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Liu F, Chen Z, Liu T, Song R, Lin Y, Turner JJ, Jia C. Self-supervised generative models for crystal structures. iScience 2024; 27:110672. [PMID: 39252963 PMCID: PMC11381803 DOI: 10.1016/j.isci.2024.110672] [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: 01/12/2024] [Revised: 03/25/2024] [Accepted: 08/01/2024] [Indexed: 09/11/2024] Open
Abstract
Inspired by advancements in natural language processing, we utilize self-supervised learning and an equivariant graph neural network to develop a unified platform for training generative models capable of generating inorganic crystal structures, as well as efficiently adapting to downstream tasks in material property prediction. To mitigate the challenge of evaluating the reliability of generated structures during training, we employ a generative adversarial network (GAN) with its discriminator being a cost-effective reliability evaluator, significantly enhancing model performance. We demonstrate the utility of our model in optimizing crystal structures under predefined conditions. Without external properties acquired experimentally or numerically, our model further displays its capability to help understand inorganic crystal formation by grouping chemically similar elements. This paper extends an invitation to further explore the scientific understanding of material structures through generative models, offering a fresh perspective on the scope and efficacy of machine learning in material science.
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Affiliation(s)
- Fangze Liu
- Department of Physics, Stanford University, Stanford, CA 94305, USA
- Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
| | - Zhantao Chen
- Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
- Linac Coherent Light Source, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
| | - Tianyi Liu
- Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
- Department of Chemistry, Stanford University, Stanford, CA 94305, USA
| | - Ruyi Song
- Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
| | - Yu Lin
- Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
| | - Joshua J Turner
- Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
- Linac Coherent Light Source, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
| | - Chunjing Jia
- Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
- Department of Physics, University of Florida, Gainesville, FL 32611, USA
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2
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Jiang M, Wang D, Kim YH, Duan C, Talapin DV, Zhou C. Evolution of Surface Chemistry in Two-Dimensional MXenes: From Mixed to Tunable Uniform Terminations. Angew Chem Int Ed Engl 2024; 63:e202409480. [PMID: 39031873 DOI: 10.1002/anie.202409480] [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: 05/20/2024] [Revised: 06/20/2024] [Accepted: 06/20/2024] [Indexed: 07/22/2024]
Abstract
Surface chemistry of MXenes is of great interest as the terminations can define the intrinsic properties of this family of materials. The diverse and tunable terminations also distinguish MXenes from many other 2D materials. Conventional fluoride-containing reagents etching approaches resulted in MXenes with mixed fluoro-, oxo-, and hydroxyl surface groups. The relatively strong chemical bonding of MXenes' surface metal atoms with oxygen and fluorine makes post-synthetic covalent surface modifications of such MXenes unfavorable. In this minireview, we focus on the recent advances in MXenes with uniform surface terminations. Unconventional methods, including Lewis acidic molten salt etching (LAMS) and bottom-up direct synthesis, have been proven successful in producing halide-terminated MXenes. These synthetic strategies have opened new possibilities for MXenes because weaker surface chemical bonds in halide-terminated MXenes facilitate post-synthetic covalent surface modifications. Both computational and experimental results on surface termination-dependent properties are summarized and discussed. Finally, we offer our perspective on the opportunities and challenges in this exciting research field.
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Affiliation(s)
- Mengni Jiang
- School of Chemistry and Material Science, Jiangsu Key Laboratory of New Power Batteries, Jiangsu Collaborative Innovation Center of Biomedical Functional Materials, Nanjing Normal University, 210023, Nanjing, Jiangsu, China
| | - Di Wang
- Department of Chemistry, University of Chicago, 60637, Chicago, Illinois, United States
| | - Young-Hwan Kim
- Pritzker School of Molecular Engineering, University of Chicago, 60637, Chicago, Illinois, United States
| | - Chunying Duan
- State Key Laboratory of Coordination Chemistry, School of Chemistry and Chemical Engineering, Nanjing University, 210023, Nanjing, Jiangsu, China
| | - Dmitri V Talapin
- Department of Chemistry, University of Chicago, 60637, Chicago, Illinois, United States
- Pritzker School of Molecular Engineering, University of Chicago, 60637, Chicago, Illinois, United States
- Center for Nanoscale Materials, Argonne National Laboratory, 60439, Argonne, Illinois, United States
| | - Chenkun Zhou
- School of Chemistry and Material Science, Jiangsu Key Laboratory of New Power Batteries, Jiangsu Collaborative Innovation Center of Biomedical Functional Materials, Nanjing Normal University, 210023, Nanjing, Jiangsu, China
- Department of Chemistry, University of Chicago, 60637, Chicago, Illinois, United States
- State Key Laboratory of Coordination Chemistry, School of Chemistry and Chemical Engineering, Nanjing University, 210023, Nanjing, Jiangsu, China
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3
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Castelvecchi D. Researchers built an 'AI Scientist' - what can it do? Nature 2024; 633:266. [PMID: 39215083 DOI: 10.1038/d41586-024-02842-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/04/2024]
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4
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Ai Z, Zhang L, Chen Y, Meng Y, Long Y, Xiao J, Yang Y, Guo W, Wang Y, Jiang J. Customizable Colorimetric Sensor Array via a High-Throughput Robot for Mitigation of Humidity Interference in Gas Sensing. ACS Sens 2024; 9:4143-4153. [PMID: 39086324 DOI: 10.1021/acssensors.4c01083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/02/2024]
Abstract
One challenge for gas sensors is humidity interference, as dynamic humidity conditions can cause unpredictable fluctuations in the response signal to analytes, increasing quantitative detection errors. Here, we introduce a concept: Select humidity sensors from a pool to compensate for the humidity signal for each gas sensor. In contrast to traditional methods that extremely suppress the humidity response, the sensor pool allows for more accurate gas quantification across a broader range of application scenarios by supplying customized, high-dimensional humidity response data as extrinsic compensation. As a proof-of-concept, mitigation of humidity interference in colorimetric gas quantification was achieved in three steps. First, across a ten-dimensional variable space, an algorithm-driven high-throughput experimental robot discovered multiple local optimum regions where colorimetric humidity sensing formulations exhibited high evaluations on sensitivity, reversibility, response time, and color change extent for 10-90% relative humidity (RH) in room temperature (25 °C). Second, from the local optimum regions, 91 sensing formulations with diverse variables were selected to construct a parent colorimetric humidity sensor array as the sensor pool for humidity signal compensation. Third, the quasi-optimal sensor subarrays were identified as customized humidity signal compensation solutions for different gas sensing scenarios across an approximately full dynamic range of humidity (10-90% RH) using an ingenious combination optimization strategy, and two accurate quantitative detections were attained: one with a mean absolute percentage error (MAPE) reduction from 4.4 to 0.75% and the other from 5.48 to 1.37%. Moreover, the parent sensor array's excellent humidity selectivity was validated against 10 gases. This work demonstrates the feasibility and superiority of robot-assisted construction of a customizable parent colorimetric sensor array to mitigate humidity interference in gas quantification.
