Materials Genome Engineering Advances最新文献

{"title":"Enhancing named entity recognition with a novel BERT-BiLSTM-CRF-RC joint training model for biomedical materials database","authors":"Mufei Li,&nbsp;Yan Zhuang,&nbsp;Ke Chen,&nbsp;Lin Han,&nbsp;Xiangfeng Li,&nbsp;Yongtao wei,&nbsp;Xiangdong Zhu,&nbsp;Mingli Yang,&nbsp;Guangfu Yin,&nbsp;Jiangli Lin,&nbsp;Xingdong Zhang","doi":"10.1002/mgea.70001","DOIUrl":"https://doi.org/10.1002/mgea.70001","url":null,"abstract":"<p>In this study, we propose a novel joint training model for named entity recognition (NER) that combines BERT, BiLSTM, CRF, and a reading comprehension (RC) mechanism. Traditional BERT-BiLSTM-CRF models often struggle with inaccurate boundary detection and excessive fragmentation of named entities due to their lack of specialized vocabulary. Our model addresses these issues by integrating an RC mechanism, which helps refine fragmented results by enabling the model to more precisely identify entity boundaries without relying on an expert-annotated dictionary. Additionally, segmentation issues are further mitigated through a segmented combined voting- and positive-sample-coverage technique. We applied this model to develop a database for mesoporous bioactive glass (MBG). Furthermore, a classifier was developed to automatically detect the presence of pertinent information within paragraphs. For this study, 200 articles were searched using MBG-related keywords, and the data were split into a training set and a test set in a 9:1 ratio. A total of 492 paragraphs were automatically extracted for training, and 50 paragraphs were extracted for testing the model. The results demonstrate that our joint training model achieves an accuracy of 92.8% in named entity recognition, which is 4.3% higher than the 88.5% accuracy of the traditional BERT-BiLSTM-CRF model.</p>","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.70001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143717162","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":"Advances in high-throughput experiments of polymer crystallization for developing polymer processing","authors":"Bao Deng,&nbsp;Jinyong Wu,&nbsp;Hao Lin,&nbsp;Ling Xu,&nbsp;Ganji Zhong,&nbsp;Jun Lei,&nbsp;Ludwig Cardon,&nbsp;Jiazhuang Xu,&nbsp;Zhongming Li","doi":"10.1002/mgea.70003","DOIUrl":"https://doi.org/10.1002/mgea.70003","url":null,"abstract":"<p>Polymer crystallization, an everlasting subject in polymeric materials, holds great significance not only as a fundamental theoretical issue but also as a pivotal basis for directing polymer processing. Given its multistep, rapid, and thermodynamic nature, tracing and comprehending polymer crystallization pose a formidable challenge, particularly when it encounters practical processing scenarios that involve complex coupled fields (such as temperature, flow, and pressure). The advent of high-time and spatially resolved experiments paves the way for <i>in situ</i> investigations of polymer crystallization. In this review, we delve into the strides in studying polymer crystallization under the effects of coupled external fields via state-of-the-art high-throughput experiments. We highlight the intricate setup of these high-throughput experimental devices, spanning from the laboratory and pilot levels to the industrial level. The individual and combined effects of external fields on polymer crystallization are discussed. By breaking away from the conventional “black box” research approach, special interest is paid to the <i>in situ</i> crystalline behavior of polymers during realistic processing. Finally, we underscore the advancements in polymer crystallization via high-throughput experiments and outline its promising development.</p>","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.70003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143717363","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":"Local large language model-assisted literature mining for on-surface reactions","authors":"Juan Xiang,&nbsp;Yizhang Li,&nbsp;Xinyi Zhang,&nbsp;Yu He,&nbsp;Qiang Sun","doi":"10.1002/mgea.88","DOIUrl":"https://doi.org/10.1002/mgea.88","url":null,"abstract":"<p>Large language models (LLMs) excel at extracting information from literatures. However, deploying LLMs necessitates substantial computational resources, and security concerns with online LLMs pose a challenge to their wider applications. Herein, we introduce a method for extracting scientific data from unstructured texts using a local LLM, exemplifying its applications to scientific literatures on the topic of on-surface reactions. By combining prompt engineering and multi-step text preprocessing, we show that the local LLM can effectively extract scientific information, achieving a recall rate of 91% and a precision rate of 70%. Moreover, despite significant differences in model parameter size, the performance of the local LLM is comparable to that of GPT-3.5 turbo (81% recall, 84% precision) and GPT-4o (85% recall, 87% precision). The simplicity, versatility, reduced computational requirements, and enhanced privacy of the local LLM makes it highly promising for data mining, with the potential to accelerate the application and development of LLMs across various fields.