Materials Genome Engineering Advances最新文献

{"title":"Applications of density functional theory to corrosion and corrosion prevention of metals: A review","authors":"Dihao Chen,&nbsp;Wenjie Zhou,&nbsp;Yucheng Ji,&nbsp;Chaofang Dong","doi":"10.1002/mgea.83","DOIUrl":"https://doi.org/10.1002/mgea.83","url":null,"abstract":"<p>Recently, density functional theory (DFT) has been a powerful tool to model the corrosion behaviors of materials, provide insights into the corrosion mechanisms, predict the corrosion performance of materials, and design the corrosion-resistant alloys and organic inhibitors. DFT enables corrosion scientist to fundamentally understand the corrosion behaviors and corrosion mechanisms of materials from the perspective of atomic and electronic structures, combining with the traditional and advanced experimental tests. This review briefly summarizes the main features of DFT calculations and present a comprehensive overview of their typical applications to corrosion and corrosion prevention of metals, involving potential-pH diagrams, hydrogen evolution reaction, anodic dissolution, passivity and passivity breakdown, and organic inhibitor for metals. The paper also reviews the correlations between DFT-computed <i>descriptors</i> and the micro/macro physiochemical parameters of corrosion. Despite the great progress achieved by DFT, there are still some challenges in addressing corrosion issues due to the lack of bridges between the DFT-calculated electronic parameters and the macro corrosion performance of materials. The DFT modeling-experiment-engineering-theory model will be a potential method to clarify and build the links.</p>","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.83","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143717304","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":"Accelerated predictions of the sublimation enthalpy of organic materials with machine learning","authors":"Yifan Liu,&nbsp;Huan Tran,&nbsp;Chaofan Huang,&nbsp;Beatriz G. del Rio,&nbsp;V. Roshan Joseph,&nbsp;Mark Losego,&nbsp;Rampi Ramprasad","doi":"10.1002/mgea.84","DOIUrl":"https://doi.org/10.1002/mgea.84","url":null,"abstract":"<p>The sublimation enthalpy, <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>Δ</mi>\u0000 <msub>\u0000 <mi>H</mi>\u0000 <mtext>sub</mtext>\u0000 </msub>\u0000 </mrow>\u0000 <annotation> ${Delta }{H}_{text{sub}}$</annotation>\u0000 </semantics></math>, is a key thermodynamic parameter governing the phase transformation of a substance between its solid and gas phases. This transformation is at the core of many important materials' purification, deposition, and etching processes. While <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>Δ</mi>\u0000 <msub>\u0000 <mi>H</mi>\u0000 <mtext>sub</mtext>\u0000 </msub>\u0000 </mrow>\u0000 <annotation> ${Delta }{H}_{text{sub}}$</annotation>\u0000 </semantics></math> can be measured experimentally and estimated computationally, these approaches have their own different challenges. Here, we develop a machine learning (ML) approach to rapidly predict <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>Δ</mi>\u0000 <msub>\u0000 <mi>H</mi>\u0000 <mtext>sub</mtext>\u0000 </msub>\u0000 </mrow>\u0000 <annotation> ${Delta }{H}_{text{sub}}$</annotation>\u0000 </semantics></math> from data generated using density functional theory (DFT). We further demonstrate how combining ML and DFT methods with active learning can be efficient in exploring the materials space, expanding the coverage of the computed dataset, and systematically improving the ML predictive model of <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>Δ</mi>\u0000 <msub>\u0000 <mi>H</mi>\u0000 <mtext>sub</mtext>\u0000 </msub>\u0000 </mrow>\u0000 <annotation> ${Delta }{H}_{text{sub}}$</annotation>\u0000 </semantics></math>. With an error of <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>∼</mo>\u0000 <mn>15</mn>\u0000 </mrow>\u0000 <annotation> ${sim} 15$</annotation>\u0000 </semantics></math> kJ/mol in instantaneous predictions of <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>Δ</mi>\u0000 <msub>\u0000 <mi>H</mi>\u0000 <mtext>sub</mtext>\u0000 </msub>\u0000 </mrow>\u0000 <annotation> ${Delta }{H}_{text{sub}}$</annotation>\u0000 </semantics></math>, the ML model developed in this work will be useful for the community.</p>","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.84","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143717365","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":"Data-driven prediction of phase formation in graphene–metal systems based on phase diagram insights","authors":"Leilei Chen,&nbsp;Changheng Li,&nbsp;Kai Xu,&nbsp;Ruonan Zhou,&nbsp;Ming Lou,&nbsp;Yujie Du,&nbsp;Denis Music,&nbsp;Keke Chang","doi":"10.1002/mgea.81","DOIUrl":"https://doi.org/10.1002/mgea.81","url":null,"abstract":"<p>Graphene–metal (G-M) composites have attracted tremendous interests due to their promising applications in electronics, optics, energy-storage devices and nano-electromechanical systems. Especially, phase formations of graphene combined with different metals are considered valuable for discovering and designing advanced G-M composites. However, the phase formations in G-M systems have rarely been systematically described since graphene was first extracted from graphite in 2004. Here, we propose a data-driven approach to predict the phase formations in G-M systems leveraging G-M binary phase diagrams, which were established using the calculation of phase diagrams method. Phase relationships obtained from G-M phase diagrams of 34 systems and formation enthalpies of corresponding carbides were employed as the training dataset in a machine learning model to further predict the phase formations in additional 13 G-M systems. Phase formation predictions achieved an accuracy of 87.5% in the test dataset. Three distinct phase formations were characterised in G-M systems. Finally, we propose a general phase formation rule in the G-M systems: metals with smaller atomic numbers in the same period are more likely to form secondary solutions with graphene.</p>","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.81","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143717230","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":"High-dimensional Bayesian optimization for metamaterial design","authors":"Zhichao Tian,&nbsp;Yang Yang,&nbsp;Sui Zhou,&nbsp;Tian Zhou,&nbsp;Ke Deng,&nbsp;Chunlin Ji,&nbsp;Yejun He,&nbsp;Jun S. Liu","doi":"10.1002/mgea.79","DOIUrl":"https://doi.org/10.1002/mgea.79","url":null,"abstract":"<p>Metamaterial design, encompassing both microstructure topology selection and geometric parameter optimization, constitutes a high-dimensional optimization problem, with computationally expensive and time-consuming design evaluations. Bayesian optimization (BO) offers a promising approach for black-box optimization involved in various material designs, and this work presents several advanced techniques to adapt BO to address the challenges associated with metamaterial design. First, variational autoencoders (VAEs) are employed for efficient dimensionality reduction, mapping complex, high-dimensional metamaterial microstructures into a compact latent space. Second, mutual information maximization is incorporated into the VAE to enhance the quality of the learned latent space, ensuring that the most relevant features for optimization are retained. Third, trust region-based Bayesian optimization (TuRBO) dynamically adjusts local search regions, ensuring stability and convergence in high-dimensional spaces. The proposed techniques are well incorporated with conventional Gaussian processes (GP)-based BO framework. We applied the proposed method for the design of electromagnetic metamaterial microstructures. Experimental results show that we achieve a significantly high probability of finding the ground-truth topology types and their geometric parameters, leading to high accuracy in matching the design target. Moreover, our approach demonstrates significant time efficiency compared with traditional design methods.</p>","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"2 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.79","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143253227","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":"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}