Materials Genome Engineering Advances最新文献

{"title":"Prediction of dynamic recrystallization behavior of SAE52100 large section bearing steel based on machine learning","authors":"Peiheng Ding,&nbsp;Changqing Shu,&nbsp;Shasha Zhang,&nbsp;Zhaokuan Zhang,&nbsp;Xingshuai Liu,&nbsp;Jicong Zhang,&nbsp;Qian Chen,&nbsp;Shuaipeng Yu,&nbsp;Xiaolin Zhu,&nbsp;Zhengjun Yao","doi":"10.1002/mgea.75","DOIUrl":"https://doi.org/10.1002/mgea.75","url":null,"abstract":"<p>This paper investigates the dynamic recrystallization characteristics of SAE52100 large section bearing steel under hot compression, focusing on both the center and surface. Using data from thermal simulation experiments the physical models were developed. Four machine learning algorithms including support vector regression, k-nearest neighbors, random forest, and extreme gradient boosting were then employed to develop dynamic recrystallization prediction models based on the experimental data and inferred values from the physical model. The results show that the machine learning methods provide a better numerical description of the model, provided these are fed with extensive data. To enhance the scope of application, we obtained data from the dynamic recrystallization models for both the center and surface of SAE52100 steel in the as-cast state, as well as extrapolated values from the literature regarding the hot-rolled condition. When the SHAP method was introduced to reveal the mechanism of the influence of each input feature on the prediction results of the machine learning model, it was found that the test results of the Cr element did not match the theory, mainly because of the small scale of Cr elemental data and the strong dependence on grain size and secondary dendrite spacing.</p>","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"2 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.75","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143253083","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}

{"title":"Editorial: Shaping the future of materials science through machine learning","authors":"Dezhen Xue,&nbsp;Turab Lookman","doi":"10.1002/mgea.80","DOIUrl":"https://doi.org/10.1002/mgea.80","url":null,"abstract":"<p>This special issue of MGE advances focuses on the revolutionary impact of machine learning (ML) on materials science. As we navigate the threshold of a new era in scientific innovation, this issue collates a series of research articles that epitomize machine learning as a foundational pillar in materials science and engineering. The synergy between ML and conventional materials science methodologies not only accelerates the discovery of novel materials but also refines the prediction of material properties and streamlines manufacturing processes. These advances offer unparalleled opportunities for technological progress and sustainability. We, as the guest editors, are excited to present these contributions that introduce new methodologies and enhance our understanding of material behavior through the prism of advanced analytics and computational power.</p><p>This issue spans a diverse array of studies demonstrating the robust capabilities of ML applications across various scales and complexities within the field. Each article contributes to a broad exploration of how machine learning can be integrated into different facets of materials science. They range from quantum computing to enhancing materials design to predictive models that impact the properties and behavior of complex materials. The contributions showcase effective strategies to predict critical physical properties and illustrate the practical implementations of ML in optimizing the development processes of technological and industrial materials.</p><p>As we confront global challenges that demand more efficient, sustainable, and high performance materials, the research showcased here offers promising new pathways and tools. The integration of ML into materials science not only boosts our analytical capabilities but also accelerates the cycle of discovery and application, effectively bridging the gap between theoretical science and practical implementation.</p><p>The pages that follow represent articles at the forefront of this interdisciplinary nexus, providing insights expected to influence a broad spectrum of sectors, including electronics, aerospace, automotive, and beyond.</p><p><b>Dezhen Xue</b>: Writing—review and editing. <b>Turab Lookman</b>: Writing—review and editing.</p><p>There is no conflict of interest.</p>","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"2 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.80","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143252787","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}

