Pub Date : 2024-11-07DOI: 10.1016/j.commatsci.2024.113495
Adam M. Krajewski , Jonathan W. Siegel , Zi-Kui Liu
Structure-informed materials informatics is a rapidly evolving discipline of materials science relying on the featurization of atomic structures or configurations to construct vector, voxel, graph, graphlet, and other representations useful for machine learning prediction of properties, fingerprinting, and generative design. This work discusses how current featurizers typically perform redundant calculations and how their efficiency could be improved by considering (1) fundamentals of crystallographic (orbits) equivalency to optimize ordered structures and (2) representation-dependent equivalency to optimize dilute, doped, and defect structures with broken symmetry. It also discusses and contrasts ways of (3) approximating random solid solutions occupying arbitrary lattices under such representations. Efficiency improvements discussed in this work were implemented within or python toolset for Structure-Informed Property and Feature Engineering with Neural Networks developed by authors since 2019 and shown to increase performance from 2 to 10 times for typical inputs. Throughout this work, the authors explicitly discuss how these advances can be applied to different kinds of similar tools in the community.
{"title":"Efficient structure-informed featurization and property prediction of ordered, dilute, and random atomic structures","authors":"Adam M. Krajewski , Jonathan W. Siegel , Zi-Kui Liu","doi":"10.1016/j.commatsci.2024.113495","DOIUrl":"10.1016/j.commatsci.2024.113495","url":null,"abstract":"<div><div>Structure-informed materials informatics is a rapidly evolving discipline of materials science relying on the featurization of atomic structures or configurations to construct vector, voxel, graph, graphlet, and other representations useful for machine learning prediction of properties, fingerprinting, and generative design. This work discusses how current featurizers typically perform redundant calculations and how their efficiency could be improved by considering (1) fundamentals of crystallographic (orbits) equivalency to optimize ordered structures and (2) representation-dependent equivalency to optimize dilute, doped, and defect structures with broken symmetry. It also discusses and contrasts ways of (3) approximating random solid solutions occupying arbitrary lattices under such representations. Efficiency improvements discussed in this work were implemented within <figure><img></figure> or <em>python toolset for Structure-Informed Property and Feature Engineering with Neural Networks</em> developed by authors since 2019 and shown to increase performance from 2 to 10 times for typical inputs. Throughout this work, the authors explicitly discuss how these advances can be applied to different kinds of similar tools in the community.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"247 ","pages":"Article 113495"},"PeriodicalIF":3.1,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142657112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-17DOI: 10.1016/j.commatsci.2024.113399
Hamid Ghasemi , Hessam Yazdani
Pursuing innovative materials through integrating machine learning (ML) with materials informatics hinges critically upon establishing accurate processing-structure–property-performance relationships and consistently applying them in training datasets. Pivotal to unraveling these relationships is an accurate representation of the microstructure in computational models. In this study, we use transmission electron microscopy (TEM) micrographs of carbon nanotubes (CNTs) within a polymer matrix to construct representative polymer-nanotube composite (PNC) models. We then simulate the models using the coarse-grained molecular dynamics (CG-MD) technique to elucidate the influence of filler morphology and aggregation on the mechanical properties of PNCs. Besides CNTs, we consider cyanoethyl nanotubes (C3NNT) as a representative of the carbon nitride family, which has remained largely unexplored as a PNC filler for load-bearing purposes. We employ the CG-MD results to train ML models—neural network (NN), support vector regression (SVR), and Gaussian process regression (GPR)—to predict the strain–stress responses of PNCs. Results indicate the profound influence of the filler morphology and aggregation on the elastic and shear stiffness of PNC composites. A high degree of transverse isotropy is observed in the mechanical behavior of composites with perfectly oriented fillers, with Poisson’s ratios surpassing conventional upper bounds observed in isotropic materials. For a given morphology, C3NNT composites exhibit higher stiffness in longitudinal and transverse directions than CNT composites. The ML models demonstrate accuracy in predicting the strain–stress response of the composites, with the GPR model showing the highest accuracy, followed by the NN and SVM models. This accuracy makes the ML models readily integrable into a multiscale modeling framework, significantly enhancing the efficiency of transferring information across scales.