</p>","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.88","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143717403","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 multi-objective feature optimization strategy for developing high-entropy alloys with optimal strength and ductility","authors":"Yan Zhang,&nbsp;Shewei Xin,&nbsp;Wei Zhou,&nbsp;Xiao Wang,&nbsp;Yangyang Xu,&nbsp;Yanjing Su","doi":"10.1002/mgea.70000","DOIUrl":"https://doi.org/10.1002/mgea.70000","url":null,"abstract":"<p>Selecting appropriate material features is essential for effective data-driven materials design. Here, we propose a multi-objective feature optimization strategy that identifies feature subsets to improve both prediction accuracy and active learning efficiency for iterative experimentation. Our approach integrates an evolutionary genetic algorithm to explore an expanded feature space, encompassing both traditional feature pools and a continuous numerical representation of elements rather than relying solely on discrete values. We demonstrate this strategy by identifying high-entropy alloys (HEAs) with optimal strength and ductility. Results show that the optimized feature subsets reduce prediction errors by 20% for strength and 11% for ductility. Additionally, within fewer than three feedback iterations, HEAs with outstanding combinations of yield strength and ductility are identified, highlighting the high efficiency of this approach. This multi-objective feature optimization strategy is adaptable to other material systems, offering a pathway to improve machine learning performance and accelerate materials discovery.</p>","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.70000","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143717088","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":"An overview of high-throughput synthesis for advanced high-entropy alloys","authors":"Tong Xie,&nbsp;Weidong Li,&nbsp;Gihan Velisa,&nbsp;Shuying Chen,&nbsp;Fanchao Meng,&nbsp;Peter K. Liaw,&nbsp;Yang Tong","doi":"10.1002/mgea.87","DOIUrl":"https://doi.org/10.1002/mgea.87","url":null,"abstract":"<p>High-entropy alloys (HEAs) have revolutionized alloy design by integrating multiple principal elements in equimolar or near-equimolar ratios to form solid solutions, vastly expanding the compositional space beyond traditional alloys based on a primary element. However, the immense compositional complexity presents significant challenges in designing alloys with targeted properties, as billions of new alloy systems emerge. High-throughput approaches, which allow the parallel execution of numerous experiments, are essential for accelerated HEA design to navigate this extensive compositional space and fully exploit their potential. Here, we reviewed how advancements in high-throughput synthesis tools have accelerated HEA database development. We also discussed the advantages and limitations of each high-throughput fabrication methodology, as understanding these is vital for achieving precise HEA design.</p>","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.87","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143717061","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":"Thermodynamics and kinetics of isothermal precipitation in magnesium alloys","authors":"Hongcan Chen,&nbsp;Jingli Sun,&nbsp;Shenglan Yang,&nbsp;Yu Zhang,&nbsp;Kai Tang,&nbsp;Chuan Zhang,&nbsp;Yangfan Lu,&nbsp;Qun Luo,&nbsp;Qian Li","doi":"10.1002/mgea.86","DOIUrl":"https://doi.org/10.1002/mgea.86","url":null,"abstract":"<p>As the lightest structural metal materials, Mg alloys are promising for wider applications but are limited by low strength and poor corrosion resistance. Precipitation is an effective way to improve the strength and other performance of Mg alloys. Facing the extremely complex precipitation process, the crystal structures of precipitates, precipitation sequence, and precipitation thermodynamic and kinetics behaviors have stimulated extensive research interests. Precipitation kinetics, which connects composition, aging processes, and precipitate microstructure, is pivotal in determining the performance of age-hardening Mg alloys. Despite numerous studies on this topic, a comprehensive review remains absent. This work aims to bridge that gap by analyzing precipitation from thermodynamic and kinetic perspectives. Thermodynamically, the stability of precipitates, nucleation driving forces, and resistances of precipitation are discussed. Kinetically, the various kinetic theories including semi-empirical models, mean-field models, phase-field model, and atomistic approaches and their applications in Mg alloys are systematically summarized. Among these, mean-field models emerge as particularly promising for accurately predicting precipitation processes. Finally, the framework for property prediction based on precipitation kinetics is introduced to illustrating the role of integrated computational materials engineering (ICME) in designing advanced Mg alloys.</p>","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.86","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143717361","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":"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}