Materials Genome Engineering Advances最新文献_第4页

High-throughput preparation for alloy composition design in additive manufacturing: A comprehensive review 增材制造中合金成分设计的高通量制备：综合评述

Materials Genome Engineering Advances Pub Date : 2024-07-03 DOI: 10.1002/mgea.55

Min Liu, Chenxu Lei, Yongxiang Wang, Baicheng Zhang, Xuanhui Qu

引用次数: 0

Multi-objective optimization of three mechanical properties of Mg alloys through machine learning 通过机器学习对镁合金的三种机械性能进行多目标优化

Materials Genome Engineering Advances Pub Date : 2024-06-27 DOI: 10.1002/mgea.54

Wei Gou, Zhang-Zhi Shi, Yuman Zhu, Xin-Fu Gu, Fu-Zhi Dai, Xing-Yu Gao, Lu-Ning Wang

引用次数: 0

Bond sensitive graph neural networks for predicting high temperature superconductors 用于预测高温超导体的键敏感图神经网络

Materials Genome Engineering Advances Pub Date : 2024-06-16 DOI: 10.1002/mgea.48

Liang Gu, Yang Liu, Pin Chen, Haiyou Huang, Ning Chen, Yang Li, Turab Lookman, Yutong Lu, Yanjing Su

{"title":"Bond sensitive graph neural networks for predicting high temperature superconductors","authors":"Liang Gu, Yang Liu, Pin Chen, Haiyou Huang, Ning Chen, Yang Li, Turab Lookman, Yutong Lu, Yanjing Su","doi":"10.1002/mgea.48","DOIUrl":"https://doi.org/10.1002/mgea.48","url":null,"abstract":"Finding high temperature superconductors (HTS) has been a continuing challenge due to the difficulty in predicting the transition temperature (Tc) of superconductors. Recently, the efficiency of predicting Tc has been greatly improved via machine learning (ML). Unfortunately, prevailing ML models have not shown adequate generalization ability to find new HTS, yet. In this work, a graph neural network model is trained to predict the maximal Tc (Tcmax) of various materials. Our model reveals a close connection between Tcmax and chemical bonds. It suggests that shorter bond lengths are favored by high Tc, which is in coherence with previous domain knowledge. More importantly, it also indicates that chemical bonds consisting of some specific chemical elements are responsible for high Tc, which is new even to the human experts. It can provide a convenient guidance to the materials scientists in search of HTS.","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"2 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.48","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141488548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Thermodynamic variational principle, its connections to the phenomenological laws and its applications to the derivation of microstructural models 热力学变分原理、其与现象学定律的联系及其在微结构模型推导中的应用

Materials Genome Engineering Advances Pub Date : 2024-06-12 DOI: 10.1002/mgea.51

Qiang Du

{"title":"Thermodynamic variational principle, its connections to the phenomenological laws and its applications to the derivation of microstructural models","authors":"Qiang Du","doi":"10.1002/mgea.51","DOIUrl":"10.1002/mgea.51","url":null,"abstract":"Understanding microstructural evolution occupies a central position in the discipline of materials science and engineering. As stated by Carter et al., microstructural evolution involves complex, coupled, and often nonlinear processes even the description of the dynamics for isolated microstructural evolution processes can be quite complicated. It would be desirable to enrich the microstructural evolution theory by introducing a powerful mathematical tool, which could enable describing and predicting the rich intertwining phenomena such as diffusive or displacive phase transformation, grain growth, generation, or annihilation of defects (vacancy, dislocations, etc.) in a straightforward manner. There have been continuing efforts along this front, and I will restrict myself to the issues in the development and application of the thermodynamic variational principle. Although being reviewed by various authors recently, we hope to redraw attentions to some valuable papers and provide our understanding and viewpoints. It is our opinion that the most appealing feature about the principle is the nature that it could give approximate solutions with tunable accuracy. The other feature is its role as a basic principle in deriving the new models. It is hoped that this paper could promote the development and application of the variational principle even further in materials science.","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"2 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.51","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141351500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A review on the applications of graph neural networks in materials science at the atomic scale 图神经网络在原子尺度材料科学中的应用综述