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Affiliation(s)
- Zhehong Ai
- Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, Zhejiang 310024, China
- Research Center for High Efficiency Computing System, Zhejiang Laboratory, Hangzhou, Zhejiang 311121, China
| | - Longhan Zhang
- Research Center for New Materials Computing, Zhejiang Laboratory, Hangzhou, Zhejiang 311121, China
- Hong Kong University of Science and Technology (Guangzhou), Guangzhou 511458, China
| | - Yangguan Chen
- Research Center for New Materials Computing, Zhejiang Laboratory, Hangzhou, Zhejiang 311121, China
| | - Yu Meng
- School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100091, China
| | - Yifan Long
- Research Center for Space Computing System, Zhejiang Laboratory, Hangzhou, Zhejiang 311121, China
| | - Julin Xiao
- Research Center for Novel Computing Sensing and Intelligent Processing, Zhejiang Laboratory, Hangzhou, Zhejiang 311121, China
| | - Yao Yang
- Research Center for Space Computing System, Zhejiang Laboratory, Hangzhou, Zhejiang 311121, China
| | - Wei Guo
- School of Materials Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu 212100, China
| | - Yueming Wang
- Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, Zhejiang 310024, China
- Key Laboratory of Space Active Optoelectronics Technology, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China
| | - Jing Jiang
- Research Center for High Efficiency Computing System, Zhejiang Laboratory, Hangzhou, Zhejiang 311121, China
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Ye J, Gaur D, Mi C, Chen Z, Fernández IL, Zhao H, Dong Y, Polavarapu L, Hoye RLZ. Strongly-confined colloidal lead-halide perovskite quantum dots: from synthesis to applications. Chem Soc Rev 2024; 53:8095-8122. [PMID: 38894687 DOI: 10.1039/d4cs00077c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Colloidal semiconductor nanocrystals enable the realization and exploitation of quantum phenomena in a controlled manner, and can be scaled up for commercial uses. These materials have become important for a wide range of applications, from ultrahigh definition displays, to solar cells, quantum computing, bioimaging, optical communications, and many more. Over the last decade, lead-halide perovskite nanocrystals have rapidly gained prominence as efficient semiconductors. Although the majority of studies have focused on large nanocrystals in the weak- to intermediate-confinement regime, quantum dots (QDs) in the strongly-confined regime (with sizes smaller than the Bohr diameter, which ranges from 4-12 nm for lead-halide perovskites) offer unique opportunities, including polarized light emission and color-pure, stable luminescence in the region that is unattainable by perovskites with single-halide compositions. In this tutorial review, we bring together the latest insights into this emerging and rapidly growing area, focusing on the synthesis, steady-state optical properties (including exciton fine-structure splitting), and transient kinetics (including hot carrier cooling) of strongly-confined perovskite QDs. We also discuss recent advances in their applications, including single photon emission for quantum technologies, as well as light-emitting diodes. We finish with our perspectives on future challenges and opportunities for strongly-confined QDs, particularly around improving the control over monodispersity and stability, important fundamental questions on the photophysics, and paths forward to improve the performance of perovskite QDs in light-emitting diodes.
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Affiliation(s)
- Junzhi Ye
- Inorganic Chemistry Laboratory, Department of Chemistry, University of Oxford, South Parks Road, Oxford, OX1 3QR, UK.
| | - Deepika Gaur
- CINBIO, Universidade de Vigo, Materials Chemistry and Physics Group, Department of Physical Chemistry Campus Universitario As Lagoas, Marcosende 36310, Vigo, Spain.
| | - Chenjia Mi
- Department of Chemistry and Biochemistry, The University of Oklahoma, Norman, Oklahoma 73019, USA
| | - Zijian Chen
- Centre for Intelligent and Biomimetic Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 440305, China
| | - Iago López Fernández
- CINBIO, Universidade de Vigo, Materials Chemistry and Physics Group, Department of Physical Chemistry Campus Universitario As Lagoas, Marcosende 36310, Vigo, Spain.
| | - Haitao Zhao
- Centre for Intelligent and Biomimetic Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 440305, China
| | - Yitong Dong
- Department of Chemistry and Biochemistry, The University of Oklahoma, Norman, Oklahoma 73019, USA
| | - Lakshminarayana Polavarapu
- CINBIO, Universidade de Vigo, Materials Chemistry and Physics Group, Department of Physical Chemistry Campus Universitario As Lagoas, Marcosende 36310, Vigo, Spain.
| | - Robert L Z Hoye
- Inorganic Chemistry Laboratory, Department of Chemistry, University of Oxford, South Parks Road, Oxford, OX1 3QR, UK.
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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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7
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Schön JC. Energy landscapes-Past, present, and future: A perspective. J Chem Phys 2024; 161:050901. [PMID: 39101536 DOI: 10.1063/5.0212867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2024] [Accepted: 06/17/2024] [Indexed: 08/06/2024] Open
Abstract
Energy landscapes and the closely related cost function landscapes have been recognized in science, mathematics, and various other fields such as economics as being highly useful paradigms and tools for the description and analysis of the properties of many systems, ranging from glasses, proteins, and abstract global optimization problems to business models. A multitude of algorithms for the exploration and exploitation of such landscapes have been developed over the past five decades in the various fields of applications, where many re-inventions but also much cross-fertilization have occurred. Twenty-five years ago, trying to increase the fruitful interactions between workers in different fields led to the creation of workshops and small conferences dedicated to the study of energy landscapes in general instead of only focusing on specific applications. In this perspective, I will present some history of the development of energy landscape studies and try to provide an outlook on in what directions the field might evolve in the future and what larger challenges are going to lie ahead, both from a conceptual and a practical point of view, with the main focus on applications of energy landscapes in chemistry and physics.
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Affiliation(s)
- J C Schön
- Max-Planck-Institute for Solid State Research, Heisenbergstr. 1, D-70569 Stuttgart, Germany
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8
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Omidvar M, Zhang H, Ihalage AA, Saunders TG, Giddens H, Forrester M, Haq S, Hao Y. Accelerated discovery of perovskite solid solutions through automated materials synthesis and characterization. Nat Commun 2024; 15:6554. [PMID: 39095463 PMCID: PMC11297172 DOI: 10.1038/s41467-024-50884-y] [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: 10/31/2023] [Accepted: 07/22/2024] [Indexed: 08/04/2024] Open
Abstract
Accelerating perovskite solid solution discovery and sustainable synthesis is crucial for addressing challenges in wireless communication and biosensors. However, the vast array of chemical compositions and their dependence on factors such as crystal structure, and sintering temperature require time-consuming manual processes. To overcome these constraints, we introduce an automated materials discovery approach encompassing machine learning (ML) assisted material screening, robotic synthesis, and high-throughput characterization. Our proposed platform for rapid sintering and dielectric analysis streamlines the characterization of perovskites and the discovery of disordered materials. The setup has been successfully validated, demonstrating processing materials within minutes, in stark contrast to conventional procedures that can take hours or days. Following setup validation with established samples, we showcase synthesizing single-phase solid solutions within the barium family, such as (BaxSr1-x)CeO3, identified through ML-guided chemistry.
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Affiliation(s)
- Mojan Omidvar
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK
| | - Hangfeng Zhang
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK
| | - Achintha Avin Ihalage
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK
| | - Theo Graves Saunders
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK
| | - Henry Giddens
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK
| | | | - Sajad Haq
- QinetiQ, Cody Technology Park, Farnborough, Hampshire, UK
| | - Yang Hao
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK.
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9
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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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McDermott S, Kotar J, Collins J, Mancini L, Bowman R, Cicuta P. Using old laboratory equipment with modern Web-of-Things standards: a smart laboratory with LabThings Retro. ROYAL SOCIETY OPEN SCIENCE 2024; 11:240634. [PMID: 39113767 PMCID: PMC11304333 DOI: 10.1098/rsos.240634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Revised: 06/19/2024] [Accepted: 06/27/2024] [Indexed: 08/10/2024]
Abstract
There has been an increasing, and welcome, open hardware trend towards science teams building and sharing their designs for new instruments. These devices, often built upon low-cost microprocessors and microcontrollers, can be readily connected to enable complex, automated and smart experiments. When designed to use open communication web standards, devices from different laboratories and manufacturers can be controlled using a single protocol and even communicate with each other. However, science labs still have a majority of old, perfectly functional equipment which tends to use older, and sometimes proprietary, standards for communications. In order to encourage the continued and integrated use of this equipment in modern automated experiments, we develop and demonstrate LabThings Retro. This allows us to retrofit old instruments to use modern Web-of-Things standards, which we demonstrate with closed-loop feedback involving an optical microscope, digital imaging and fluid pumping.
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Affiliation(s)
| | - Jurij Kotar
- Cavendish Laboratory, University of Cambridge, Cambridge, UK
| | - Joel Collins
- Department of Physics, University of Bath, Bath, UK
| | | | | | - Pietro Cicuta
- Cavendish Laboratory, University of Cambridge, Cambridge, UK
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11
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Liu H, Yin H, Luo Z, Wang X. Integrating chemistry knowledge in large language models via prompt engineering. Synth Syst Biotechnol 2024; 10:23-38. [PMID: 39206087 PMCID: PMC11350497 DOI: 10.1016/j.synbio.2024.07.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 07/08/2024] [Accepted: 07/20/2024] [Indexed: 09/04/2024] Open
Abstract
This paper presents a study on the integration of domain-specific knowledge in prompt engineering to enhance the performance of large language models (LLMs) in scientific domains. The proposed domain-knowledge embedded prompt engineering method outperforms traditional prompt engineering strategies on various metrics, including capability, accuracy, F1 score, and hallucination drop. The effectiveness of the method is demonstrated through case studies on complex materials including the MacMillan catalyst, paclitaxel, and lithium cobalt oxide. The results suggest that domain-knowledge prompts can guide LLMs to generate more accurate and relevant responses, highlighting the potential of LLMs as powerful tools for scientific discovery and innovation when equipped with domain-specific prompts. The study also discusses limitations and future directions for domain-specific prompt engineering development.