{"title":"Predicting the glass transition temperature of polymer based on generative adversarial networks and automated machine learning","authors":"Zhanjie Liu,&nbsp;Yixuan Huo,&nbsp;Qionghai Chen,&nbsp;Siqi Zhan,&nbsp;Qian Li,&nbsp;Qingsong Zhao,&nbsp;Lihong Cui,&nbsp;Jun Liu","doi":"10.1002/mgea.78","DOIUrl":"https://doi.org/10.1002/mgea.78","url":null,"abstract":"<p>Solution styrene-butadiene rubber (SSBR) finds wide applications in high performance tire design and various other fields. This study aims to create a quantitative structure–property relationship (QSPR) model linking SSBR's glass transition temperature (<i>T</i>_<i>g</i>) to its structural properties. A dataset of 68 sets of data from published literature was compiled to develop a predictive machine learning model for SSBR's structural design and synthesis using small sample sizes. To tackle small sample sizes, a framework combining generative adversarial networks (GAN) and the Tree-based Pipeline Optimization Tool (TPOT) is proposed. GAN is first used to generate additional samples that mirror the original dataset's distribution, expanding the dataset. The TPOT is then applied to automatically find the best model and parameter combinations, creating an optimal predictive model for the mixed dataset. Experimental results show that using GAN to enlarge the dataset and TPOT regression models significantly enhances model performance, increasing the <i>R</i>² value from 0.745 to 0.985 and decreasing the RMSE from 7.676 to 1.569. The proposed GAN–TPOT framework demonstrates the potential of combining generative models with automated machine learning to improve materials science research. This combination accelerates research and development processes, enhances prediction and design accuracy, and introduces new perspectives and possibilities for the field.</p>","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"2 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.78","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143252853","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}

{"title":"PDGPT: A large language model for acquiring phase diagram information in magnesium alloys","authors":"Zini Yan,&nbsp;Hongyu Liang,&nbsp;Jingya Wang,&nbsp;Hongbin Zhang,&nbsp;Alisson Kwiatkowski da Silva,&nbsp;Shiyu Liang,&nbsp;Ziyuan Rao,&nbsp;Xiaoqin Zeng","doi":"10.1002/mgea.77","DOIUrl":"https://doi.org/10.1002/mgea.77","url":null,"abstract":"<p>Magnesium alloys, known for their lightweight advantages, are increasingly in demand across a range of applications, from aerospace to the automotive industry. With rising requirements for strength and corrosion resistance, the development of new magnesium alloy systems has become critical. Phase diagrams play a crucial role in guiding the magnesium alloy design by providing key insights into phase stability, composition, and temperature ranges, enabling the optimization of alloy properties and processing conditions. However, accessing and interpreting phase diagram data with thermodynamic calculation software can be complex and time-consuming, often requiring intricate calculations and iterative refinement based on thermodynamic models. To address this challenge, we introduce PDGPT, a ChatGPT-based large language model designed to streamline the acquisition of magnesium alloys Phase Diagram information with high efficiency and accuracy. Enhanced by prompt-engineering, supervised fine-tuning and retrieval-augmented generation, PDGPT leverages the predictive and reasoning capabilities of large language models along with computational phase diagram data. By combining large language models with traditional phase diagram research tools, PDGPT not only improves the accessibility of critical phase diagram information but also sets the stage for future advancements in applying large language models to materials science.</p>","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"2 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.77","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143252748","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}

{"title":"A knowledge-based materials descriptor for compositional dependence of phase transformation in NiTi shape memory alloys","authors":"Cheng Li,&nbsp;Qingkai Liang,&nbsp;Yumei Zhou,&nbsp;Dezhen Xue","doi":"10.1002/mgea.72","DOIUrl":"https://doi.org/10.1002/mgea.72","url":null,"abstract":"<p>This study presents ∆τ, a novel descriptor that captures the compositional dependence of phase transformation temperature (Ap) in NiTi-based shape memory alloys (SMAs). Designed to address the complexity of multicomponent SMAs, ∆τ was integrated into symbolic regression (SR) and kernel ridge regression (KRR) models, yielding substantial improvements in predicting key functional properties: transformation temperature, enthalpy, and thermal hysteresis. Using the KRR model with ∆τ, we explored the NiTiHfZrCu compositional space, identifying six promising alloys with high Ap (&gt;250°C), large enthalpy (&gt;27 J/g), and low thermal hysteresis. Experimental validation confirmed the model's accuracy with the alloys showing high-temperature transformation behavior and low hysteresis, suitable for high-performance applications in aerospace and nuclear industries. These findings underscore the power of domain-informed descriptors like ∆τ in enhancing machine learning-driven materials design.</p>","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.72","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143717154","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}