通过将机器学习（ML）与材料信息学相整合来追求创新材料，关键在于建立准确的加工-结构-性能关系，并将其持续应用于训练数据集。要揭示这些关系，关键是在计算模型中准确表示微观结构。在本研究中，我们使用聚合物基体中碳纳米管（CNT）的透射电子显微镜（TEM）显微照片来构建具有代表性的聚合物-纳米管复合材料（PNC）模型。然后，我们使用粗粒度分子动力学（CG-MD）技术对模型进行模拟，以阐明填料形态和聚集对 PNC 机械性能的影响。除碳纳米管外，我们还将氰乙基纳米管（C3NNT）作为碳氮化物家族的代表，这种材料作为 PNC 填料用于承重目的在很大程度上仍未得到开发。我们利用 CG-MD 结果训练 ML 模型--神经网络 (NN)、支持向量回归 (SVR) 和高斯过程回归 (GPR)--以预测 PNC 的应变应力响应。结果表明，填料形态和聚集对 PNC 复合材料的弹性和剪切刚度有深远影响。在完全取向填料的复合材料机械行为中观察到了高度的横向各向同性，泊松比超过了在各向同性材料中观察到的传统上限。在给定形态下，C3NNT 复合材料在纵向和横向的刚度均高于 CNT 复合材料。ML 模型能准确预测复合材料的应变应力响应，其中 GPR 模型的准确度最高，其次是 NN 和 SVM 模型。这种准确性使得 ML 模型很容易集成到多尺度建模框架中，从而大大提高了跨尺度信息传递的效率。
{"title":"Atomistic simulation and machine learning predictions of mechanical response in nanotube-polymer composites considering filler morphology and aggregation","authors":"Hamid Ghasemi , Hessam Yazdani","doi":"10.1016/j.commatsci.2024.113399","DOIUrl":"10.1016/j.commatsci.2024.113399","url":null,"abstract":"<div><div>Pursuing innovative materials through integrating machine learning (ML) with materials informatics hinges critically upon establishing accurate processing-structure–property-performance relationships and consistently applying them in training datasets. Pivotal to unraveling these relationships is an accurate representation of the microstructure in computational models. In this study, we use transmission electron microscopy (TEM) micrographs of carbon nanotubes (CNTs) within a polymer matrix to construct representative polymer-nanotube composite (PNC) models. We then simulate the models using the coarse-grained molecular dynamics (CG-MD) technique to elucidate the influence of filler morphology and aggregation on the mechanical properties of PNCs. Besides CNTs, we consider cyanoethyl nanotubes (C<sub>3</sub>NNT) as a representative of the carbon nitride family, which has remained largely unexplored as a PNC filler for load-bearing purposes. We employ the CG-MD results to train ML models—neural network (NN), support vector regression (SVR), and Gaussian process regression (GPR)—to predict the strain–stress responses of PNCs. Results indicate the profound influence of the filler morphology and aggregation on the elastic and shear stiffness of PNC composites. A high degree of transverse isotropy is observed in the mechanical behavior of composites with perfectly oriented fillers, with Poisson’s ratios surpassing conventional upper bounds observed in isotropic materials. For a given morphology, C<sub>3</sub>NNT composites exhibit higher stiffness in longitudinal and transverse directions than CNT composites. The ML models demonstrate accuracy in predicting the strain–stress response of the composites, with the GPR model showing the highest accuracy, followed by the NN and SVM models. This accuracy makes the ML models readily integrable into a multiscale modeling framework, significantly enhancing the efficiency of transferring information across scales.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"246 ","pages":"Article 113399"},"PeriodicalIF":3.1,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142445865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-17DOI: 10.1016/j.commatsci.2024.113449
Ning Wang , Yushun Zhao , Zhenxing Cao , Gong Cheng , Junjiao Li , Guoxin Zhao , Yuna Sang , Chao Sui , Xiaodong He , Chao Wang
In recent times, the bonded graphene and carbon nanotubes (CNTs) hybrid (BGCH) film has garnered considerable attention due to its exceptional mechanical, thermal, and electrical properties. Its inherent hydrophobic characteristics render it promising for diverse applications such as seawater desalination and anti-icing strategies. However, the wettability, particularly the dynamics of water droplet impact on the film surface, remains unclear. In this study, employing molecular dynamics simulations, we constructed a model of the BGCH film and observed four distinct impact phenomena (ball bouncing, spreading, retraction, pancake bouncing) when water droplets struck BGCH with short CNTs. Notably, at a velocity of 12 Å/ps, a pancake bouncing pattern emerged, markedly reducing the duration of solid–liquid contact. Moreover, the impact behaviors were found to be intricately linked to the structural parameters and inclined impact induced droplet flow on the substrate surface, augmenting the contact time. Furthermore, longer CNTs dissipated more energy from the water droplet through structural deformation. This work systematically investigates the nanodroplet bouncing behaviors of BGCH, providing theoretical insights for their applications in hydrophobicity fields.