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Affiliation(s)
- Hongxuan Liu
- Department of Chemical Engineering, Tsinghua University, Beijing, 100084, China
| | - Haoyu Yin
- Department of Chemical Engineering, Tsinghua University, Beijing, 100084, China
| | - Zhiyao Luo
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Old Road Campus Research Building, Headington, Oxford, OX3 7DQ, United Kingdom
| | - Xiaonan Wang
- Department of Chemical Engineering, Tsinghua University, Beijing, 100084, China
- Key Laboratory for Industrial Biocatalysis, Ministry of Education, Tsinghua University, Beijing, 100084, China
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12
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Chen C, Nguyen DT, Lee SJ, Baker NA, Karakoti AS, Lauw L, Owen C, Mueller KT, Bilodeau BA, Murugesan V, Troyer M. Accelerating Computational Materials Discovery with Machine Learning and Cloud High-Performance Computing: from Large-Scale Screening to Experimental Validation. J Am Chem Soc 2024; 146:20009-20018. [PMID: 38980280 DOI: 10.1021/jacs.4c03849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
High-throughput computational materials discovery has promised significant acceleration of the design and discovery of new materials for many years. Despite a surge in interest and activity, the constraints imposed by large-scale computational resources present a significant bottleneck. Furthermore, examples of very large-scale computational discovery carried out through experimental validation remain scarce, especially for materials with product applicability. Here, we demonstrate how this vision became reality by combining state-of-the-art machine learning (ML) models and traditional physics-based models on cloud high-performance computing (HPC) resources to quickly navigate through more than 32 million candidates and predict around half a million potentially stable materials. By focusing on solid-state electrolytes for battery applications, our discovery pipeline further identified 18 promising candidates with new compositions and rediscovered a decade's worth of collective knowledge in the field as a byproduct. We then synthesized and experimentally characterized the structures and conductivities of our top candidates, the NaxLi3-xYCl6 (0≤ x≤ 3) series, demonstrating the potential of these compounds to serve as solid electrolytes. Additional candidate materials that are currently under experimental investigation could offer more examples of the computational discovery of new phases of Li- and Na-conducting solid electrolytes. The showcased screening of millions of materials candidates highlights the transformative potential of advanced ML and HPC methodologies, propelling materials discovery into a new era of efficiency and innovation.
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Affiliation(s)
- Chi Chen
- Azure Quantum, Microsoft, One Microsoft Way, Redmond, Washington 98052, United States
| | - Dan Thien Nguyen
- Pacific Northwest National Laboratory, Physical and Computational Sciences Directorate, 902 Battelle Blvd., Richland, Washington 99352, United States
| | - Shannon J Lee
- Pacific Northwest National Laboratory, Physical and Computational Sciences Directorate, 902 Battelle Blvd., Richland, Washington 99352, United States
| | - Nathan A Baker
- Azure Quantum, Microsoft, One Microsoft Way, Redmond, Washington 98052, United States
| | - Ajay S Karakoti
- Pacific Northwest National Laboratory, Physical and Computational Sciences Directorate, 902 Battelle Blvd., Richland, Washington 99352, United States
| | - Linda Lauw
- Azure Quantum, Microsoft, One Microsoft Way, Redmond, Washington 98052, United States
| | - Craig Owen
- Microsoft Surface, Microsoft, One Microsoft Way, Redmond, Washington 98052, United States
| | - Karl T Mueller
- Pacific Northwest National Laboratory, Physical and Computational Sciences Directorate, 902 Battelle Blvd., Richland, Washington 99352, United States
| | - Brian A Bilodeau
- Azure Quantum, Microsoft, One Microsoft Way, Redmond, Washington 98052, United States
| | - Vijayakumar Murugesan
- Pacific Northwest National Laboratory, Physical and Computational Sciences Directorate, 902 Battelle Blvd., Richland, Washington 99352, United States
| | - Matthias Troyer
- Azure Quantum, Microsoft, One Microsoft Way, Redmond, Washington 98052, United States
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13
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Xiong XL, Ma YP, Liu H, Huang CZ, Zhou J. Efficient and Accurate pH Determination with pH Test Strips Based on Machine Learning. Anal Chem 2024; 96:11498-11507. [PMID: 38946253 DOI: 10.1021/acs.analchem.4c02153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
The determination of pH values is crucial in various fields, such as analytical chemistry, medical diagnostics, and biochemical research. pH test strips, renowned for their convenience and cost-effectiveness, are commonly utilized for pH qualitative estimation. Recently, quantitative methods for determining pH values using pH test strips have been developed. However, these methods can be prone to errors due to environmental factors, such as lighting conditions, which affect the imaging quality of the pH test strips. To address these challenges, we developed an innovative approach that combines machine learning techniques with pH test strips for the quantitative determination of pH values. Our method involves extracting artificial features from the pH test strip images and combining them across multiple dimensions for comprehensive analysis. To ensure optimal feature selection, we developed a feature selection strategy based on SHAP importance. This strategy helps in identifying the most relevant features that contribute to accurate pH prediction. Furthermore, we integrated multiple machine learning algorithms, employing a robust stacking fusion strategy to establish a highly reliable pH value prediction model. Our proposed method automates the determination of pH values through pH test strips, effectively overcoming the limitations associated with environmental lighting interference. Experimental results demonstrate that this method is convenient, effective, and highly reliable for the determination of pH values.
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Affiliation(s)
- Xiao Long Xiong
- College of Computer and Information Science, Southwest University, Chongqing 400715, China
| | - Yun Peng Ma
- Key Laboratory of Luminescence Analysis and Molecular Sensing (Southwest University), Ministry of Education, College of Pharmaceutical Sciences, Southwest University, Chongqing 400715, China
| | - Hui Liu
- Key Laboratory of Luminescence Analysis and Molecular Sensing (Southwest University), Ministry of Education, College of Pharmaceutical Sciences, Southwest University, Chongqing 400715, China
| | - Cheng Zhi Huang
- Key Laboratory of Luminescence Analysis and Molecular Sensing (Southwest University), Ministry of Education, College of Pharmaceutical Sciences, Southwest University, Chongqing 400715, China
| | - Jun Zhou
- College of Computer and Information Science, Southwest University, Chongqing 400715, China
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14
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Lei C, Guan W, Zhao Y, Yu G. Chemistries and materials for atmospheric water harvesting. Chem Soc Rev 2024; 53:7328-7362. [PMID: 38896434 DOI: 10.1039/d4cs00423j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Atmospheric water harvesting (AWH) is recognized as a crucial strategy to address the global challenge of water scarcity by tapping into the vast reserves of atmospheric moisture for potable water supply. Within this domain, sorbents lie in the core of AWH technologies as they possess broad adaptability across a wide spectrum of humidity levels, underpinned by the cyclic sorption and desorption processes of sorbents, necessitating a multi-scale viewpoint regarding the rational material and chemical selection and design. This Invited Review delves into the essential sorption mechanisms observed across various classes of sorbent systems, emphasizing the water-sorbent interactions and the progression of water networks. A special focus is placed on the insights derived from isotherm profiles, which elucidate sorbent structures and sorption dynamics. From these foundational principles, we derive material and chemical design guidelines and identify key tuning factors from a structural-functional perspective across multiple material systems, addressing their fundamental chemistries and unique attributes. The review further navigates through system-level design considerations to optimize water production efficiency. This review aims to equip researchers in the field of AWH with a thorough understanding of the water-sorbent interactions, material design principles, and system-level considerations essential for advancing this technology.
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Affiliation(s)
- Chuxin Lei
- Materials Science and Engineering Program and Walker Department of Mechanical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
| | - Weixin Guan
- Materials Science and Engineering Program and Walker Department of Mechanical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
| | - Yaxuan Zhao
- Materials Science and Engineering Program and Walker Department of Mechanical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
| | - Guihua Yu
- Materials Science and Engineering Program and Walker Department of Mechanical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
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15
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Anosova O, Kurlin V, Senechal M. The importance of definitions in crystallography. IUCRJ 2024; 11:453-463. [PMID: 38805320 PMCID: PMC11220884 DOI: 10.1107/s2052252524004056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Accepted: 05/02/2024] [Indexed: 05/30/2024]
Abstract
This paper was motivated by the articles `Same or different - that is the question' in CrystEngComm (July 2020) and `Change to the definition of a crystal' in the IUCr Newsletter (June 2021). Experimental approaches to crystal comparisons require rigorously defined classifications in crystallography and beyond. Since crystal structures are determined in a rigid form, their strongest equivalence in practice is rigid motion, which is a composition of translations and rotations in 3D space. Conventional representations based on reduced cells and standardizations theoretically distinguish all periodic crystals. However, all cell-based representations are inherently discontinuous under almost any atomic displacement that can arbitrarily scale up a reduced cell. Hence, comparison of millions of known structures in materials databases requires continuous distance metrics.
