Zhong-Hao Ye, Feng Guo, Chuan-Guo Chai, Yu-Shi Wen, Zheng-Rong Zhang, Heng-Shuai Li, Shou-Xin Cui, Gui-Qing Zhang, Xiao-Chun Wang
In this work, we report the discovery of energy cocrystals using an efficient iterative workflow combining an evolutionary algorithm and a machine learning potential (MLP). The compound 2,4,6,8,10,12-Hexanitro-2,4,6,8,10,12-hexaazaisowurtzitane (CL-20) has attracted significant attention owing to its higher energy density than traditional energetic materials. However, the higher sensitivity has limited its applications. An important way to reduce its sensitivity involves cocrystal engineering with traditional explosives. Many cocrystal structures are expected to be composed of these two components. We developed an efficient iterative workflow to explore the phase space of CL-20 and 1,3,5,7-tetranitro-1,3,5,7-tetraazacyclooctane (HMX) cocrystals using an evolutionary algorithm and an MLP. The algorithm was based on the Universal Structure Predictor: Evolutionary Xtallography (USPEX) software, and the MLP was the reactive force field with neural networks (ReaxFF-nn ) model. A set of high-density cocrystal structures was produced through this workflow; these structures were further checked via first-principles geometry optimizations. After careful screening, we identified several high-density cocrystal structures with densities of up to 1.937 g/cm3 and HMX: CL-20 ratios of 1:1 and 1:2. The influence of hydrogen bonds on the formation of high-density cocrystals was also discussed, and a roughly linear relationship was found between energy and density.
{"title":"Searching new cocrystal structures of CL-20 and HMX via evolutionary algorithm and machine learning potential","authors":"Zhong-Hao Ye, Feng Guo, Chuan-Guo Chai, Yu-Shi Wen, Zheng-Rong Zhang, Heng-Shuai Li, Shou-Xin Cui, Gui-Qing Zhang, Xiao-Chun Wang","doi":"10.20517/jmi.2023.37","DOIUrl":"https://doi.org/10.20517/jmi.2023.37","url":null,"abstract":"In this work, we report the discovery of energy cocrystals using an efficient iterative workflow combining an evolutionary algorithm and a machine learning potential (MLP). The compound 2,4,6,8,10,12-Hexanitro-2,4,6,8,10,12-hexaazaisowurtzitane (CL-20) has attracted significant attention owing to its higher energy density than traditional energetic materials. However, the higher sensitivity has limited its applications. An important way to reduce its sensitivity involves cocrystal engineering with traditional explosives. Many cocrystal structures are expected to be composed of these two components. We developed an efficient iterative workflow to explore the phase space of CL-20 and 1,3,5,7-tetranitro-1,3,5,7-tetraazacyclooctane (HMX) cocrystals using an evolutionary algorithm and an MLP. The algorithm was based on the Universal Structure Predictor: Evolutionary Xtallography (USPEX) software, and the MLP was the reactive force field with neural networks (ReaxFF-nn ) model. A set of high-density cocrystal structures was produced through this workflow; these structures were further checked via first-principles geometry optimizations. After careful screening, we identified several high-density cocrystal structures with densities of up to 1.937 g/cm3 and HMX: CL-20 ratios of 1:1 and 1:2. The influence of hydrogen bonds on the formation of high-density cocrystals was also discussed, and a roughly linear relationship was found between energy and density.","PeriodicalId":476895,"journal":{"name":"Journal of materials informatics","volume":"26 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141109977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Haber-Bosch (H-B) process, which is widely used in industry to synthesize ammonia, leads to serious energy and environment-related issues. The electrochemical nitrogen reduction reaction (eNRR) is the most promising candidate to replace H-B processes because it is more energy-efficient and environmentally friendly. Atomic-level catalysts, such as single-atom and double-atom catalysts (SACs and DACs), are of great interest due to their high atomic utilization and activity. The synergy between the metal atoms and two-dimensional (2D) support not only modulates the local electronic structure of the catalyst but also controls the catalytic performance. In this article, we explored the eNRR performance of 2D Fe3@Nx C20-x (x = 0~4), whose structure was based on the experimentally synthesized Ag3@C20 sheet, by means of density functional theory calculations. Through calculations, we found that the 2D Fe3@N4C16 with Fe2 site coordinated with four N is a promising eNRR catalyst: the limiting potential is as low as -0.45 V, and the competing hydrogen evolution reaction can be effectively suppressed. Our work not only confirms that the coordination environment of the metal site is crucial for the electrocatalytic activity but also provides a new guideline for designing low-cost eNRR catalysts with high efficiency.