{"title":"Nanodroplet bouncing behaviors of bonded graphene-carbon nanotube hybrid film","authors":"Ning Wang , Yushun Zhao , Zhenxing Cao , Gong Cheng , Junjiao Li , Guoxin Zhao , Yuna Sang , Chao Sui , Xiaodong He , Chao Wang","doi":"10.1016/j.commatsci.2024.113449","DOIUrl":"10.1016/j.commatsci.2024.113449","url":null,"abstract":"<div><div>In recent times, the bonded graphene and carbon nanotubes (CNTs) hybrid (BGCH) film has garnered considerable attention due to its exceptional mechanical, thermal, and electrical properties. Its inherent hydrophobic characteristics render it promising for diverse applications such as seawater desalination and anti-icing strategies. However, the wettability, particularly the dynamics of water droplet impact on the film surface, remains unclear. In this study, employing molecular dynamics simulations, we constructed a model of the BGCH film and observed four distinct impact phenomena (ball bouncing, spreading, retraction, pancake bouncing) when water droplets struck BGCH with short CNTs. Notably, at a velocity of 12 Å/ps, a pancake bouncing pattern emerged, markedly reducing the duration of solid–liquid contact. Moreover, the impact behaviors were found to be intricately linked to the structural parameters and inclined impact induced droplet flow on the substrate surface, augmenting the contact time. Furthermore, longer CNTs dissipated more energy from the water droplet through structural deformation. This work systematically investigates the nanodroplet bouncing behaviors of BGCH, providing theoretical insights for their applications in hydrophobicity fields.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"246 ","pages":"Article 113449"},"PeriodicalIF":3.1,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142445864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-16DOI: 10.1016/j.commatsci.2024.113445
Archit Anand, Priyanka Kumari, Ajay Kumar Kalyani
Computational methods, such as the Density Functional Theory (DFT), have long been a reliable tool for predicting material properties. However, their use in high-throughput screening has been limited due to computational costs. In this paper, we present a graph-based machine learning (ML) framework that overcomes these limitations, offering a more efficient approach to material selection and property prediction. Our framework, which includes a knowledge graph (KG) approach, and a graph neural network (GNN) based model, significantly reduces the search space by filtering materials from the Crystallography Open Database (COD) using KGs. We then use a modified Gated Graph ConvNet (GatedGCN) model to predict the maximum longitudinal piezoelectric modulus ( of the screened materials. Based on the study, a list of new perovskite-based piezoelectric materials is shown with the top candidate reaching a value of as high as ∼ 10.81 C/m2.