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Affiliation(s)
- Olga Anosova
- Computer Science Department and Materials Innovation FactoryUniversity of LiverpoolLiverpoolUnited Kingdom
| | - Vitaliy Kurlin
- Computer Science Department and Materials Innovation FactoryUniversity of LiverpoolLiverpoolUnited Kingdom
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16
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Gao Y, Lin H, Zhu X. General Aqueous System Simulation through an AI-Embedded Metaverse Chemistry Laboratory. J Phys Chem Lett 2024; 15:5978-5984. [PMID: 38814104 DOI: 10.1021/acs.jpclett.4c01111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/31/2024]
Abstract
Recent decades have witnessed the rapid development of autonomous laboratories and artificial intelligence, where experiments can be automatically run and optimized. Although human work is reduced, the total time of experimental optimization is still consuming due to limitations of the current ab metaverse framework, which accurately predicts the future state of the system by receiving and analyzing in situ experimental data. To substitute for traditional simulation methods, we designed a physically endorsed deep learning model to predict the future system picture ranging from atomic image to bulk appearance, intensively using the correlations between properties of the system. Through this framework, we studied the general aqueous system, covering 100+ common ionic solutions. We can accurately simulate properties for a general aqueous system as well as predict the time of solvation of ionic compounds ahead of real experiments. In this way, the experiments can be optimized more efficiently without waiting for the end of a bad iteration. We hope our work offers a fresh direction for the digitization of chemical information, enhancing access to and use of experimental data in advancing the field of physical chemistry.
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Affiliation(s)
- Yuechen Gao
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, Shenzhen, Guangdong, China 518172
| | - Haoxiang Lin
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, Shenzhen, Guangdong, China 518172
| | - Xi Zhu
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, Shenzhen, Guangdong, China 518172
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17
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Zhang J, Hauch JA, Brabec CJ. Toward Self-Driven Autonomous Material and Device Acceleration Platforms (AMADAP) for Emerging Photovoltaics Technologies. Acc Chem Res 2024; 57:1434-1445. [PMID: 38652511 PMCID: PMC11079961 DOI: 10.1021/acs.accounts.4c00095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 04/11/2024] [Accepted: 04/16/2024] [Indexed: 04/25/2024]
Abstract
ConspectusIn the ever-increasing renewable-energy demand scenario, developing new photovoltaic technologies is important, even in the presence of established terawatt-scale silicon technology. Emerging photovoltaic technologies play a crucial role in diversifying material flows while expanding the photovoltaic product portfolio, thus enhancing security and competitiveness within the solar industry. They also serve as a valuable backup for silicon photovoltaic, providing resilience to the overall energy infrastructure. However, the development of functional solar materials poses intricate multiobjective optimization challenges in a large multidimensional composition and parameter space, in some cases with millions of potential candidates to be explored. Solving it necessitates reproducible, user-independent laboratory work and intelligent preselection of innovative experimental methods.Materials acceleration platforms (MAPs) seamlessly integrate robotic materials synthesis and characterization with AI-driven data analysis and experimental design, positioning them as enabling technologies for the discovery and exploration of new materials. They are proposed to revolutionize materials development away from the Edisonian trial-and-error approaches to ultrashort cycles of experiments with exceptional precision, generating a reliable and highly qualitative data situation that allows training machine learning algorithms with predictive power. MAPs are designed to assist the researcher in multidimensional aspects of materials discovery, such as material synthesis, precursor preparation, sample processing and characterization, and data analysis, and are drawing escalating attention in the field of energy materials. Device acceleration platforms (DAPs), however, are designed to optimize functional films and layer stacks. Unlike MAPs, which focus on material discovery, a central aspect of DAPs is the identification and refinement of ideal processing conditions for a predetermined set of materials. Such platforms prove especially invaluable when dealing with "disordered semiconductors," which depend heavily on the processing parameters that ultimately define the functional properties and functionality of thin film layers. By facilitating the fine-tuning of processing conditions, DAPs contribute significantly to the advancement and optimization of disordered semiconductor devices, such as emerging photovoltaics.In this Account, we review the recent advancements made by our group in automated and autonomous laboratories for advanced material discovery and device optimization with a strong focus on emerging photovoltaics, such as solution-processing perovskite solar cells and organic photovoltaics. We first introduce two MAPs and two DAPs developed in-house: a microwave-assisted high-throughput synthesis platform for the discovery of organic interface materials, a multipurpose robot-based pipetting platform for the synthesis of new semiconductors and the characterization of thin film semiconductor composites, the SPINBOT system, which is a spin-coating DAP with the potential to optimize complex device architectures, and finally, AMANDA, a fully integrated and autonomously operating DAP. Notably, we underscore the utilization of a robot-based high-throughput experimentation technique to address the common optimization challenges encountered in extensive multidimensional composition and parameter spaces pertaining to organic and perovskite photovoltaics materials. Finally, we briefly propose a holistic concept and technology, a self-driven autonomous material and device acceleration platform (AMADAP) laboratory, for autonomous functional solar materials discovery and development. We hope to discover how AMADAP can be further strengthened and universalized with advancing development of hardware and software infrastructures in the future.
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Affiliation(s)
- Jiyun Zhang
- Forschungszentrum
Juelich GmbH, Helmholtz-Institute Erlangen-Nürnberg (HI ERN), Department of High Throughput Methods in Photovoltaics, Immerwahrstraße 2, 91058 Erlangen, Germany
- Friedrich-Alexander-University
Erlangen-Nuremberg, Faculty of Engineering, Department of Material
Science, Institute of Materials for Electronics
and Energy Technology (i-MEET), Martensstrasse 7, 91058 Erlangen, Germany
| | - Jens A. Hauch
- Forschungszentrum
Juelich GmbH, Helmholtz-Institute Erlangen-Nürnberg (HI ERN), Department of High Throughput Methods in Photovoltaics, Immerwahrstraße 2, 91058 Erlangen, Germany
| | - Christoph J. Brabec
- Forschungszentrum
Juelich GmbH, Helmholtz-Institute Erlangen-Nürnberg (HI ERN), Department of High Throughput Methods in Photovoltaics, Immerwahrstraße 2, 91058 Erlangen, Germany
- Friedrich-Alexander-University
Erlangen-Nuremberg, Faculty of Engineering, Department of Material
Science, Institute of Materials for Electronics
and Energy Technology (i-MEET), Martensstrasse 7, 91058 Erlangen, Germany
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18
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Jeong J, Kim J, Sun J, Min K. Machine-Learning-Driven High-Throughput Screening for High-Energy Density and Stable NASICON Cathodes. ACS APPLIED MATERIALS & INTERFACES 2024. [PMID: 38693838 DOI: 10.1021/acsami.3c18448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2024]
Abstract
The Na super ionic conductor (NASICON), which has outstanding structural stability and a high operating voltage, is an appealing material for overcoming the limits of low specific energy and larger volume distortion of sodium-ion batteries. In this study, to discover ideal NASICON cathode materials, a screening platform based on density functional theory (DFT) calculations and machine learning (ML) is developed. A training database was generated utilizing the previous 124 545 electrode databases, and a test set of 3126 potential NASICON structures [NaxMyM'1-y(PO4)3] with 27 dopants at the metal site and 6 dopants at the polyanion central site was constructed. The developed ML surrogate model identifies 796 materials that satisfy the following criteria: formation energy of <0.0 eV/atom, energy above hull of ≤0.025 eV/atom, volume change of ≤4%, and theoretical capacity of ≥50 mAh/g. The thermodynamically stable configurations of doped NASICON structures were then selected using machine learning interatomic potential (MLIP), enabling rapid consideration of various dopant site configurations. DFT calculations are followed on 796 screened materials to obtain energy density, average voltage, and volume change. Finally, 50 candidates with an average voltage of ≥3.5 V are identified. The suggested platform accelerates the exploration for optimal NASICON materials by narrowing the focus on materials with desired properties, saving considerable resources.