Materials Genome Engineering Advances Pub Date : 2024-06-10 DOI: 10.1002/mgea.50

Xingyue Shi, Linming Zhou, Yuhui Huang, Yongjun Wu, Zijian Hong

{"title":"A review on the applications of graph neural networks in materials science at the atomic scale","authors":"Xingyue Shi, Linming Zhou, Yuhui Huang, Yongjun Wu, Zijian Hong","doi":"10.1002/mgea.50","DOIUrl":"10.1002/mgea.50","url":null,"abstract":"In recent years, interdisciplinary research has become increasingly popular within the scientific community. The fields of materials science and chemistry have also gradually begun to apply the machine learning technology developed by scientists from computer science. Graph neural networks (GNNs) are new machine learning models with powerful feature extraction, relationship inference, and compositional generalization capabilities. These advantages drive researchers to design computational models to accelerate material property prediction and new materials design, dramatically reducing the cost of traditional experimental methods. This review focuses on the principles and applications of the GNNs. The basic concepts and advantages of the GNNs are first introduced and compared to the traditional machine learning and neural networks. Then, the principles and highlights of seven classic GNN models, namely crystal graph convolutional neural networks, iCGCNN, Orbital Graph Convolutional Neural Network, MatErials Graph Network, Global Attention mechanism with Graph Neural Network, Atomistic Line Graph Neural Network, and BonDNet are discussed. Their connections and differences are also summarized. Finally, insights and prospects are provided for the rapid development of GNNs in materials science at the atomic scale.","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"2 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.50","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141363499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On the potential of using ensemble learning algorithm to approach the partitioning coefficient (k) value in Scheil–Gulliver equation 论使用集合学习算法接近 Scheil-Gulliver 公式中的分区系数 (k) 值的潜力

Materials Genome Engineering Advances Pub Date : 2024-06-04 DOI: 10.1002/mgea.46

Ziyu Li, He Tan, Anders E. W. Jarfors, Jacob Steggo, Lucia Lattanzi, Per Jansson

{"title":"On the potential of using ensemble learning algorithm to approach the partitioning coefficient (k) value in Scheil–Gulliver equation","authors":"Ziyu Li, He Tan, Anders E. W. Jarfors, Jacob Steggo, Lucia Lattanzi, Per Jansson","doi":"10.1002/mgea.46","DOIUrl":"10.1002/mgea.46","url":null,"abstract":"The Scheil–Gulliver equation is essential for assessing solid fractions during alloy solidification in materials science. Despite the prevalent use of the Calculation of Phase Diagrams (CALPHAD) method, its computational intensity and time are limiting the simulation efficiency. Recently, Artificial Intelligence has emerged as a potent tool in materials science, offering robust and reliable predictive modeling capabilities. This study introduces an ensemble-based method that has the potential to enhance the prediction of the partitioning coefficient (k) in the Scheil equation by inputting various alloy compositions. The findings demonstrate that this approach can predict the temperature and solid fraction at the eutectic temperature with an accuracy exceeding 90%, while the accuracy for k prediction surpasses 70%. Additionally, a case study on a commercial alloy revealed that the model's predictions are within a 5°C deviation from experimental results, and the predicted solid fraction at the eutectic temperature is within a 15% difference of the values obtained from the CALPHAD model.","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"2 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.46","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141387475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data mining accelerated the design strategy of high-entropy alloys with the largest hardness based on genetic algorithm optimization 数据挖掘加速了基于遗传算法优化的最大硬度高熵合金的设计策略

Materials Genome Engineering Advances Pub Date : 2024-06-04 DOI: 10.1002/mgea.49

Xianzhe Jin, Hong Luo, Xuefei Wang, Hongxu Cheng, Chunhui Fan, Xiaogang Li, Xiongbo Yan

{"title":"Data mining accelerated the design strategy of high-entropy alloys with the largest hardness based on genetic algorithm optimization","authors":"Xianzhe Jin, Hong Luo, Xuefei Wang, Hongxu Cheng, Chunhui Fan, Xiaogang Li, Xiongbo Yan","doi":"10.1002/mgea.49","DOIUrl":"10.1002/mgea.49","url":null,"abstract":"This article proposed a design strategy that integrated machine learning models based on random forest and genetic algorithm (GA) for the rapid screening of hardness in the AlCoCrCuFeMoNiTi high-entropy alloys system. Through feature engineering and modeling, valence electron concentration, atomic size difference (δr), Pauling electronegativity difference (Δχ), geometric parameters (Λ), and the Cr content were identified as the five key features in the database. The GA was employed to search for alloys with superior hardness and guided synthesis. After three iterations, the HEA Al18Co21Cr23Fe23Mo15 exhibiting the highest predicted hardness (868.8 HV) was identified. The alloy was predominantly composed of BCC, ordered B2, and σ phases, with an experimental hardness of 899.8 ± 9.9 HV, which as approximately 5.38% greater than the maximum hardness observed in the original dataset. The design strategy can also solve other regression problems and pave the way for optimizing material performance in various engineering applications.","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"2 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.49","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141387519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The anodic dissolution kinetics of Mg alloys in water based on ab initio molecular dynamics simulations 基于ab initio分子动力学模拟的镁合金在水中的阳极溶解动力学