{"title":"High throughput screening of new piezoelectric materials using graph machine learning and knowledge graph approach","authors":"Archit Anand, Priyanka Kumari, Ajay Kumar Kalyani","doi":"10.1016/j.commatsci.2024.113445","DOIUrl":"10.1016/j.commatsci.2024.113445","url":null,"abstract":"<div><div>Computational methods, such as the Density Functional Theory (DFT), have long been a reliable tool for predicting material properties. However, their use in high-throughput screening has been limited due to computational costs. In this paper, we present a graph-based machine learning (ML) framework that overcomes these limitations, offering a more efficient approach to material selection and property prediction. Our framework, which includes a knowledge graph (KG) approach, and a graph neural network (GNN) based model, significantly reduces the search space by filtering materials from the Crystallography Open Database (COD) using KGs. We then use a modified Gated Graph ConvNet (GatedGCN) model to predict the maximum longitudinal piezoelectric modulus (<span><math><mrow><msub><mrow><msub><mrow><mo>‖</mo><mi>e</mi></mrow><mrow><mi>ij</mi></mrow></msub><mrow><mo>‖</mo></mrow></mrow><mrow><mi>max</mi></mrow></msub><mrow><mo>)</mo></mrow></mrow></math></span> of the screened materials. Based on the study, a list of new perovskite-based piezoelectric materials is shown with the top candidate reaching a value of <span><math><msub><mrow><msub><mrow><mo>‖</mo><mi>e</mi></mrow><mrow><mi>ij</mi></mrow></msub><mrow><mo>‖</mo></mrow></mrow><mrow><mi>max</mi></mrow></msub></math></span> as high as ∼ 10.81 C/m<sup>2</sup>.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"246 ","pages":"Article 113445"},"PeriodicalIF":3.1,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142441069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The phase-field method has become a useful tool for the simulation of classical metallurgical phase transformations as well as other phenomena related to materials science. The thermodynamic consistency that forms the basis of these formulations lends to its strong predictive capabilities and utility. However, a strong impediment to the usage of the method for typical applied problems of industrial and academic relevance is the significant overhead with regard to the code development and know-how required for quantitative model formulations. In this paper, we report the development of an open-source phase-field software stack that contains generic formulations for the simulation of multiphase and multi-component phase transformations. The solvers incorporate thermodynamic coupling that allows the realization of simulations with real alloys in scenarios directly relevant to the materials industry. Further, the solvers utilize parallelization strategies using either multiple CPUs or GPUs to provide cross-platform portability and usability on available supercomputing machines. Finally, the solver stack also contains a graphical user interface to gradually introduce the usage of the software. The user interface also provides a collection of post-processing tools that allow the estimation of useful metrics related to microstructural evolution.
相场法已成为模拟经典冶金相变以及与材料科学相关的其他现象的有用工具。构成这些公式基础的热力学一致性使其具有很强的预测能力和实用性。然而，将该方法用于解决工业和学术界相关的典型应用问题的一个重大障碍是，定量模型公式化所需的代码开发和技术诀窍方面的巨大开销。在本文中，我们报告了开源相场软件栈的开发情况，该软件栈包含用于模拟多相和多组分相变的通用公式。求解器包含热力学耦合，可在与材料行业直接相关的场景中使用真实合金实现模拟。此外，求解器采用并行化策略，使用多个 CPU 或 GPU，提供跨平台可移植性，并可在现有的超级计算机上使用。最后，求解器堆栈还包含一个图形用户界面，用于逐步介绍软件的使用方法。用户界面还提供了一系列后处理工具，可以估算与微结构演变相关的有用指标。
{"title":"MicroSim: A high-performance phase-field solver based on CPU and GPU implementations","authors":"Tanmay Dutta , Dasari Mohan , Saurav Shenoy , Nasir Attar , Abhishek Kalokhe , Ajay Sagar , Swapnil Bhure , Swaroop S. Pradhan , Jitendriya Praharaj , Subham Mridha , Anshika Kushwaha , Vaishali Shah , M.P. Gururajan , V. Venkatesh Shenoi , Gandham Phanikumar , Saswata Bhattacharyya , Abhik Choudhury","doi":"10.1016/j.commatsci.2024.113438","DOIUrl":"10.1016/j.commatsci.2024.113438","url":null,"abstract":"<div><div>The phase-field method has become a useful tool for the simulation of classical metallurgical phase transformations as well as other phenomena related to materials science. The thermodynamic consistency that forms the basis of these formulations lends to its strong predictive capabilities and utility. However, a strong impediment to the usage of the method for typical applied problems of industrial and academic relevance is the significant overhead with regard to the code development and know-how required for quantitative model formulations. In this paper, we report the development of an open-source phase-field software stack that contains generic formulations for the simulation of multiphase and multi-component phase transformations. The solvers incorporate thermodynamic coupling that allows the realization of simulations with real alloys in scenarios directly relevant to the materials industry. Further, the solvers utilize parallelization strategies using either multiple CPUs or GPUs to provide cross-platform portability and usability on available supercomputing machines. Finally, the solver stack also contains a graphical user interface to gradually introduce the usage of the software. The user interface also provides a collection of post-processing tools that allow the estimation of useful metrics related to microstructural evolution.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"246 ","pages":"Article 113438"},"PeriodicalIF":3.1,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142441070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-15DOI: 10.1016/j.commatsci.2024.113444
Russlan Jaafreh, Surjeet Kumar, Kotiba Hamad, Jung-Gu Kim
This research encompasses a comprehensive exploration of feature compression and graphical representation in the domain of single crystal materials. The study introduces a novel framework known as Material Fingerprint (MatPrint), leveraging crystal structure and composition features generated via the Magpie platform. MatPrint incorporates 576 crystal and composition features, transformed into 64-bit binary values through the IEEE-754 standard. These features contribute to a nuanced binary graphical representation of materials, emphasizing sensitivity to both composition and crystal structure, particularly beneficial in distinguishing unique graphical profiles for each material, including polymorphs. Additionally, the current MatPrint representations of 2021 compounds and their formation energy were used in a learning process using a pretrained ResNet-18 model to establish a baseline for the efficiency of the representation in data-driven tasks regarding material property prediction, the employed model exhibited a validation loss of 0.18 eV/atom which proposes that the current model can be used extensively with a larger dataset that can be used in different areas of material informatics. Finally, the proposed methodology plays a crucial role in the reversible compression of tabular data derived from the feature generation process, facilitating its use in diverse machine and deep learning models.