{"title":"A review on inverse analysis models in steel material design","authors":"Yoshitaka Adachi,&nbsp;Ta-Te Chen,&nbsp;Fei Sun,&nbsp;Daichi Maruyama,&nbsp;Kengo Sawai,&nbsp;Yoshihito Fukatsu,&nbsp;Zhi-Lei Wang","doi":"10.1002/mgea.71","DOIUrl":"https://doi.org/10.1002/mgea.71","url":null,"abstract":"<p>This paper reviews various inverse analysis models used in steel material design, with a focus on integrating process, microstructure, and properties through advanced machine learning techniques. The study underscores the importance of establishing comprehensive models that effectively link these elements for enhanced materials engineering. Key models discussed include the convolutional neural network–artificial neural network-coupled model, which employs convolutional neural networks for feature extraction; the Bayesian-optimized generative adversarial network–conditional generative adversarial network model, which generates diverse virtual microstructures; the multi-objective optimization model, which concentrates on process–property relationships; and the microstructure–process parallelization model, which correlates microstructural features with process conditions. Each model is assessed for its strengths and limitations, influencing its practical applicability in material design. The paper concludes by advocating for continued improvements in model accuracy and versatility, with the ultimate goal of enhancing steel properties and expanding the scope of data-driven material development.</p>","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"2 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.71","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143248963","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}

{"title":"Machine learning-assisted performance analysis of organic photovoltaics","authors":"Sijing Zhong,&nbsp;Jiayi Huang,&nbsp;Hengyu Meng,&nbsp;Zhuo Feng,&nbsp;Qianyue Wang,&nbsp;Zhenyu Huang,&nbsp;Lijie Zhang,&nbsp;Shiwei Li,&nbsp;Weiyang Gong,&nbsp;Yusen Huang,&nbsp;Lei Ying,&nbsp;Ning Li","doi":"10.1002/mgea.74","DOIUrl":"https://doi.org/10.1002/mgea.74","url":null,"abstract":"<p>Although the power conversion efficiency of organic solar cells (OSCs) has been rapidly improved, there is still a lot of room for designing and developing new materials and their combinations to approach the efficiency limit. In this work, we establish a database of ∼100 bulk heterojunction OSCs composed of representative donors and acceptors reported in the literature, and train machine learning models to identify the efficiency potential of donor-acceptor combinations. We find that the fully connected neural network achieves a Pearson coefficient of up to 0.88 for predicting the efficiency of OSCs with different combinations of donors and acceptors. We use sure independence screening and sparsifying method with feature analysis to analyze and evaluate the performance of OSCs. To prove the reliability and viability of the predictive model, we introduce the theoretical efficiency limits and confidence tests into the process, which provides a simple but reliable solution to quickly analyze and evaluate the potential of OSC materials and material combinations.</p>","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"2 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.74","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143248953","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}

{"title":"Harnessing quantum power: Revolutionizing materials design through advanced quantum computation","authors":"Zikang Guo,&nbsp;Rui Li,&nbsp;Xianfeng He,&nbsp;Jiang Guo,&nbsp;Shenghong Ju","doi":"10.1002/mgea.73","DOIUrl":"https://doi.org/10.1002/mgea.73","url":null,"abstract":"<p>The design of advanced materials for applications in areas of photovoltaics, energy storage, and structural engineering has made significant strides. However, the rapid proliferation of candidate materials—characterized by structural complexity that complicates the relationships between features—presents substantial challenges in manufacturing, fabrication, and characterization. This review introduces a comprehensive methodology for materials design using cutting-edge quantum computing, with a particular focus on quadratic unconstrained binary optimization (QUBO) and quantum machine learning (QML). We introduce the loop framework for QUBO-empowered materials design, including constructing high-quality datasets that capture critical material properties, employing tailored computational methods for precise material modeling, developing advanced figures of merit to evaluate performance metrics, and utilizing quantum optimization algorithms to discover optimal materials. In addition, we delve into the core principles of QML and illustrate its transformative potential in accelerating material discovery through a range of quantum simulations and innovative adaptations. The review also highlights advanced active learning strategies that integrate quantum artificial intelligence, offering a more efficient pathway to explore the vast, complex material design space. Finally, we discuss the key challenges and future opportunities for QML in material design, emphasizing their potential to revolutionize the field and facilitate groundbreaking innovations.</p>","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"2 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.73","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143248401","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}