{"title":"Regulating the electrocatalytic performance for nitrogen reduction reaction by tuning the N contents in Fe<sub>3</sub>@N<sub><i>x</i></sub>C<sub>20-<i>x</i></sub> (<i>x</i> = 0~4): a DFT exploration","authors":"Bing Han, Fengyu Li","doi":"10.20517/jmi.2023.32","DOIUrl":"https://doi.org/10.20517/jmi.2023.32","url":null,"abstract":"The Haber-Bosch (H-B) process, which is widely used in industry to synthesize ammonia, leads to serious energy and environment-related issues. The electrochemical nitrogen reduction reaction (eNRR) is the most promising candidate to replace H-B processes because it is more energy-efficient and environmentally friendly. Atomic-level catalysts, such as single-atom and double-atom catalysts (SACs and DACs), are of great interest due to their high atomic utilization and activity. The synergy between the metal atoms and two-dimensional (2D) support not only modulates the local electronic structure of the catalyst but also controls the catalytic performance. In this article, we explored the eNRR performance of 2D Fe3@Nx C20-x (x = 0~4), whose structure was based on the experimentally synthesized Ag3@C20 sheet, by means of density functional theory calculations. Through calculations, we found that the 2D Fe3@N4C16 with Fe2 site coordinated with four N is a promising eNRR catalyst: the limiting potential is as low as -0.45 V, and the competing hydrogen evolution reaction can be effectively suppressed. Our work not only confirms that the coordination environment of the metal site is crucial for the electrocatalytic activity but also provides a new guideline for designing low-cost eNRR catalysts with high efficiency.","PeriodicalId":476895,"journal":{"name":"Journal of materials informatics","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135818809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Large-scale atomistic simulations of two-dimensional (2D) materials rely on highly accurate and efficient force fields. Here, we present a physics-infused machine learning framework that enables the efficient development and interpretability of interatomic interaction models for 2D materials. By considering the characteristics of chemical bonds and structural topology, we have devised a set of efficient descriptors. This enables accurate force field training using a small dataset. The machine learning force fields show great success in describing the phase transformation and domain switching behaviors of monolayer Group IV monochalcogenides, e.g., GeSe and PbTe. Notably, this type of force field can be readily extended to other non-transition 2D systems, such as hexagonal boron nitride (h BN), leveraging their structural similarity. Our work provides a straightforward but accurate extension of simulation time and length scales for 2D materials.
{"title":"Physics infused machine learning force fields for 2D materials monolayers","authors":"Yang Yang, Bo Xu, Hongxiang Zong","doi":"10.20517/jmi.2023.31","DOIUrl":"https://doi.org/10.20517/jmi.2023.31","url":null,"abstract":"Large-scale atomistic simulations of two-dimensional (2D) materials rely on highly accurate and efficient force fields. Here, we present a physics-infused machine learning framework that enables the efficient development and interpretability of interatomic interaction models for 2D materials. By considering the characteristics of chemical bonds and structural topology, we have devised a set of efficient descriptors. This enables accurate force field training using a small dataset. The machine learning force fields show great success in describing the phase transformation and domain switching behaviors of monolayer Group IV monochalcogenides, e.g., GeSe and PbTe. Notably, this type of force field can be readily extended to other non-transition 2D systems, such as hexagonal boron nitride (h BN), leveraging their structural similarity. Our work provides a straightforward but accurate extension of simulation time and length scales for 2D materials.","PeriodicalId":476895,"journal":{"name":"Journal of materials informatics","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135934721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The atomic structures of solid-solid interfaces in materials are of fundamental importance for understanding the physical properties of interfacial materials, which is, however, difficult to determine both in experimental and theoretical approaches. New theoretical methodologies utilizing various global optimization algorithms and machine learning (ML) potentials have emerged in recent years, offering a promising approach to unraveling interfacial structures. In this review, we give a concise overview of state-of-the-art techniques employed in the studies of interfacial structures, e.g., ML-assisted phenomenological theory for the global search of interface structure (ML-interface). We also present a few applications of these methodologies.
{"title":"Recent advances in the interface structure prediction for heteromaterial systems","authors":"Ji-Li Li, Ye-Fei Li","doi":"10.20517/jmi.2023.24","DOIUrl":"https://doi.org/10.20517/jmi.2023.24","url":null,"abstract":"The atomic structures of solid-solid interfaces in materials are of fundamental importance for understanding the physical properties of interfacial materials, which is, however, difficult to determine both in experimental and theoretical approaches. New theoretical methodologies utilizing various global optimization algorithms and machine learning (ML) potentials have emerged in recent years, offering a promising approach to unraveling interfacial structures. In this review, we give a concise overview of state-of-the-art techniques employed in the studies of interfacial structures, e.g., ML-assisted phenomenological theory for the global search of interface structure (ML-interface). We also present a few applications of these methodologies.","PeriodicalId":476895,"journal":{"name":"Journal of materials informatics","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135883325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xiaoyu Chong, Wei Yu, Yingxue Liang, Shun-Li Shang, Chao Li, Aimin Zhang, Yan Wei, Xingyu Gao, Yi Wang, Jing Feng, Li Chen, Haifeng Song, Zi-Kui Liu
Thermodynamic calculations of Ellingham diagrams and the forming oxides have been performed relevant to the Pt-based alloys Pt82Al12M6 (M = Cr, Hf, Pt, and Ta). The predicted Ellingham diagrams indicate that the elements Hf and Al are easy to oxidize, followed by Ta and Cr, while Pt is extremely difficult to oxidize. Oxidation experiments characterized by X-ray diffraction (XRD) and electron probe micro-analyzers verify the present thermodynamic predictions, showing that the best alloy with superior oxidation resistance is Pt82Al12Cr6, followed by Pt88Al12 due to the formation of the dense and continuous α-Al2O3 scale on the surface of alloys; while the worse alloy is Pt82Al12Hf6 followed by Pt82Al12Ta6 due to drastic internal oxidation and the formation of deleterious HfO2, AlTaO4, and Ta2O5. The present work, combining computations with experimental verifications, provides a fundamental understanding and knowledgebase to develop Pt-based superalloys with superior oxidation resistance that can be used in ultra-high temperatures.