{"title":"Introducing Materials Fingerprint (MatPrint): A novel method in graphical material representation and features compression","authors":"Russlan Jaafreh, Surjeet Kumar, Kotiba Hamad, Jung-Gu Kim","doi":"10.1016/j.commatsci.2024.113444","DOIUrl":"10.1016/j.commatsci.2024.113444","url":null,"abstract":"<div><div>This research encompasses a comprehensive exploration of feature compression and graphical representation in the domain of single crystal materials. The study introduces a novel framework known as Material Fingerprint (<strong>MatPrint</strong>), leveraging crystal structure and composition features generated via the Magpie platform. <strong>MatPrint</strong> incorporates 576 crystal and composition features, transformed into 64-bit binary values through the IEEE-754 standard. These features contribute to a nuanced binary graphical representation of materials, emphasizing sensitivity to both composition and crystal structure, particularly beneficial in distinguishing unique graphical profiles for each material, including polymorphs. Additionally, the current MatPrint representations of 2021 compounds and their formation energy were used in a learning process using a pretrained ResNet-18 model to establish a baseline for the efficiency of the representation in data-driven tasks regarding material property prediction, the employed model exhibited a validation loss of 0.18 eV/atom which proposes that the current model can be used extensively with a larger dataset that can be used in different areas of material informatics. Finally, the proposed methodology plays a crucial role in the reversible compression of tabular data derived from the feature generation process, facilitating its use in diverse machine and deep learning models.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"246 ","pages":"Article 113444"},"PeriodicalIF":3.1,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142432967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-15DOI: 10.1016/j.commatsci.2024.113447
Otmane El Ouardi, Jones Alami, Mohammed Makha
Bismuth Vanadate (BiVO4) is a promising candidate for solar water splitting due to its excellent photocatalytic properties. The monoclinic scheelite structure, in particular, is noted for its high-water oxidation activity and has an energy gap of 2.4–2.5 eV. Recently, other phases, especially the tetragonal zircon phase, have also demonstrated interesting photocatalytic properties. Therefore, our study aims to provide a direct comparison of the photocatalytic capabilities of different BiVO4 structures. To do so, we employed a comprehensive approach to understand the photocatalytic activity of various BiVO4 crystalline structures, focusing on their structural, electronic, and optical properties using density functional theory (DFT). To describe the electronic properties more accurately, we used corrected density functional theory. We investigated the impact of on-site Coulomb interaction on the structural and electronic properties of BiVO4. Our results indicate that the monoclinic scheelite structure has a narrow band gap (2.44 eV), light hole effective masses, the largest dipole moment, stronger visible light absorption, and a suitable valence band edge position. These features contribute to its excellent photocatalytic activity, making it a strong candidate for use as a photoanode in photoelectrochemical cells. Moreover, the tetragonal zircon phase exhibits light electron effective masses compared to the scheelite phases, along with suitable conduction and valence band edge positions and a direct band gap. These properties suggest its potential application as a photocathode for solar water splitting. Our findings provide valuable insights into enhancing the overall performance of BiVO4 for solar water splitting applications, highlighting the distinct advantages of both the monoclinic scheelite and tetragonal zircon phases.