{"title":"Interpretable machine learning for stability and electronic structure prediction of Janus III–VI van der Waals heterostructures","authors":"Yudong Shi,&nbsp;Yinggan Zhang,&nbsp;Jiansen Wen,&nbsp;Zhou Cui,&nbsp;Jianhui Chen,&nbsp;Xiaochun Huang,&nbsp;Cuilian Wen,&nbsp;Baisheng Sa,&nbsp;Zhimei Sun","doi":"10.1002/mgea.76","DOIUrl":"https://doi.org/10.1002/mgea.76","url":null,"abstract":"<p>Machine learning (ML) techniques have made enormous progress in the field of materials science. However, many conventional ML algorithms operate as “black-boxes”, lacking transparency in revealing explicit relationships between material features and target properties. To address this, the development of interpretable ML models is essential to drive further advancements in AI-driven materials discovery. In this study, we present an interpretable framework that combines traditional machine learning with symbolic regression, using Janus III–VI vdW heterostructures as a case study. This approach enables fast and accurate predictions of stability and electronic structure. Our results demonstrate that the prediction accuracy using the classification model for stability, based on formation energy, reaches 0.960. On the other hand, the <i>R</i>², MAE, and RMSE value using the regression model for electronic structure prediction, based on band gap, achieves 0.927, 0.113, and 0.141 on the testing set, respectively. Additionally, we identify a universal interpretable descriptor comprising five simple parameters that reveals the underlying physical relationships between the candidate heterostructures and their band gaps. This descriptor not only delivers high accuracy in band gap prediction but also provides explicit physical insight into the material properties.</p>","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"2 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.76","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143248402","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}

{"title":"The role of physical metallurgical relationships in enhancing alloy properties prediction and design: A case study on Q&P steel","authors":"Yong Li,&nbsp;Hua Li,&nbsp;Chenchong Wang,&nbsp;Pedro Eduardo Jose Rivera-Diaz-del-Castillo","doi":"10.1002/mgea.70","DOIUrl":"https://doi.org/10.1002/mgea.70","url":null,"abstract":"<p>Traditional alloy design typically relies on a trial-and-error approach, which is both time-consuming and expensive. Whilst physical metallurgical (PM) models offer some predictive capabilities, their reliability is limited by errors accumulating across space scales. To address this, this study proposes a novel framework that combines PM knowledge graphs (PMKGs) with graph neural networks (GNNs) to predict the tensile properties of quenching and partitioning steels, using genetic algorithms for dual-objective optimization. Compared to traditional artificial intelligence (AI) models, this framework shows significant advantages in predicting ultimate tensile strength (UTS) and total elongation (TEL) with higher accuracy and stability. Notably, the <i>R</i>² for TEL prediction improved by approximately 15%. Furthermore, this framework successfully balances UTS and TEL, resulting in the design of alloys with superior overall properties. The designed alloys, with a composition of approximately 0.3 wt.% C, 3 wt.% Mn, 1.2 wt.% Si, and minor amounts of Cr and Al, achieve a UTS exceeding 1500 MPa and TEL near 20%, aligning with PM principles and validating the rationality and feasibility of this method. This study offers new insights into applying AI in complex multi-objective alloy design, highlighting the potential of integrating expert knowledge with GNNs.</p>","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.70","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143717298","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}