Materials Genome Engineering Advances最新文献

{"title":"ProME: An integrated computational platform for material properties at extremes and its application in multicomponent alloy design","authors":"Xingyu Gao,&nbsp;William Yi Wang,&nbsp;Xin Chen,&nbsp;Xiaoyu Chong,&nbsp;Jiawei Xian,&nbsp;Fuyang Tian,&nbsp;Lifang Wang,&nbsp;Huajie Chen,&nbsp;Yu Liu,&nbsp;Houbing Huang,&nbsp;Haifeng Song","doi":"10.1002/mgea.70029","DOIUrl":"https://doi.org/10.1002/mgea.70029","url":null,"abstract":"<p>We have built an integrated computational platform for material properties at extreme conditions, Professional Materials at Extremes (ProME) v1.0, which enables integrated calculations for multicomponent alloys, covering high temperatures up to tens of thousands of Kelvin, high pressures up to millions of atmospheres, and high strain rates up to millions per second. A series of software packages have been developed and integrated into ProME v1.0, including AI-based crystal search for crystal structure search under pressure, similar atomic environment for disordered configuration modeling, Multiphase Fast Previewer by Mean-Field Potential for multiphase thermodynamic properties, High-throughput Toolkit for Elasticity Modeling for thermo-elastic properties, TRansport at Extremes for electrical and thermal conductivity, High plastic phase model software for phase-field simulation of microstructure evolution under high strain rates, and AutoCalphad for modeling and optimization of phase diagrams with variable compositions. ProME v1.0 has been applied to design the composition of the quaternary alloys Platinum-Iridium-Aluminum-Chromium (Pt-Ir-Al-Cr) for engine nozzles of aerospace attitude-orbit control, achieving high-temperature strength comparable to the currently used Pt-Ir alloys but with significantly reduced costs for raw materials. ProME offers crucial support for advancing both fundamental scientific understanding and industrial innovation in materials research and development.</p>","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"3 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.70029","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145146257","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":"Toward accurate machine learning-driven prediction of polymeric composites properties based on experimental data","authors":"Joseph Han,&nbsp;In Kim,&nbsp;Namjung Cho,&nbsp;Kwan Soo Yang,&nbsp;Jin Suk Myung,&nbsp;Jaeseong Park,&nbsp;Seong Hun Kim,&nbsp;Woo Jin Choi","doi":"10.1002/mgea.70027","DOIUrl":"https://doi.org/10.1002/mgea.70027","url":null,"abstract":"<p>In response to climate change, there has been a focus on developing lightweight and environmentally friendly materials, with active research aimed at enhancing the energy efficiency of electric and hybrid vehicles. In this context, the development of polymer composites with superior thermal conductivity (TC) has been recognized as critical to meeting mechanical property requirements. This paper presents a machine learning model that utilized 1774 experimental data points to predict various properties of polymer composites, such as density, heat deflection temperature, flexural modulus, flexural strength, tensile yield strength, impact strength, and TC. Various data representation methods for composition data are employed, and the XGBoost model is trained, achieving high accuracy with an average <i>R</i>² score of 0.95. This machine learning model, informed by experimental data, is a useful tool for predicting and optimizing the properties of polymer composites.</p>","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"3 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.70027","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145146210","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":"Optimize the quantum yield of G-quartet-based circularly polarized luminescence materials via active learning strategy-BgoFace","authors":"Tianliang Li,&nbsp;Lifei Chen,&nbsp;Bin Cao,&nbsp;Siyuan Liu,&nbsp;Lixing Lin,&nbsp;Zeyu Li,&nbsp;Yingying Chen,&nbsp;Zhenzhen Li,&nbsp;Tong-yi Zhang,&nbsp;Lingyan Feng","doi":"10.1002/mgea.70031","DOIUrl":"https://doi.org/10.1002/mgea.70031","url":null,"abstract":"<p>G-quartet (G4)-based circularly polarized luminescence (CPL) materials within CPL engineering have attracted substantial attention in optoelectronics and photonics owing to their excellent chiral properties and promising applications in advanced optical devices. However, their practical use is limited by relatively low quantum yield (QY), which reduces emission efficiency. Addressing this challenge, we present BgoFace, an integrated active learning (AL)-based software, to optimize G4-based CPL materials with high QY. Starting with an initial dataset of 54 experimentally validated samples, the system executed six AL cycles encompassing 24 targeted experimental groups. Through this closed-loop