{"title":"Understanding the photocatalytic activity of bismuth vanadate phases for solar water splitting: A DFT-based comparative study","authors":"Otmane El Ouardi, Jones Alami, Mohammed Makha","doi":"10.1016/j.commatsci.2024.113447","DOIUrl":"10.1016/j.commatsci.2024.113447","url":null,"abstract":"<div><div>Bismuth Vanadate (BiVO<sub>4</sub>) is a promising candidate for solar water splitting due to its excellent photocatalytic properties. The monoclinic scheelite structure, in particular, is noted for its high-water oxidation activity and has an energy gap of 2.4–2.5 eV. Recently, other phases, especially the tetragonal zircon phase, have also demonstrated interesting photocatalytic properties. Therefore, our study aims to provide a direct comparison of the photocatalytic capabilities of different BiVO<sub>4</sub> structures. To do so, we employed a comprehensive approach to understand the photocatalytic activity of various BiVO<sub>4</sub> crystalline structures, focusing on their structural, electronic, and optical properties using density functional theory (DFT). To describe the electronic properties more accurately, we used corrected density functional theory. We investigated the impact of on-site Coulomb interaction on the structural and electronic properties of BiVO<sub>4</sub>. Our results indicate that the monoclinic scheelite structure has a narrow band gap (2.44 eV), light hole effective masses, the largest dipole moment, stronger visible light absorption, and a suitable valence band edge position. These features contribute to its excellent photocatalytic activity, making it a strong candidate for use as a photoanode in photoelectrochemical cells. Moreover, the tetragonal zircon phase exhibits light electron effective masses compared to the scheelite phases, along with suitable conduction and valence band edge positions and a direct band gap. These properties suggest its potential application as a photocathode for solar water splitting. Our findings provide valuable insights into enhancing the overall performance of BiVO<sub>4</sub> for solar water splitting applications, highlighting the distinct advantages of both the monoclinic scheelite and tetragonal zircon phases.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"246 ","pages":"Article 113447"},"PeriodicalIF":3.1,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142438094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-15DOI: 10.1016/j.commatsci.2024.113433
Gopal R. Iyer , Shashikant Kumar , Edgar Josué Landinez Borda , Babak Sadigh , Sebastien Hamel , Vasily Bulatov , Vincenzo Lordi , Amit Samanta
Density functional theory (DFT) is routinely used to make electronic structure predictions for high-throughput screening of materials and molecules for technologically relevant areas, like the identification of better catalysts, electronic materials, and drug discovery. However, the DFT formalism is limited by (a) its poor (quadratic-to-quartic) scaling, and (b) the need to perform repeated eigenvalue computations of the electronic Hamiltonian as part of its self-consistent field (SCF) iteration procedure to obtain the converged ground state electron density, . Approaches that directly predict of a structure with high accuracy can accelerate conventional SCF calculations and can also be used in linearly scaling methods such as orbital-free DFT. To this end, we present a procedure to predict the ground state electron density of molecular and periodic three-dimensional systems directly from the atomic structure with a particular emphasis on physical interpretability. In our framework, is modeled using many-body correlation descriptors that accurately capture the effects of local atomic arrangements in the neighborhood of a grid point. Our use of a linear regression scheme to fit to charge density data enables transparent analysis of the relative contributions of various types of local atomic correlations. By systematically including increasingly complex correlations, our model is shown to accurately predict for a variety of chemically and electronically diverse systems — amorphous Ge, Al(001) slab, crystalline , molecular benzene, and polyethylene. We then demonstrate a symbolic regression-based protocol to construct easily computable, interpretable features from lower-order correlations that significantly improves our electron density predictions with effectively no increase in the computational cost.