{"title":"Understanding oxidation resistance of Pt-based alloys through computations of Ellingham diagrams with experimental verifications","authors":"Xiaoyu Chong, Wei Yu, Yingxue Liang, Shun-Li Shang, Chao Li, Aimin Zhang, Yan Wei, Xingyu Gao, Yi Wang, Jing Feng, Li Chen, Haifeng Song, Zi-Kui Liu","doi":"10.20517/jmi.2023.17","DOIUrl":"https://doi.org/10.20517/jmi.2023.17","url":null,"abstract":"Thermodynamic calculations of Ellingham diagrams and the forming oxides have been performed relevant to the Pt-based alloys Pt82Al12M6 (M = Cr, Hf, Pt, and Ta). The predicted Ellingham diagrams indicate that the elements Hf and Al are easy to oxidize, followed by Ta and Cr, while Pt is extremely difficult to oxidize. Oxidation experiments characterized by X-ray diffraction (XRD) and electron probe micro-analyzers verify the present thermodynamic predictions, showing that the best alloy with superior oxidation resistance is Pt82Al12Cr6, followed by Pt88Al12 due to the formation of the dense and continuous α-Al2O3 scale on the surface of alloys; while the worse alloy is Pt82Al12Hf6 followed by Pt82Al12Ta6 due to drastic internal oxidation and the formation of deleterious HfO2, AlTaO4, and Ta2O5. The present work, combining computations with experimental verifications, provides a fundamental understanding and knowledgebase to develop Pt-based superalloys with superior oxidation resistance that can be used in ultra-high temperatures.","PeriodicalId":476895,"journal":{"name":"Journal of materials informatics","volume":"2013 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136012669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A comprehensive understanding of the interaction between liquids and two-dimensional (2D) materials is pivotal for the manipulation, transfer, and assembly of 2D materials across a wide range of applications, from liquid cell microscopy to hydrovoltaics. This review discusses this interaction by surveying the intrinsic wettability of suspended 2D materials and the apparent wettability of substrate-supported 2D materials, both of which have recently been revealed through water contact angle (WCA) experiments. We discuss important factors that can affect the apparent WCA, including thin film elasticity, surface contamination, and the microstructure and electronic state of the underneath substrate. We also discuss some microscopic-level insights into the 2D material-liquid interface that have recently been provided via spectroscopy characterizations and surface energy measurements. By discussing the latest experimental advancements in characterizing the interaction between 2D materials and liquid droplets, this review aims to inspire future theoretical progress capable of unraveling the intricate and occasionally contradictory wetting behavior observed in 2D material systems.
{"title":"Characterizing the wetting behavior of 2D materials: a review","authors":"Chuanli Yu, Zhaohe Dai","doi":"10.20517/jmi.2023.27","DOIUrl":"https://doi.org/10.20517/jmi.2023.27","url":null,"abstract":"A comprehensive understanding of the interaction between liquids and two-dimensional (2D) materials is pivotal for the manipulation, transfer, and assembly of 2D materials across a wide range of applications, from liquid cell microscopy to hydrovoltaics. This review discusses this interaction by surveying the intrinsic wettability of suspended 2D materials and the apparent wettability of substrate-supported 2D materials, both of which have recently been revealed through water contact angle (WCA) experiments. We discuss important factors that can affect the apparent WCA, including thin film elasticity, surface contamination, and the microstructure and electronic state of the underneath substrate. We also discuss some microscopic-level insights into the 2D material-liquid interface that have recently been provided via spectroscopy characterizations and surface energy measurements. By discussing the latest experimental advancements in characterizing the interaction between 2D materials and liquid droplets, this review aims to inspire future theoretical progress capable of unraveling the intricate and occasionally contradictory wetting behavior observed in 2D material systems.","PeriodicalId":476895,"journal":{"name":"Journal of materials informatics","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135445108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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