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Affiliation(s)
- Jinyoung Jeong
- School of Mechanical Engineering, Soongsil University, 369 Sangdo-ro, Dongjak-gu, Seoul 06978, Republic of Korea
| | - Juo Kim
- School of Mechanical Engineering, Soongsil University, 369 Sangdo-ro, Dongjak-gu, Seoul 06978, Republic of Korea
| | - Jiwon Sun
- School of Mechanical Engineering, Soongsil University, 369 Sangdo-ro, Dongjak-gu, Seoul 06978, Republic of Korea
| | - Kyoungmin Min
- School of Mechanical Engineering, Soongsil University, 369 Sangdo-ro, Dongjak-gu, Seoul 06978, Republic of Korea
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19
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Maqsood A, Chen C, Jacobsson TJ. The Future of Material Scientists in an Age of Artificial Intelligence. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2401401. [PMID: 38477440 PMCID: PMC11109614 DOI: 10.1002/advs.202401401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 02/13/2024] [Indexed: 03/14/2024]
Abstract
Material science has historically evolved in tandem with advancements in technologies for characterization, synthesis, and computation. Another type of technology to add to this mix is machine learning (ML) and artificial intelligence (AI). Now increasingly sophisticated AI-models are seen that can solve progressively harder problems across a variety of fields. From a material science perspective, it is indisputable that machine learning and artificial intelligence offer a potent toolkit with the potential to substantially accelerate research efforts in areas such as the development and discovery of new functional materials. Less clear is how to best harness this development, what new skill sets will be required, and how it may affect established research practices. In this paper, those question are explored with respect to increasingly more sophisticated ML/AI-approaches. To structure the discussion, a conceptual framework of an AI-ladder is introduced. This AI-ladder ranges from basic data-fitting techniques to more advanced functionalities such as semi-autonomous experimentation, experimental design, knowledge generation, hypothesis formulation, and the orchestration of specialized AI modules as stepping-stones toward general artificial intelligence. This ladder metaphor provides a hierarchical framework for contemplating the opportunities, challenges, and evolving skill sets required to stay competitive in the age of artificial intelligence.
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Affiliation(s)
- Ayman Maqsood
- Institute of Photoelectronic Thin Film Devices and TechnologyKey Laboratory of Photoelectronic Thin Film Devices and Technology of TianjinCollege of Electronic Information and Optical EngineeringNankai UniversityTianjin300350China
| | - Chen Chen
- Institute of Photoelectronic Thin Film Devices and TechnologyKey Laboratory of Photoelectronic Thin Film Devices and Technology of TianjinCollege of Electronic Information and Optical EngineeringNankai UniversityTianjin300350China
| | - T. Jesper Jacobsson
- Institute of Photoelectronic Thin Film Devices and TechnologyKey Laboratory of Photoelectronic Thin Film Devices and Technology of TianjinCollege of Electronic Information and Optical EngineeringNankai UniversityTianjin300350China
- Department of PhysicsChemistry and Biology (IFM)Linköping UniversityLinköping581 83Sweden
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20
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Kuznetsova V, Coogan Á, Botov D, Gromova Y, Ushakova EV, Gun'ko YK. Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2308912. [PMID: 38241607 PMCID: PMC11167410 DOI: 10.1002/adma.202308912] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 01/10/2024] [Indexed: 01/21/2024]
Abstract
Machine learning holds significant research potential in the field of nanotechnology, enabling nanomaterial structure and property predictions, facilitating materials design and discovery, and reducing the need for time-consuming and labor-intensive experiments and simulations. In contrast to their achiral counterparts, the application of machine learning for chiral nanomaterials is still in its infancy, with a limited number of publications to date. This is despite the great potential of machine learning to advance the development of new sustainable chiral materials with high values of optical activity, circularly polarized luminescence, and enantioselectivity, as well as for the analysis of structural chirality by electron microscopy. In this review, an analysis of machine learning methods used for studying achiral nanomaterials is provided, subsequently offering guidance on adapting and extending this work to chiral nanomaterials. An overview of chiral nanomaterials within the framework of synthesis-structure-property-application relationships is presented and insights on how to leverage machine learning for the study of these highly complex relationships are provided. Some key recent publications are reviewed and discussed on the application of machine learning for chiral nanomaterials. Finally, the review captures the key achievements, ongoing challenges, and the prospective outlook for this very important research field.
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Affiliation(s)
- Vera Kuznetsova
- School of Chemistry, CRANN and AMBER Research Centres, Trinity College Dublin, College Green, Dublin, D02 PN40, Ireland
| | - Áine Coogan
- School of Chemistry, CRANN and AMBER Research Centres, Trinity College Dublin, College Green, Dublin, D02 PN40, Ireland
| | - Dmitry Botov
- Everypixel Media Innovation Group, 021 Fillmore St., PMB 15, San Francisco, CA, 94115, USA
- Neapolis University Pafos, 2 Danais Avenue, Pafos, 8042, Cyprus
| | - Yulia Gromova
- Department of Molecular and Cellular Biology, Harvard University, 52 Oxford St., Cambridge, MA, 02138, USA
| | - Elena V Ushakova
- Department of Materials Science and Engineering, and Centre for Functional Photonics (CFP), City University of Hong Kong, Hong Kong SAR, 999077, P. R. China
| | - Yurii K Gun'ko
- School of Chemistry, CRANN and AMBER Research Centres, Trinity College Dublin, College Green, Dublin, D02 PN40, Ireland
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21
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Xie J, Zhou Y, Faizan M, Li Z, Li T, Fu Y, Wang X, Zhang L. Designing semiconductor materials and devices in the post-Moore era by tackling computational challenges with data-driven strategies. NATURE COMPUTATIONAL SCIENCE 2024; 4:322-333. [PMID: 38783137 DOI: 10.1038/s43588-024-00632-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 04/18/2024] [Indexed: 05/25/2024]
Abstract
In the post-Moore's law era, the progress of electronics relies on discovering superior semiconductor materials and optimizing device fabrication. Computational methods, augmented by emerging data-driven strategies, offer a promising alternative to the traditional trial-and-error approach. In this Perspective, we highlight data-driven computational frameworks for enhancing semiconductor discovery and device development by elaborating on their advances in exploring the materials design space, predicting semiconductor properties and optimizing device fabrication, with a concluding discussion on the challenges and opportunities in these areas.
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Affiliation(s)
- Jiahao Xie
- State Key Laboratory of Integrated Optoelectronics, Key Laboratory of Automobile Materials of MOE, Key Laboratory of Material Simulation Methods & Software of MOE, and School of Materials Science and Engineering, Jilin University, Changchun, China
| | - Yansong Zhou
- State Key Laboratory of Superhard Materials, International Center of Computational Method and Software, School of Physics, Jilin University, Changchun, China
| | - Muhammad Faizan
- State Key Laboratory of Integrated Optoelectronics, Key Laboratory of Automobile Materials of MOE, Key Laboratory of Material Simulation Methods & Software of MOE, and School of Materials Science and Engineering, Jilin University, Changchun, China
| | - Zewei Li
- State Key Laboratory of Integrated Optoelectronics, Key Laboratory of Automobile Materials of MOE, Key Laboratory of Material Simulation Methods & Software of MOE, and School of Materials Science and Engineering, Jilin University, Changchun, China
| | - Tianshu Li
- State Key Laboratory of Integrated Optoelectronics, Key Laboratory of Automobile Materials of MOE, Key Laboratory of Material Simulation Methods & Software of MOE, and School of Materials Science and Engineering, Jilin University, Changchun, China
| | - Yuhao Fu
- State Key Laboratory of Superhard Materials, International Center of Computational Method and Software, School of Physics, Jilin University, Changchun, China
| | - Xinjiang Wang
- State Key Laboratory of Integrated Optoelectronics, Key Laboratory of Automobile Materials of MOE, Key Laboratory of Material Simulation Methods & Software of MOE, and School of Materials Science and Engineering, Jilin University, Changchun, China.
| | - Lijun Zhang
- State Key Laboratory of Integrated Optoelectronics, Key Laboratory of Automobile Materials of MOE, Key Laboratory of Material Simulation Methods & Software of MOE, and School of Materials Science and Engineering, Jilin University, Changchun, China.