Materials Genome Engineering Advances Pub Date : 2024-06-04 DOI: 10.1002/mgea.47

Jieqiong Yan, Xinchen Xu, Gaoning Shi, Yaowei Wang, Chaohong Guan, Yuyang Chen, Yao Yang, Tao Ying, Hong Zhu, Qingli Tang, Xiaoqin Zeng

{"title":"The anodic dissolution kinetics of Mg alloys in water based on ab initio molecular dynamics simulations","authors":"Jieqiong Yan, Xinchen Xu, Gaoning Shi, Yaowei Wang, Chaohong Guan, Yuyang Chen, Yao Yang, Tao Ying, Hong Zhu, Qingli Tang, Xiaoqin Zeng","doi":"10.1002/mgea.47","DOIUrl":"10.1002/mgea.47","url":null,"abstract":"The corrosion susceptibility of magnesium (Mg) alloys presents a significant challenge for their broad application. Although there have been extensive experimental and theoretical investigations, the corrosion mechanisms of Mg alloys are still unclear, especially the anodic dissolution process. Here, a thorough theoretical investigation based on ab initio molecular dynamics and metadynamics simulations has been conducted to clarify the underlying corrosion mechanism of Mg anode and propose effective strategies for enhancing corrosion resistance. Through comprehensive analyses of interfacial structures and equilibrium potentials for Mg(0001)/H2O interface models with different water thicknesses, the Mg(0001)/72 H2O model is identified to be reasonable with −2.17 V vs. standard hydrogen electrode equilibrium potential. In addition, utilizing metadynamics, the free energy barrier for Mg dissolution is calculated to be 0.835 eV, enabling the theoretical determination of anodic polarization curves for pure Mg that aligns well with experimental data. Based on the Mg(0001)/72 H2O model, we further explore the effects of various alloying elements on anodic corrosion resistance, among which Al and Mn alloying elements are found to enhance corrosion resistance of Mg. This study provides valuable atomic-scale insights into the corrosion mechanism of magnesium alloys, offering theoretical guidance for developing novel corrosion-resistant Mg alloys.","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"2 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.47","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141388167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A machine learning-based crystal graph network and its application in development of functional materials 基于机器学习的晶体图网络及其在功能材料开发中的应用

Materials Genome Engineering Advances Pub Date : 2024-06-04 DOI: 10.1002/mgea.38

Gang Xu, You Xue, Xiaoxiao Geng, Xinmei Hou, Jinwu Xu

{"title":"A machine learning-based crystal graph network and its application in development of functional materials","authors":"Gang Xu, You Xue, Xiaoxiao Geng, Xinmei Hou, Jinwu Xu","doi":"10.1002/mgea.38","DOIUrl":"10.1002/mgea.38","url":null,"abstract":"An active area of MGI (Materials Genome Initiative)/MGE (Materials Genome Engineering) is to accelerate the development of new materials by means of active learning and “digital trial-error” using a prediction model of material property. Machine learning methods have widely been employed for predicting crystalline materials properties with crystal graph neural networks (CGNN). The prediction accuracy of the state-of-the-art (SOTA) CGNN models based on big models and big data is generally higher. However, for the development of some classes of materials, the datasets obtained by experiments are usually lacking due to costly experiments and measurement costs. The lack of datasets will impact the accuracy of CGNN models and may result in overfitting during training models. This paper proposes a simplified crystal graph convolutional neural network (S-CGCNN) which possesses higher prediction accuracy while reducing the vast amount of train datasets and computation costs. The S-CGCNN model has successfully predicted properties of crystalline materials, such as piezoelectric materials and dielectric materials, and increased the prediction accuracy up to 12%–20% than existing SOTA CGNN models. Furthermore, the distribution map between properties and compositions of materials has been built to screen the latent space of candidate materials efficiently by principal component analysis.","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"2 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.38","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141387041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrated unified phase-field modeling (UPFM) 综合统一相场建模（UPFM）

Materials Genome Engineering Advances Pub Date : 2024-06-02 DOI: 10.1002/mgea.44

Yuhong Zhao

引用次数: 0