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Zhang Y, Chen F, Liu Z, Ju Y, Cui D, Zhu J, Jiang X, Guo X, He J, Zhang L, Zhang X, Su Y. A materials terminology knowledge graph automatically constructed from text corpus. Sci Data 2024; 11:600. [PMID: 38849436 PMCID: PMC11161478 DOI: 10.1038/s41597-024-03448-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 05/31/2024] [Indexed: 06/09/2024] Open
Abstract
A scalable, reusable, and broad-coverage unified material knowledge representation shows its importance and will bring great benefits to data sharing among materials communities. A knowledge graph (KG) for materials terminology, which is a formal collection of term entities and relationships, is conceptually important to achieve this goal. In this work, we propose a KG for materials terminology, named Materials Genome Engineering Database Knowledge Graph (MGED-KG), which is automatically constructed from text corpus via natural language processing. MGED-KG is the most comprehensive KG for materials terminology in both Chinese and English languages, consisting of 8,660 terms and their explanations. It encompasses 11 principal categories, such as Metals, Composites, Nanomaterials, each with two or three levels of subcategories, resulting in a total of 235 distinct category labels. For further application, a knowledge web system based on MGED-KG is developed and shows its great power in improving data sharing efficiency from the aspects of query expansion, term, and data recommendation.
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Affiliation(s)
- Yuwei Zhang
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China
| | - Fangyi Chen
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China
| | - Zeyi Liu
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China
| | - Yunzhuo Ju
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China
| | - Dongliang Cui
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China
| | - Jinyi Zhu
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China
| | - Xue Jiang
- Beijing Advanced Innovation Center for Materials Genome Engineering, Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing, 100083, China.
- Liaoning Academy of Materials, Shenyang, 110000, Liaoning, China.
- Shunde Innovation School, University of Science and Technology Beijing, Guangdong, 528399, China.
| | - Xi Guo
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China.
- Beijing Key Laboratory of Knowledge Engineering for Materials, Beijing, 100083, China.
| | - Jie He
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China.
- Liaoning Academy of Materials, Shenyang, 110000, Liaoning, China.
| | - Lei Zhang
- Beijing Advanced Innovation Center for Materials Genome Engineering, Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing, 100083, China
| | - Xiaotong Zhang
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China
| | - Yanjing Su
- Beijing Advanced Innovation Center for Materials Genome Engineering, Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing, 100083, China
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Wang G, Wang C, Zhang X, Li Z, Zhou J, Sun Z. Machine learning interatomic potential: Bridge the gap between small-scale models and realistic device-scale simulations. iScience 2024; 27:109673. [PMID: 38646181 PMCID: PMC11033164 DOI: 10.1016/j.isci.2024.109673] [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] [Indexed: 04/23/2024] Open
Abstract
Machine learning interatomic potential (MLIP) overcomes the challenges of high computational costs in density-functional theory and the relatively low accuracy in classical large-scale molecular dynamics, facilitating more efficient and precise simulations in materials research and design. In this review, the current state of the four essential stages of MLIP is discussed, including data generation methods, material structure descriptors, six unique machine learning algorithms, and available software. Furthermore, the applications of MLIP in various fields are investigated, notably in phase-change memory materials, structure searching, material properties predicting, and the pre-trained universal models. Eventually, the future perspectives, consisting of standard datasets, transferability, generalization, and trade-off between accuracy and complexity in MLIPs, are reported.