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22
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Dai K, Geng Z, Zhang W, Wei X, Wang J, Nie G, Liu C. Biomaterial design for regenerating aged bone: materiobiological advances and paradigmatic shifts. Natl Sci Rev 2024; 11:nwae076. [PMID: 38577669 PMCID: PMC10989671 DOI: 10.1093/nsr/nwae076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Revised: 01/04/2024] [Accepted: 02/26/2024] [Indexed: 04/06/2024] Open
Abstract
China's aging demographic poses a challenge for treating prevalent bone diseases impacting life quality. As bone regeneration capacity diminishes with age due to cellular dysfunction and inflammation, advanced biomaterials-based approaches offer hope for aged bone regeneration. This review synthesizes materiobiology principles, focusing on biomaterials that target specific biological functions to restore tissue integrity. It covers strategies for stem cell manipulation, regulation of the inflammatory microenvironment, blood vessel regeneration, intervention in bone anabolism and catabolism, and nerve regulation. The review also explores molecular and cellular mechanisms underlying aged bone regeneration and proposes a database-driven design process for future biomaterial development. These insights may also guide therapies for other age-related conditions, contributing to the pursuit of 'healthy aging'.
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Affiliation(s)
- Kai Dai
- Engineering Research Center for Biomedical Materials of the Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai 200237, China
- Frontiers Science Center for Materiobiology and Dynamic Chemistry, East China University of Science and Technology; Shanghai 200237, China
- Key Laboratory for Ultrafine Materials of the Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
| | - Zhen Geng
- Institute of Translational Medicine, Shanghai University, Shanghai 200444, China
- National Center for Translational Medicine (Shanghai) SHU Branch, Shanghai University, Shanghai 200444, China
| | - Wenchao Zhang
- Engineering Research Center for Biomedical Materials of the Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
- Frontiers Science Center for Materiobiology and Dynamic Chemistry, East China University of Science and Technology; Shanghai 200237, China
| | - Xue Wei
- Engineering Research Center for Biomedical Materials of the Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
- Frontiers Science Center for Materiobiology and Dynamic Chemistry, East China University of Science and Technology; Shanghai 200237, China
| | - Jing Wang
- Engineering Research Center for Biomedical Materials of the Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai 200237, China
- Frontiers Science Center for Materiobiology and Dynamic Chemistry, East China University of Science and Technology; Shanghai 200237, China
| | - Guangjun Nie
- CAS Key Laboratory for Biomedical Effects of Nanomaterials & Nanosafety, CAS Centre for Excellence in Nanoscience, National Centre for Nanoscience and Technology, Beijing 100190, China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Changsheng Liu
- Engineering Research Center for Biomedical Materials of the Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
- Frontiers Science Center for Materiobiology and Dynamic Chemistry, East China University of Science and Technology; Shanghai 200237, China
- Key Laboratory for Ultrafine Materials of the Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
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23
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Cheetham AK, Seshadri R. Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery. CHEMISTRY OF MATERIALS : A PUBLICATION OF THE AMERICAN CHEMICAL SOCIETY 2024; 36:3490-3495. [PMID: 38681084 PMCID: PMC11044265 DOI: 10.1021/acs.chemmater.4c00643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 03/22/2024] [Accepted: 03/25/2024] [Indexed: 05/01/2024]
Abstract
The discovery of new crystalline inorganic compounds-novel compositions of matter within known structure types, or even compounds with completely new crystal structures-constitutes an important goal of solid-state and materials chemistry. Some fractions of new compounds can eventually lead to new structural and functional materials that enhance the efficiency of existing technologies or even enable completely new technologies. Materials researchers eagerly welcome new approaches to the discovery of new compounds, especially those that offer the promise of accelerated success. The recent report from a group of scientists at Google who employ a combination of existing data sets, high-throughput density functional theory calculations of structural stability, and the tools of artificial intelligence and machine learning (AI/ML) to propose new compounds is an exciting advance. We examine the claims of this work here, unfortunately finding scant evidence for compounds that fulfill the trifecta of novelty, credibility, and utility. While the methods adopted in this work appear to hold promise, there is clearly a great need to incorporate domain expertise in materials synthesis and crystallography.
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Affiliation(s)
- Anthony K. Cheetham
- Materials
Department and Materials Research Laboratory, University of California, Santa
Barbara, California 93106, United States
- Department
of Materials Science and Engineering, National
University of Singapore, Singapore 117575, Singapore
| | - Ram Seshadri
- Materials
Department and Materials Research Laboratory, University of California, Santa
Barbara, California 93106, United States
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24
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Adam D. The automated lab of tomorrow. Proc Natl Acad Sci U S A 2024; 121:e2406320121. [PMID: 38630717 PMCID: PMC11046582 DOI: 10.1073/pnas.2406320121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/19/2024] Open
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25
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Luo JD, Zhang Y, Cheng X, Li F, Tan HY, Zhou MY, Wang ZW, Hao XD, Yin YC, Jiang B, Yao HB. Halide Superionic Conductors with Non-Close-Packed Anion Frameworks. Angew Chem Int Ed Engl 2024; 63:e202400424. [PMID: 38433094 DOI: 10.1002/anie.202400424] [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: 01/07/2024] [Revised: 02/28/2024] [Accepted: 03/01/2024] [Indexed: 03/05/2024]
Abstract
Halide superionic conductors (SICs) are drawing significant research attention for their potential applications in all-solid-state batteries. A key challenge in developing such SICs is to explore and design halide structural frameworks that enable rapid ion movement. In this work, we show that the close-packed anion frameworks shared by traditional halide ionic conductors face intrinsic limitations in fast ion conduction, regardless of structural regulation. Beyond the close-packed anion frameworks, we identify that the non-close-packed anion frameworks have great potential to achieve superionic conductivity. Notably, we unravel that the non-close-packed UCl3-type framework exhibit superionic conductivity for a diverse range of carrier ions, including Li+, Na+, K+, and Ag+, which are validated through both ab initio molecular dynamics simulations and experimental measurements. We elucidate that the remarkable ionic conductivity observed in the UCl3-type framework structure stems from its significantly more distorted site and larger diffusion channel than its close-packed counterparts. By employing the non-close-packed anion framework as the key feature for high-throughput computational screening, we also identify LiGaCl3 as a promising candidate for halide SICs. These discoveries provide crucial insights for the exploration and design of novel halide SICs.
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Affiliation(s)
- Jin-Da Luo
- Hefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei, Anhui, 230026, China
- Department of Applied Chemistry, University of Science and Technology of China, Hefei, Anhui, 230026, China
| | - Yixi Zhang
- Key Laboratory of Precision and Intelligent Chemistry, Department of Chemical Physics, University of Science and Technology of China, Hefei, Anhui, 230026, China
| | - Xiaobin Cheng
- Department of Applied Chemistry, University of Science and Technology of China, Hefei, Anhui, 230026, China
| | - Feng Li
- Hefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei, Anhui, 230026, China
| | - Hao-Yuan Tan
- Hefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei, Anhui, 230026, China
| | - Mei-Yu Zhou
- Department of Applied Chemistry, University of Science and Technology of China, Hefei, Anhui, 230026, China
| | - Zi-Wei Wang
- Department of Applied Chemistry, University of Science and Technology of China, Hefei, Anhui, 230026, China
| | - Xu-Dong Hao
- Department of Applied Chemistry, University of Science and Technology of China, Hefei, Anhui, 230026, China
| | - Yi-Chen Yin
- Hefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei, Anhui, 230026, China
- Department of Applied Chemistry, University of Science and Technology of China, Hefei, Anhui, 230026, China
| | - Bin Jiang
- Key Laboratory of Precision and Intelligent Chemistry, Department of Chemical Physics, University of Science and Technology of China, Hefei, Anhui, 230026, China
| | - Hong-Bin Yao
- Hefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei, Anhui, 230026, China
- Department of Applied Chemistry, University of Science and Technology of China, Hefei, Anhui, 230026, China
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26
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Qi C, Zhou Y, Yuan X, Peng Q, Yang Y, Li Y, Wen X. Machine Learning-Accelerated First-Principles Study of Atomic Configuration and Ionic Diffusion in Li 10GeP 2S 12 Solid Electrolyte. MATERIALS (BASEL, SWITZERLAND) 2024; 17:1810. [PMID: 38673167 PMCID: PMC11051406 DOI: 10.3390/ma17081810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2024] [Revised: 04/07/2024] [Accepted: 04/11/2024] [Indexed: 04/28/2024]
Abstract
The solid electrolyte Li10GeP2S12 (LGPS) plays a crucial role in the development of all-solid-state batteries and has been widely studied both experimentally and theoretically. The properties of solid electrolytes, such as thermodynamic stability, conductivity, band gap, and more, are closely related to their ground-state structures. However, the presence of site-disordered co-occupancy of Ge/P and defective fractional occupancy of lithium ions results in an exceptionally large number of possible atomic configurations (structures). Currently, the electrostatic energy criterion is widely used to screen favorable candidates and reduce computational costs in first-principles calculations. In this study, we employ the machine learning- and active-learning-based LAsou method, in combination with first-principles calculations, to efficiently predict the most stable configuration of LGPS as reported in the literature. Then, we investigate the diffusion properties of Li ions within the temperature range of 500-900 K using ab initio molecular dynamics. The results demonstrate that the atomic configurations with different skeletons and Li ion distributions significantly affect the Li ions' diffusion. Moreover, the results also suggest that the LAsou method is valuable for refining experimental crystal structures, accelerating theoretical calculations, and facilitating the design of new solid electrolyte materials in the future.