workflow, BgoFace successfully identified G4 complexes exhibiting a near doubling of QY (37.25%). This achievement significantly advances the previously low QY values typically reported for G4-based CPL materials. The optimized materials demonstrate enhanced stability and processability, attributable to the AL algorithm's simultaneous consideration of multiple physicochemical parameters. This study not only advances the field of G4-based CPL materials for optical and photonic applications, but also establishes a generalizable AL framework suitable for optimizing functional nanomaterials in optoelectronic device design. By bridging data-driven design and experimental validation, BgoFace offers a transformative strategy for accelerating the development of functional nanomaterial engineering.</p>","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"3 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.70031","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145146207","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":"GPUMD 4.0: A high-performance molecular dynamics package for versatile materials simulations with machine-learned potentials","authors":"Ke Xu,&nbsp;Hekai Bu,&nbsp;Shuning Pan,&nbsp;Eric Lindgren,&nbsp;Yongchao Wu,&nbsp;Yong Wang,&nbsp;Jiahui Liu,&nbsp;Keke Song,&nbsp;Bin Xu,&nbsp;Yifan Li,&nbsp;Tobias Hainer,&nbsp;Lucas Svensson,&nbsp;Julia Wiktor,&nbsp;Rui Zhao,&nbsp;Hongfu Huang,&nbsp;Cheng Qian,&nbsp;Shuo Zhang,&nbsp;Zezhu Zeng,&nbsp;Bohan Zhang,&nbsp;Benrui Tang,&nbsp;Yang Xiao,&nbsp;Zihan Yan,&nbsp;Jiuyang Shi,&nbsp;Zhixin Liang,&nbsp;Junjie Wang,&nbsp;Ting Liang,&nbsp;Shuo Cao,&nbsp;Yanzhou Wang,&nbsp;Penghua Ying,&nbsp;Nan Xu,&nbsp;Chengbing Chen,&nbsp;Yuwen Zhang,&nbsp;Zherui Chen,&nbsp;Xin Wu,&nbsp;Wenwu Jiang,&nbsp;Esme Berger,&nbsp;Yanlong Li,&nbsp;Shunda Chen,&nbsp;Alexander J. Gabourie,&nbsp;Haikuan Dong,&nbsp;Shiyun Xiong,&nbsp;Ning Wei,&nbsp;Yue Chen,&nbsp;Jianbin Xu,&nbsp;Feng Ding,&nbsp;Zhimei Sun,&nbsp;Tapio Ala-Nissila,&nbsp;Ari Harju,&nbsp;Jincheng Zheng,&nbsp;Pengfei Guan,&nbsp;Paul Erhart,&nbsp;Jian Sun,&nbsp;Wengen Ouyang,&nbsp;Yanjing Su,&nbsp;Zheyong Fan","doi":"10.1002/mgea.70028","DOIUrl":"https://doi.org/10.1002/mgea.70028","url":null,"abstract":"<p>This paper provides a comprehensive overview of the latest stable release of the graphics processing units molecular dynamics (GPUMD) package, GPUMD 4.0. We begin with a brief review of its development history, starting from the initial version. We then discuss the theoretical foundations for the development of the GPUMD package, including the formulations of the interatomic force, virial and heat current for many-body potentials, the development of the highly efficient and flexible neuroevolution potential (NEP) method, the supported integrators and related operations, the various physical properties that can be calculated on the fly, and the GPUMD ecosystem. After presenting these functionalities, we review a range of applications enabled by GPUMD, particularly in combination with the NEP approach. Finally, we outline possible future development directions for GPUMD.</p>","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"3 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.70028","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145146206","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-throughput investigation of processing–microstructure relation in quenching and partitioning steels via gradient heat treatment and rapid characterization","authors":"Daicheng Lin,&nbsp;Yizhuang Li,&nbsp;Yibo Zhang,&nbsp;Dong Ma,&nbsp;Wei Xu,&nbsp;Zhiyuan Liang","doi":"10.1002/mgea.70025","DOIUrl":"https://doi.org/10.1002/mgea.70025","url":null,"abstract":"<p>This study presents a new high-throughput method to investigate the relationship between the quenching temperature (QT) and microstructure in quenching and partitioning (Q&amp;P) steels produced by “one-step” Q&amp;P treatment. This approach involves a gradient heat treatment, in which a rod sample is quenched and then a significant temperature gradient is established along the rod for isothermal holding, allowing the exploration of QT from 457 to 280°C in a single heat treatment. Synchrotron X-ray diffraction and high-speed nanoindentation mapping were used to efficiently measure phase fractions and the carbon content in retained austenite (RA) across different QTs. The results show that as QT decreases, a larger fraction of austenite transforms into martensite and bainite during isothermal holding, leading to increased carbon enrichment in the untransformed austenite. The volume fraction of RA initially increases with decreasing QT due to carbon enrichment, then decreases as the untransformed austenite fraction reduces after isothermal holding. The experimental results are compared to the predictions by thermodynamic models, which tend to overestimate the kinetics of phase transformation and carbon partitioning, emphasizing the importance of high-throughput experimental validation.