{"title":"Interpretable, extensible linear and symbolic regression models for charge density prediction using a hierarchy of many-body correlation descriptors","authors":"Gopal R. Iyer , Shashikant Kumar , Edgar Josué Landinez Borda , Babak Sadigh , Sebastien Hamel , Vasily Bulatov , Vincenzo Lordi , Amit Samanta","doi":"10.1016/j.commatsci.2024.113433","DOIUrl":"10.1016/j.commatsci.2024.113433","url":null,"abstract":"<div><div>Density functional theory (DFT) is routinely used to make electronic structure predictions for high-throughput screening of materials and molecules for technologically relevant areas, like the identification of better catalysts, electronic materials, and drug discovery. However, the DFT formalism is limited by (a) its poor (quadratic-to-quartic) scaling, and (b) the need to perform repeated eigenvalue computations of the electronic Hamiltonian as part of its self-consistent field (SCF) iteration procedure to obtain the converged ground state electron density, <span><math><mrow><mi>ρ</mi><mfenced><mrow><mi>r</mi></mrow></mfenced></mrow></math></span>. Approaches that directly predict <span><math><mrow><mi>ρ</mi><mfenced><mrow><mi>r</mi></mrow></mfenced></mrow></math></span> of a structure with high accuracy can accelerate conventional SCF calculations and can also be used in linearly scaling methods such as orbital-free DFT. To this end, we present a procedure to predict the ground state electron density of molecular and periodic three-dimensional systems directly from the atomic structure with a particular emphasis on physical interpretability. In our framework, <span><math><mrow><mi>ρ</mi><mfenced><mrow><mi>r</mi></mrow></mfenced></mrow></math></span> is modeled using many-body correlation descriptors that accurately capture the effects of local atomic arrangements in the neighborhood of a grid point. Our use of a linear regression scheme to fit to charge density data enables transparent analysis of the relative contributions of various types of local atomic correlations. By systematically including increasingly complex correlations, our model is shown to accurately predict <span><math><mrow><mi>ρ</mi><mfenced><mrow><mi>r</mi></mrow></mfenced></mrow></math></span> for a variety of chemically and electronically diverse systems — amorphous Ge, Al(001) slab, crystalline <span><math><mrow><msub><mrow><mi>Ga</mi></mrow><mrow><mn>2</mn></mrow></msub><msub><mrow><mi>O</mi></mrow><mrow><mn>3</mn></mrow></msub></mrow></math></span>, molecular benzene, and polyethylene. We then demonstrate a symbolic regression-based protocol to construct easily computable, interpretable features from lower-order correlations that significantly improves our electron density predictions with effectively no increase in the computational cost.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"246 ","pages":"Article 113433"},"PeriodicalIF":3.1,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142438020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-14DOI: 10.1016/j.commatsci.2024.113450
Jinyan Liu , Guanghao Zhang , Jianyong Wang , Hong Zhang , Ye Han
Cu-Sn alloy materials are widely used in electronic industry, aerospace and 3D printing. When studying the structure and properties of materials, a contradiction between arithmetic and accuracy is encountered by Molecular dynamics (MD). Molecular dynamics is a general theoretical calculation method for studying the mechanical properties of alloy materials. However, molecular dynamics simulations of alloy materials are limited to simple systems because the construction of traditional interatomic potentials is replicative and inefficient. In this study, the Cu-Sn material machine learning interatomic potential was constructed by using a deep neural network model, and the first-principles calculation results were used as the training data set to ensure the accuracy of the quantum mechanical interatomic potential. This process features an “active learning” process, data generation and model training methods with minimal human intervention. Molecular dynamics using machine learning interatomic potentials (MLIP) and first-principles calculations show good consistency. It was concluded that MLIP can accurately predict energy, force and mechanical properties. The root mean square error (RMSEs) of the energy and force per atom is approximately 10 meV/atom and 100 meV/Å. It shows good advantages in energy-volume curve, phase transition temperature and elastic modulus, laying the foundation for the wide application of the MD method in the design and development of Cu-Sn alloy materials.