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Affiliation(s)
- Changlin Qi
- State Key Laboratory of Coal Conversion, Institute of Coal Chemistry, Chinese Academy of Sciences, Taiyuan 030001, China (X.W.)
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China
| | - Yuwei Zhou
- State Key Laboratory of Coal Conversion, Institute of Coal Chemistry, Chinese Academy of Sciences, Taiyuan 030001, China (X.W.)
- National Energy Center for Coal to Clean Fuels, Synfuels China Co., Ltd., Huairou District, Beijing 101400, China
| | - Xiaoze Yuan
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China
- State Key Laboratory of Nonlinear Mechanics, Institute of Mechanics, Chinese Academy of Sciences, Beijing 100190, China
| | - Qing Peng
- State Key Laboratory of Nonlinear Mechanics, Institute of Mechanics, Chinese Academy of Sciences, Beijing 100190, China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
- Guangdong Aerospace Research Academy, Guangzhou 511458, China
| | - Yong Yang
- State Key Laboratory of Coal Conversion, Institute of Coal Chemistry, Chinese Academy of Sciences, Taiyuan 030001, China (X.W.)
- National Energy Center for Coal to Clean Fuels, Synfuels China Co., Ltd., Huairou District, Beijing 101400, China
| | - Yongwang Li
- National Energy Center for Coal to Clean Fuels, Synfuels China Co., Ltd., Huairou District, Beijing 101400, China
| | - Xiaodong Wen
- State Key Laboratory of Coal Conversion, Institute of Coal Chemistry, Chinese Academy of Sciences, Taiyuan 030001, China (X.W.)
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China
- National Energy Center for Coal to Clean Fuels, Synfuels China Co., Ltd., Huairou District, Beijing 101400, China
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27
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Padula D. A Computational Perspective on the Reactivity of π-spacers in Self-Immolative Elimination Reactions. Chem Asian J 2024; 19:e202400010. [PMID: 38407472 DOI: 10.1002/asia.202400010] [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: 01/04/2024] [Revised: 02/16/2024] [Accepted: 02/26/2024] [Indexed: 02/27/2024]
Abstract
The controlled release of chemicals, especially in drug delivery, is crucial, often employing "self-immolative" spacers to enhance reliability. These spacers separate the payload from the protecting group, ensuring a more controlled release. Over the years, design rules have been proposed to improve the elimination process's reaction rate by modifying spacers with electron-donating groups or reducing their aromaticity. The spacer design is critical for determining the range of functional groups released during this process. This study explores various strategies from the literature aimed at improving release rates, focusing on the electronic nature of the spacer, its aromaticity, the electronic nature of its substituents, and the leaving groups involved in the elimination reaction. Through computational analysis, I investigate activation free energies by identifying transition states for model reactions. My calculations align qualitatively with experimental results, demonstrating the feasibility and reliability of computationally pre-screening model self-immolative eliminations. This approach allows proposing optimal combinations of spacer and leaving group for achieving the highest possible release rate.
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Affiliation(s)
- Daniele Padula
- Dipartimento di Biotecnologie, Chimica e Farmacia, Università di Siena, Via A. Moro 2, 53100, Siena, Italy
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28
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Bai L, Xia Z, Triffitt JT, Su J. Generation artificial intelligence (GenAI) and Biomaterials Translational: steering innovation without misdirection. BIOMATERIALS TRANSLATIONAL 2024; 5:1-2. [PMID: 39220662 PMCID: PMC11362347 DOI: 10.12336/biomatertransl.2024.01.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 09/04/2024]
Affiliation(s)
- Long Bai
- Organoid Research Center, Institute of Translational Medicine, Shanghai University, Shanghai, China
- National Center for Translational Medicine (Shanghai) SHU Branch, Shanghai University, Shanghai, China
- Wenzhou Institute of Shanghai University, Wenzhou, Zhejiang Province, China
| | - Zhidao Xia
- Centre for Nanohealth, ILS2, Medical School, Swansea University, Swansea, UK
| | - James T. Triffitt
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, The Oxford University Institute of Musculoskeletal Sciences, The Botnar Research Centre, Nuffield Orthopaedic Centre, Oxford, UK
| | - Jiacan Su
- Organoid Research Center, Institute of Translational Medicine, Shanghai University, Shanghai, China
- Department of Orthopedics, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
- National Center for Translational Medicine (Shanghai) SHU Branch, Shanghai University, Shanghai, China
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29
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Chen L, Wang B, Zhang W, Zheng S, Chen Z, Zhang M, Dong C, Pan F, Li S. Crystal Structure Assignment for Unknown Compounds from X-ray Diffraction Patterns with Deep Learning. J Am Chem Soc 2024; 146:8098-8109. [PMID: 38477574 DOI: 10.1021/jacs.3c11852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/14/2024]
Abstract
Determining the structures of previously unseen compounds from experimental characterizations is a crucial part of materials science. It requires a step of searching for the structure type that conforms to the lattice of the unknown compound, which enables the pattern matching process for characterization data, such as X-ray diffraction (XRD) patterns. However, this procedure typically places a high demand on domain expertise, thus creating an obstacle for computer-driven automation. Here, we address this challenge by leveraging a deep-learning model composed of a union of convolutional residual neural networks. The accuracy of the model is demonstrated on a dataset of over 60,000 different compounds for 100 structure types, and additional categories can be integrated without the need to retrain the existing networks. We also unravel the operation of the deep-learning black box and highlight the way in which the resemblance between the unknown compound and a structure type is quantified based on both local and global characteristics in XRD patterns. This computational tool opens new avenues for automating structure analysis on materials unearthed in high-throughput experimentation.
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Affiliation(s)
- Litao Chen
- School of Advanced Materials, Peking University, Shenzhen Graduate School, Shenzhen 518055, People's Republic of China
| | - Bingxu Wang
- School of Advanced Materials, Peking University, Shenzhen Graduate School, Shenzhen 518055, People's Republic of China
| | - Wentao Zhang
- School of Advanced Materials, Peking University, Shenzhen Graduate School, Shenzhen 518055, People's Republic of China
| | - Shisheng Zheng
- School of Advanced Materials, Peking University, Shenzhen Graduate School, Shenzhen 518055, People's Republic of China
| | - Zhefeng Chen
- School of Advanced Materials, Peking University, Shenzhen Graduate School, Shenzhen 518055, People's Republic of China
| | - Mingzheng Zhang
- School of Advanced Materials, Peking University, Shenzhen Graduate School, Shenzhen 518055, People's Republic of China
| | - Cheng Dong
- School of Advanced Materials, Peking University, Shenzhen Graduate School, Shenzhen 518055, People's Republic of China
| | - Feng Pan
- School of Advanced Materials, Peking University, Shenzhen Graduate School, Shenzhen 518055, People's Republic of China
| | - Shunning Li
- School of Advanced Materials, Peking University, Shenzhen Graduate School, Shenzhen 518055, People's Republic of China
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30
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Dral PO. AI in computational chemistry through the lens of a decade-long journey. Chem Commun (Camb) 2024; 60:3240-3258. [PMID: 38444290 DOI: 10.1039/d4cc00010b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2024]
Abstract
This article gives a perspective on the progress of AI tools in computational chemistry through the lens of the author's decade-long contributions put in the wider context of the trends in this rapidly expanding field. This progress over the last decade is tremendous: while a decade ago we had a glimpse of what was to come through many proof-of-concept studies, now we witness the emergence of many AI-based computational chemistry tools that are mature enough to make faster and more accurate simulations increasingly routine. Such simulations in turn allow us to validate and even revise experimental results, deepen our understanding of the physicochemical processes in nature, and design better materials, devices, and drugs. The rapid introduction of powerful AI tools gives rise to unique challenges and opportunities that are discussed in this article too.