</p>","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"3 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.70025","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145146255","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-throughput computational screening of porous materials for CO2 removal from Fischer–Tropsch synthesis","authors":"Junpeng Yuan,&nbsp;Min Li,&nbsp;Hui Wang","doi":"10.1002/mgea.70023","DOIUrl":"https://doi.org/10.1002/mgea.70023","url":null,"abstract":"<p>Fischer–Tropsch synthesis is an important method for producing clean fuels and fine chemicals, but by-products such as CO₂ bring severe challenges of low energy utilization and air pollution in commercial-scale production. In this work, the competitive adsorption selectivity of CO₂ in a five-component gas mixture of tens of thousands of porous materials was calculated based on high-throughput screening and grand canonical Monte Carlo simulation. Seven promising CO₂-type adsorbents were obtained under equimolar and industrial components, among which RUBTAK03 had a higher adsorption selectivity between 65 and 75. The CO₂ adsorption capacity of KINNIG under a single component was 8.72 mmol/g at 298 K and 1 bar, surpassing most well-known metal–organic frameworks. This strong CO₂ capture performance originates from three-dimensional interlaced channels, fluorinated organic ligands, and ultra-micropores, including channels and cages. In particular, this type of porous material composed of organic ligands or inorganic pillars containing fluorine atoms achieves an efficient capture of CO₂ from air and industrial tail gas, providing theoretical guidance for the design of novel and efficient adsorbents.</p>","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"3 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.70023","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145146471","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 general approach to qualitatively and graphically characterize the diffuse behavior of interstitial nonmetallic atoms in multi-principal element alloys based on site preference","authors":"Yang Qiao,&nbsp;Xingyu Chen,&nbsp;Bo Wu,&nbsp;Jiawen Sun,&nbsp;Jiaming Huang,&nbsp;Xiangyan Su,&nbsp;Xiaolin Zhou,&nbsp;Xiaoqiong Zhang,&nbsp;Xuan Fang,&nbsp;Yan Zhao,&nbsp;Baisheng Sa,&nbsp;Ming Liu,&nbsp;Yu Liu,&nbsp;Chunxu Wang,&nbsp;Frank Vrionis","doi":"10.1002/mgea.70021","DOIUrl":"https://doi.org/10.1002/mgea.70021","url":null,"abstract":"<p>It is urgent to establish a series of reasonable and general approaches to qualitatively and graphically characterize the four core effects of multi-principal element alloys (MPEAs) based on the inherent site preference. In this contribution, a qualitatively and graphically characterizing approach to the diffusion behavior of interstitial nonmetallic atoms diffusing along the neighboring octahedra in MPEAs was explored intensively. For this purpose, the C atom diffusing along the neighboring octahedra in FCC_CoNiV MPEA with (V_1.0000)_1a(Co_0.4445Ni_0.4444V_0.1111)_3c, a constant ordered occupying configuration predicted in our previous paper, was demonstrated in detail. Six distinct diffusion paths along [110], [101], and [011] directions on <i>XY</i>, <i>XZ</i>, and <i>YZ</i> planes of FCC_CoNiV MPEA with forward and backward diffusion directions were explored one by one, respectively. The diffusion energy barrier, diffusion coefficient, diffusion constant, and activation energy were derived by employing first-principles calculations based on density functional theory alongside the Climbing Image Nudged Elastic Band method. Unlike diffusing behavior in pure metallic elements, the non-periodic diffusion energy barrier waves are revealed for the real FCC_CoNiV MPEA structure. The significant variations in the diffusion energy barriers are influenced by the atomic environment, particularly the interaction between V and C atoms, which enhances the localization of electrons and increases the overall diffusion energy barrier. The energy barriers show similar trends along six paths, but significant variations occur across different octahedral sites.