, Minnesota 55455, United States
| | - Laura Gagliardi
- Department of Chemistry, Pritzker School of Molecular Engineering, Chicago Center for Theoretical Chemistry, University of Chicago, Chicago, Illinois 60637, United States
| | - Anima Anandkumar
- Computing and Mathematical Sciences, California Institute of Technology, Pasadena, California 91125, United States
- NVIDIA Corporation, Santa Clara, California 95051, United States
| | - Christian Borgs
- Bakar Institute of Digital Materials for the Planet, College of Computing, Data Science, and Society, University of California, Berkeley, California 94720, United States
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California 94720, United States
| | - Jennifer T Chayes
- Bakar Institute of Digital Materials for the Planet, College of Computing, Data Science, and Society, University of California, Berkeley, California 94720, United States
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California 94720, United States
- Department of Mathematics, University of California, Berkeley, California 94720, United States
- Department of Statistics, University of California, Berkeley, California 94720, United States
- School of Information, University of California, Berkeley, California 94720, United States
| | - Omar M Yaghi
- Department of Chemistry, University of California, Berkeley, California 94720, United States
- Kavli Energy Nanoscience Institute, University of California, Berkeley, California 94720, United States
- Bakar Institute of Digital Materials for the Planet, College of Computing, Data Science, and Society, University of California, Berkeley, California 94720, United States
- KACST-UC Berkeley Center of Excellence for Nanomaterials for Clean Energy Applications, King Abdulaziz City for Science and Technology, Riyadh 11442, Saudi Arabia
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