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Affiliation(s)
- Pavlo O Dral
- State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China.
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31
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Yamamoto T, Otsubo Y, Nagase T, Kosuge T, Azuma M. Synthesis and Structure of Vacancy-Ordered Perovskite Ba 6Ta 2Na 2X 2O 17 (X = P, V): Significance of Structural Model Selection on Discovered Compounds. Inorg Chem 2024; 63:4482-4486. [PMID: 38415588 DOI: 10.1021/acs.inorgchem.3c04545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/29/2024]
Abstract
Vacancy-ordered 12H-type hexagonal perovskites Ba6Ru2Na2X2O17 (X = P, V) with a (c'cchcc)2 stacking sequence of [BaO3]c, [BaO3]h, and [BaO2]c' layers, where c and h represent a cubic and hexagonal stacking sequence, were previously reported by Quarez et al. in 2003. They also synthesized Ba6Ta2Na2V2O17, but structural refinement was absent. Very recently, Szymanski et al. reported 43 new compounds, including 12H-type Ba6Ta2Na2V2O17, using large-scale ab initio phase-stability data from the Materials Project and Google DeepMind with the assistance of an autonomous laboratory. But their structural refinement was very poor. Here, we report the synthesis and structure of Ba6Ta2Na2V2O17, which does not have 12H-type structure but has a vacancy-ordered 6C-type perovskite with a (c'ccccc) stacking sequence of [BaO3]c and [BaO2]c' layers. We also report the phosphite analogue Ba6Ta2Na2P2O17 as a new compound. We claim an importance of careful structural characterization on newly discovered compounds; otherwise, the database constructed will lose credibility.
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Affiliation(s)
- Takafumi Yamamoto
- Laboratory for Materials and Structures, Institute of Innovative Research, Tokyo Institute of Technology, 4259 Nagatsuta-cho, Yokohama, Kanagawa 226-8501, Japan
| | - Yuya Otsubo
- Laboratory for Materials and Structures, Institute of Innovative Research, Tokyo Institute of Technology, 4259 Nagatsuta-cho, Yokohama, Kanagawa 226-8501, Japan
| | - Teppei Nagase
- Laboratory for Materials and Structures, Institute of Innovative Research, Tokyo Institute of Technology, 4259 Nagatsuta-cho, Yokohama, Kanagawa 226-8501, Japan
| | - Taiki Kosuge
- Laboratory for Materials and Structures, Institute of Innovative Research, Tokyo Institute of Technology, 4259 Nagatsuta-cho, Yokohama, Kanagawa 226-8501, Japan
| | - Masaki Azuma
- Laboratory for Materials and Structures, Institute of Innovative Research, Tokyo Institute of Technology, 4259 Nagatsuta-cho, Yokohama, Kanagawa 226-8501, Japan
- Kanagawa Institute of Industrial Science and Technology, Ebina, Kanagawa 243-0435, Japan
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32
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Lunt AM, Fakhruldeen H, Pizzuto G, Longley L, White A, Rankin N, Clowes R, Alston B, Gigli L, Day GM, Cooper AI, Chong SY. Modular, multi-robot integration of laboratories: an autonomous workflow for solid-state chemistry. Chem Sci 2024; 15:2456-2463. [PMID: 38362408 PMCID: PMC10866346 DOI: 10.1039/d3sc06206f] [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: 11/21/2023] [Accepted: 12/23/2023] [Indexed: 02/17/2024] Open
Abstract
Automation can transform productivity in research activities that use liquid handling, such as organic synthesis, but it has made less impact in materials laboratories, which require sample preparation steps and a range of solid-state characterization techniques. For example, powder X-ray diffraction (PXRD) is a key method in materials and pharmaceutical chemistry, but its end-to-end automation is challenging because it involves solid powder handling and sample processing. Here we present a fully autonomous solid-state workflow for PXRD experiments that can match or even surpass manual data quality, encompassing crystal growth, sample preparation, and automated data capture. The workflow involves 12 steps performed by a team of three multipurpose robots, illustrating the power of flexible, modular automation to integrate complex, multitask laboratories.
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Affiliation(s)
- Amy M Lunt
- Department of Chemistry and Materials Innovation Factory, University of Liverpool L7 3NY UK
- Leverhulme Research Centre for Functional Materials Design, University of Liverpool Liverpool L7 3NY UK
| | - Hatem Fakhruldeen
- Department of Chemistry and Materials Innovation Factory, University of Liverpool L7 3NY UK
| | - Gabriella Pizzuto
- Department of Chemistry and Materials Innovation Factory, University of Liverpool L7 3NY UK
| | - Louis Longley
- Department of Chemistry and Materials Innovation Factory, University of Liverpool L7 3NY UK
| | - Alexander White
- Department of Chemistry and Materials Innovation Factory, University of Liverpool L7 3NY UK
| | - Nicola Rankin
- Department of Chemistry and Materials Innovation Factory, University of Liverpool L7 3NY UK
- Leverhulme Research Centre for Functional Materials Design, University of Liverpool Liverpool L7 3NY UK
| | - Rob Clowes
- Department of Chemistry and Materials Innovation Factory, University of Liverpool L7 3NY UK
| | - Ben Alston
- Department of Chemistry and Materials Innovation Factory, University of Liverpool L7 3NY UK
- Leverhulme Research Centre for Functional Materials Design, University of Liverpool Liverpool L7 3NY UK
| | - Lucia Gigli
- Computational Systems Chemistry, School of Chemistry, University of Southampton SO17 1BJ UK
| | - Graeme M Day
- Computational Systems Chemistry, School of Chemistry, University of Southampton SO17 1BJ UK
| | - Andrew I Cooper
- Department of Chemistry and Materials Innovation Factory, University of Liverpool L7 3NY UK
- Leverhulme Research Centre for Functional Materials Design, University of Liverpool Liverpool L7 3NY UK
| | - Samantha Y Chong
- Department of Chemistry and Materials Innovation Factory, University of Liverpool L7 3NY UK
- Leverhulme Research Centre for Functional Materials Design, University of Liverpool Liverpool L7 3NY UK
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33
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Ouyang B, Zeng Y. The rise of high-entropy battery materials. Nat Commun 2024; 15:973. [PMID: 38302492 PMCID: PMC10834409 DOI: 10.1038/s41467-024-45309-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 01/20/2024] [Indexed: 02/03/2024] Open
Affiliation(s)
- Bin Ouyang
- Department of Chemistry and Biochemistry, Florida State University, Tallahassee, FL, 32304, USA.
| | - Yan Zeng
- Department of Chemistry and Biochemistry, Florida State University, Tallahassee, FL, 32304, USA.
- Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.
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34
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Toby BH. A simple solution to the Rietveld refinement recipe problem. J Appl Crystallogr 2024; 57:175-180. [PMID: 38322720 PMCID: PMC10840306 DOI: 10.1107/s1600576723011032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Accepted: 12/22/2023] [Indexed: 02/08/2024] Open
Abstract
Rietveld refinements are widely used for many purposes in the physical sciences. Conducting a Rietveld refinement typically requires expert input because correct results may require that parameters be added to the fit in the proper order. This order will depend on the nature of the data and the initial parameter values. A mechanism for computing the next parameter to add to the refinement is shown. The fitting function is evaluated with the current parameter value set and each parameter incremented and decremented by a small offset. This provides the partial derivatives with respect to each parameter, along with information to discriminate meaningful values from numerical computational errors. The implementation of this mechanism in the open-source GSAS-II program is discussed. This new method is discussed as an important step towards the development of automated Rietveld refinement technology.
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Affiliation(s)
- B. H. Toby
- Argonne National Laboratory, 9700 S. Cass Avenue, 401/B4192, Lemont, IL 60439, USA
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35
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Peplow M. Robot chemist sparks row with claim it created new materials. Nature 2023:10.1038/d41586-023-03956-w. [PMID: 38087101 DOI: 10.1038/d41586-023-03956-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2024]
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36
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Peplow M. Google AI and robots join forces to build new materials. Nature 2023:10.1038/d41586-023-03745-5. [PMID: 38030771 DOI: 10.1038/d41586-023-03745-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2023]
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