</p>","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"3 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.70021","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145146243","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":"PyGAMD: Python graphics processing unit-accelerated molecular dynamics software","authors":"Jialei Xu,&nbsp;Shenghong Guo,&nbsp;Miaolan Zhen,&nbsp;Zhuochen Yu,&nbsp;Youliang Zhu,&nbsp;Giuseppe Milano,&nbsp;Zhongyuan Lu","doi":"10.1002/mgea.70019","DOIUrl":"https://doi.org/10.1002/mgea.70019","url":null,"abstract":"<p>PyGAMD (Python GPU-accelerated molecular dynamics software) is a molecular simulation platform developed from scratch. It is designed for soft matter, especially for polymer by integrating coarse-grained/multi-scale models, methods, and force fields. It essentially includes an interpreter of molecular dynamics (MD) which supports secondary programming so that users can write their own functions by themselves, such as analytical potential forms for nonbonded, bond, angle, and dihedral interactions in an easy way, greatly extending the flexibility of MD simulations. The interpreter is written by pure Python language, making it easy to be modified and further developed. Some built-in libraries written by other languages that have been compiled for Python are added into PyGAMD to extend it's features, including configuration initialization, property analysis, etc. Machine learning force fields that are trained by DeePMD-kit are supported by PyGAMD for conveniently implementing multi-scale modeling and simulations. By designing an advanced framework of software, graphics processing unit-acceleration achieved by the Numba library of Python and compute unified device architecture reaches a high computing efficiency.</p>","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"3 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.70019","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144514914","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":"Deep learning accelerated the discovery of multi-principal element alloys with various strength-toughness trade-offs","authors":"Chunhui Fan,&nbsp;Hong Luo,&nbsp;Qiancheng Zhao,&nbsp;Xuefei Wang,&nbsp;Hongxu Cheng,&nbsp;Yue Chang","doi":"10.1002/mgea.70020","DOIUrl":"https://doi.org/10.1002/mgea.70020","url":null,"abstract":"<p>Machine learning has significantly enhanced the efficiency of multi-principal element alloys (MPEAs) development. Nonetheless, despite its potential, the rapid discovery of MPEAs with various strength-toughness trade-offs remains a largely unexplored area. This challenge lies in the inherent trade-off between strength and toughness, the complexity and scarcity of existing MPEAs data, and the absence of efficient strategies for Pareto front optimization in high-dimensional and sparse composition design spaces. Here, we present an alloy design framework that integrates multiple deep learning models and Pareto optimization algorithms to address these challenges. Remarkably, through merely three iterations, the framework yields eight MPEAs that notably surpassed the original dataset benchmarks, showing varied strength-toughness trade-offs. Microstructural analysis further confirmed the framework's ability to influence phase formation and microstructure through precise alloy composition adjustments, achieving outstanding and various strength-toughness combinations. Given its effectiveness, it holds substantial application potential in accelerating the design of materials tailored to meet a wide range of strength and toughness requirements.</p>","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"3 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.70020","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145146208","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-guided plasticity model in refractory high-entropy alloys","authors":"Shang Zhao,&nbsp;Jinshan Li,&nbsp;Weijie Liao,&nbsp;Ruihao Yuan","doi":"10.1002/mgea.70022","DOIUrl":"https://doi.org/10.1002/mgea.70022","url":null,"abstract":"<p>Refractory high-entropy alloys (RHEAs) represent a promising class of structural materials with significant potential for various applications. However, their limited plasticity at room temperature restricts their deformability, posing challenges for processing and industrial implementation. Traditional experimental methods for characterizing this property are time-consuming and resource-intensive, necessitating the development of efficient predictive models. In this study, we propose a machine learning approach to predict the fracture strain of RHEAs. A dataset comprising 128 RHEAs fracture strain samples is compiled from the literature and classified into two categories: “high plasticity” and “low plasticity.” Through feature selection techniques, a critical subset of features is identified, enabling a support vector classification model to achieve 96% prediction accuracy. Additionally, an interpretable machine learning algorithm is employed to derive explicit functional expressions describing the relationship between key features and fracture strain, achieving 88% accuracy. Although slightly less accurate, it provides valuable insights into the underlying mechanisms, making it a useful tool for materials design and optimization.</p>","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"3 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.70022","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144514913","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}