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Affiliation(s)
- Guanjie Wang
- School of Materials Science and Engineering, Beihang University, Beijing 100191, China
- School of Integrated Circuit Science and Engineering, Beihang University, Beijing 100191, China
| | - Changrui Wang
- School of Materials Science and Engineering, Beihang University, Beijing 100191, China
| | - Xuanguang Zhang
- School of Materials Science and Engineering, Beihang University, Beijing 100191, China
| | - Zefeng Li
- School of Materials Science and Engineering, Beihang University, Beijing 100191, China
| | - Jian Zhou
- School of Materials Science and Engineering, Beihang University, Beijing 100191, China
| | - Zhimei Sun
- School of Materials Science and Engineering, Beihang University, Beijing 100191, China
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Miao R, Bissoli M, Basagni A, Marotta E, Corni S, Amendola V. Data-Driven Predetermination of Cu Oxidation State in Copper Nanoparticles: Application to the Synthesis by Laser Ablation in Liquid. J Am Chem Soc 2023; 145:25737-25752. [PMID: 37907392 PMCID: PMC10690790 DOI: 10.1021/jacs.3c09158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 10/12/2023] [Accepted: 10/18/2023] [Indexed: 11/02/2023]
Abstract
Copper-based nanocrystals are reference nanomaterials for integration into emerging green technologies, with laser ablation in liquid (LAL) being a remarkable technique for their synthesis. However, the achievement of a specific type of nanocrystal, among the whole library of nanomaterials available using LAL, has been until now an empirical endeavor based on changing synthesis parameters and characterizing the products. Here, we started from the bibliographic analysis of LAL synthesis of Cu-based nanocrystals to identify the relevant physical and chemical features for the predetermination of copper oxidation state. First, single features and their combinations were screened by linear regression analysis, also using a genetic algorithm, to find the best correlation with experimental output and identify the equation giving the best prediction of the LAL results. Then, machine learning (ML) models were exploited to unravel cross-correlations between features that are hidden in the linear regression analysis. Although the LAL-generated Cu nanocrystals may be present in a range of oxidation states, from metallic copper to cuprous oxide (Cu2O) and cupric oxide (CuO), in addition to the formation of other materials such as Cu2S and CuCN, ML was able to guide the experiments toward the maximization of the compounds in the greatest demand for integration in sustainable processes. This approach is of general applicability to other nanomaterials and can help understand the origin of the chemical pathways of nanocrystals generated by LAL, providing a rational guideline for the conscious predetermination of laser-synthesis parameters toward the desired compounds.
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Affiliation(s)
- Runpeng Miao
- Department of Chemical Sciences, University of Padova, 35131 Padova, Italy
| | - Michael Bissoli
- Department of Chemical Sciences, University of Padova, 35131 Padova, Italy
| | - Andrea Basagni
- Department of Chemical Sciences, University of Padova, 35131 Padova, Italy
| | - Ester Marotta
- Department of Chemical Sciences, University of Padova, 35131 Padova, Italy
| | - Stefano Corni
- Department of Chemical Sciences, University of Padova, 35131 Padova, Italy
| | - Vincenzo Amendola
- Department of Chemical Sciences, University of Padova, 35131 Padova, Italy
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Zhu X, Yang Y, Shu X, Xu T, Jing Y. Computational insights into the rational design of organic electrode materials for metal ion batteries. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2023. [DOI: 10.1002/wcms.1660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/17/2023]
Affiliation(s)
- Xinyue Zhu
- Jiangsu Co‐Innovation Centre of Efficient Processing and Utilization of Forest Resources, College of Chemical Engineering Nanjing Forestry University Nanjing China
| | - Youchao Yang
- Jiangsu Co‐Innovation Centre of Efficient Processing and Utilization of Forest Resources, College of Chemical Engineering Nanjing Forestry University Nanjing China
| | - Xipeng Shu
- Jiangsu Co‐Innovation Centre of Efficient Processing and Utilization of Forest Resources, College of Chemical Engineering Nanjing Forestry University Nanjing China
| | - Tianze Xu
- Jiangsu Co‐Innovation Centre of Efficient Processing and Utilization of Forest Resources, College of Chemical Engineering Nanjing Forestry University Nanjing China
| | - Yu Jing
- Jiangsu Co‐Innovation Centre of Efficient Processing and Utilization of Forest Resources, College of Chemical Engineering Nanjing Forestry University Nanjing China
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