{"title":"Research on Cu-Sn machine learning interatomic potential with active learning strategy","authors":"Jinyan Liu , Guanghao Zhang , Jianyong Wang , Hong Zhang , Ye Han","doi":"10.1016/j.commatsci.2024.113450","DOIUrl":"10.1016/j.commatsci.2024.113450","url":null,"abstract":"<div><div>Cu-Sn alloy materials are widely used in electronic industry, aerospace and 3D printing. When studying the structure and properties of materials, a contradiction between arithmetic and accuracy is encountered by Molecular dynamics (MD). Molecular dynamics is a general theoretical calculation method for studying the mechanical properties of alloy materials. However, molecular dynamics simulations of alloy materials are limited to simple systems because the construction of traditional interatomic potentials is replicative and inefficient. In this study, the Cu-Sn material machine learning interatomic potential was constructed by using a deep neural network model, and the first-principles calculation results were used as the training data set to ensure the accuracy of the quantum mechanical interatomic potential. This process features an “active learning” process, data generation and model training methods with minimal human intervention. Molecular dynamics using machine learning interatomic potentials (MLIP) and first-principles calculations show good consistency. It was concluded that MLIP can accurately predict energy, force and mechanical properties. The root mean square error (RMSEs) of the energy and force per atom is approximately 10 meV/atom and 100 meV/Å. It shows good advantages in energy-volume curve, phase transition temperature and elastic modulus, laying the foundation for the wide application of the MD method in the design and development of Cu-Sn alloy materials.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"246 ","pages":"Article 113450"},"PeriodicalIF":3.1,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142432966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The importance of advanced materials like zirconium dioxide (ZrO2) in diverse medical, industrial, and technological contexts is underscored by contemporary technology. ZrO2′s unique combination of properties renders it indispensable for a broad spectrum of applications, suggesting its enduring importance. This study presents the very first investigation into the physical properties, structural stability, and ground-state characteristics of sixteen distinct ZrO2 polymorphs through the application of density functional theory (DFT). Motivated by the potential of ZrO2 polymorphs to substitute for SiO2, we conducted calculations to ascertain their dielectric properties. A comprehensive analysis was conducted on all structural features, and their stability was assessed. ZrO2 polymorphs exhibit a wide bandgap with the type of bandgap also examined. Calculated zone-center phonon frequencies demonstrate the dynamical stability of ZrO2, with existing polymorphs showing strong agreement with experimental frequencies, particularly within the monoclinic polymorph. Raman and infrared (IR) spectra of ZrO2 polymorphs were simulated using density functional perturbation theory. ZrO2 demonstrates notable mechanical stability, as evidenced by calculated hardness (moduli), ductility, improved ductility, and higher elasticity. Calculated optical properties, including the dielectric constant and refractive index of ZrO2 polymorphs, play a pivotal role in optimizing their performance in various applications such as optoelectronic devices and antireflective materials.
{"title":"Density functional theory analysis of novel ZrO2 polymorphs: Unveiling structural stability, electronic structure, vibrational and optical properties","authors":"Kanimozhi Balakrishnan , Vasu Veerapandy , Vajeeston Nalini , Ponniah Vajeeston","doi":"10.1016/j.commatsci.2024.113439","DOIUrl":"10.1016/j.commatsci.2024.113439","url":null,"abstract":"<div><div>The importance of advanced materials like zirconium dioxide (ZrO<sub>2</sub>) in diverse medical, industrial, and technological contexts is underscored by contemporary technology. ZrO<sub>2</sub>′s unique combination of properties renders it indispensable for a broad spectrum of applications, suggesting its enduring importance. This study presents the very first investigation into the physical properties, structural stability, and ground-state characteristics of sixteen distinct ZrO<sub>2</sub> polymorphs through the application of density functional theory (DFT). Motivated by the potential of ZrO<sub>2</sub> polymorphs to substitute for SiO<sub>2</sub>, we conducted calculations to ascertain their dielectric properties. A comprehensive analysis was conducted on all structural features, and their stability was assessed. ZrO<sub>2</sub> polymorphs exhibit a wide bandgap with the type of bandgap also examined. Calculated zone-center phonon frequencies demonstrate the dynamical stability of ZrO<sub>2</sub>, with existing polymorphs showing strong agreement with experimental frequencies, particularly within the monoclinic polymorph. Raman and infrared (IR) spectra of ZrO<sub>2</sub> polymorphs were simulated using density functional perturbation theory. ZrO<sub>2</sub> demonstrates notable mechanical stability, as evidenced by calculated hardness (moduli), ductility, improved ductility, and higher elasticity. Calculated optical properties, including the dielectric constant and refractive index of ZrO<sub>2</sub> polymorphs, play a pivotal role in optimizing their performance in various applications such as optoelectronic devices and antireflective materials.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"246 ","pages":"Article 113439"},"PeriodicalIF":3.1,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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