Materials Genome Engineering Advances最新文献_第5页

Systematic assessment of various universal machine-learning interatomic potentials 对各种通用机器学习原子间势能的系统评估

Materials Genome Engineering Advances Pub Date : 2024-07-31 DOI: 10.1002/mgea.58

Haochen Yu, Matteo Giantomassi, Giuliana Materzanini, Junjie Wang, Gian-Marco Rignanese

{"title":"Systematic assessment of various universal machine-learning interatomic potentials","authors":"Haochen Yu, Matteo Giantomassi, Giuliana Materzanini, Junjie Wang, Gian-Marco Rignanese","doi":"10.1002/mgea.58","DOIUrl":"https://doi.org/10.1002/mgea.58","url":null,"abstract":"Machine-learning interatomic potentials have revolutionized materials modeling at the atomic scale. Thanks to these, it is now indeed possible to perform simulations of ab initio quality over very large time and length scales. More recently, various universal machine-learning models have been proposed as an out-of-box approach avoiding the need to train and validate specific potentials for each particular material of interest. In this paper, we review and evaluate four different universal machine-learning interatomic potentials (uMLIPs), all based on graph neural network architectures which have demonstrated transferability from one chemical system to another. The evaluation procedure relies on data both from a recent verification study of density-functional-theory implementations and from the Materials Project. Through this comprehensive evaluation, we aim to provide guidance to materials scientists in selecting suitable models for their specific research problems, offer recommendations for model selection and optimization, and stimulate discussion on potential areas for improvement in current machine-learning methodologies in materials science.","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"2 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.58","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142430404","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

Development of a two-dimensional bipolar electrochemistry technique for high throughput corrosion screening 开发用于高通量腐蚀筛选的二维双极电化学技术

Materials Genome Engineering Advances Pub Date : 2024-07-19 DOI: 10.1002/mgea.57

Yiqi Zhou, Dirk Lars Engelberg

引用次数: 0

Artificial intelligence enabled smart design and manufacturing of advanced materials: The endless Frontier in AI+ era 人工智能支持先进材料的智能设计和制造：人工智能+时代的无尽前沿

Materials Genome Engineering Advances Pub Date : 2024-07-16 DOI: 10.1002/mgea.56

William Yi Wang, Suyang Zhang, Gaonan Li, Jiaqi Lu, Yong Ren, Xinchao Wang, Xingyu Gao, Yanjing Su, Haifeng Song, Jinshan Li

{"title":"Artificial intelligence enabled smart design and manufacturing of advanced materials: The endless Frontier in AI+ era","authors":"William Yi Wang, Suyang Zhang, Gaonan Li, Jiaqi Lu, Yong Ren, Xinchao Wang, Xingyu Gao, Yanjing Su, Haifeng Song, Jinshan Li","doi":"10.1002/mgea.56","DOIUrl":"https://doi.org/10.1002/mgea.56","url":null,"abstract":"Future-oriented Science & Technology (S&T) Strategies trigger the innovative developments of advanced materials, providing an envision to the significant progress of leading-/cutting-edge science, engineering, and technologies for the next few decades. Motivated by Made in China 2025 and New Material Power Strategy by 2035, several key viewpoints about automated research workflows for accelerated discovery and smart manufacturing of advanced materials in terms of AI for Science and main respective of big data, database, standards, and ecosystems are discussed. Referring to classical toolkits at various spatial and temporal scales, AI-based toolkits and AI-enabled computations for material design are compared, highlighting the dominant role of the AI agent paradigm. Our recent developed ProME platform together with its functions is introduced briefly. A case study of AI agent assistant welding is presented, which is consisted of the large language model, auto-coding via AI agent, image processing, image mosaic, and machine learning for welding defect detection. Finally, more duties are called to educate the next generation workforce with creative minds and skills. It is believed that the transformation of knowledge-enabled data-driven integrated computational material engineering era to AI+ era promotes the transformation of smart design and manufacturing paradigm from “designing the materials” to “designing with materials.”","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"2 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.56","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142430056","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

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