Predicting single-phase solid solutions in as-sputtered high entropy alloys: High-throughput screening with machine-learning model

Ren, Ji‐Chang; Zhou, Junjun; Butch, Christopher J.; Ding, Zhigang; Li, Shuang; Zhao, Yongli; Liu, Wei

doi:10.1016/j.jmst.2022.07.059

Cited by 21 publications

(4 citation statements)

References 54 publications

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“…Their experiments convincingly illustrate that VEC values exceeding 8 promote the formation of FCC phases, while values lower than 6.87 tend to favor BCC phases. These findings have been corroborated by various ML studies, including those conducted by Singh et al 89 and Ren et al 59 . To further scrutinize VEC’s influence across the dataset, we calculated mean VEC values along with standard deviations (Fig.…”

Section: Discussionsupporting

confidence: 66%

“…Notably, since 2018, many studies have explored the application of ML in predicting phases, encompassing various aspects. Some studies focused on distinguishing whether a SS is a single phase or a non-single phase 30 ; other works delved into classifying SSS vs dual phase (SS+IM) 44 ; SSS vs IM vs AM 45 – 48 ; SSS vs IM vs dual phase (SS+IM) 49 , 50 ; SSS vs IM vs AM vs dual phase (SS+IM) 37 , 51 – 53 ; SSS vs IM vs AM vs dual phase (SS+AM) 54 ; FCC vs BCC vs dual phase (BCC+FCC) 55 – 58 ; FCC vs BCC vs dual phase (BCC+FCC) vs IM 59 , 60 ; FCC vs BCC vs HCP vs IM vs others 61 , 62 ; FCC vs BCC vs IM vs AM 63 ; FCC vs BCC vs HCP vs Multiphase 64 . The existing body of literature highlights the significance of predicting whether a SS is a single phase or a mixture of solid solutions with other phases.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, although there have been significant contributions to the use of ML for phase prediction in HEAs, most of these studies relied on relatively small datasets containing fewer than 1000 experimental records with similar features. The handful of studies that employed larger datasets with over 2000 data points, such as 54 , 59 , 61 , 70 , are either limited in terms of the number of elements and phases considered or experience unequal distribution of samples across different classes or categories. These limitations highlight the need for studies that can address a wider range of elements and phases while ensuring a balanced dataset.…”

Section: Introductionmentioning

confidence: 99%

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Data-driven analysis and prediction of stable phases for high-entropy alloy design

Peivaste,

Jossou,

Tiamiyu

2023

Sci Rep

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High-entropy alloys (HEAs) represent a promising class of materials with exceptional structural and functional properties. However, their design and optimization pose challenges due to the large composition-phase space coupled with the complex and diverse nature of the phase formation dynamics. In this study, a data-driven approach that utilizes machine learning (ML) techniques to predict HEA phases and their composition-dependent phases is proposed. By employing a comprehensive dataset comprising 5692 experimental records encompassing 50 elements and 11 phase categories, we compare the performance of various ML models. Our analysis identifies the most influential features for accurate phase prediction. Furthermore, the class imbalance is addressed by employing data augmentation methods, raising the number of records to 1500 in each category, and ensuring a balanced representation of phase categories. The results show that XGBoost and Random Forest consistently outperform the other models, achieving 86% accuracy in predicting all phases. Additionally, this work provides an extensive analysis of HEA phase formers, showing the contributions of elements and features to the presence of specific phases. We also examine the impact of including different phases on ML model accuracy and feature significance. Notably, the findings underscore the need for ML model selection based on specific applications and desired predictions, as feature importance varies across models and phases. This study significantly advances the understanding of HEA phase formation, enabling targeted alloy design and fostering progress in the field of materials science.

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Section: Discussionsupporting

confidence: 66%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Data-driven analysis and prediction of stable phases for high-entropy alloy design

Peivaste,

Jossou,

Tiamiyu

2023

Sci Rep

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“…ML techniques have significantly advanced the establishment of high-throughput screening (HTS) pipelines for discovering a broad spectrum of materials. − This progress is evident in the polymer domain, primarily attributed to the evolution of polymer informatics, which has introduced a diverse set of descriptors for numerically characterizing polymers, including fingerprint descriptors, physiochemical descriptors, and graph descriptors. − Given the complex nature of polymers, in terms of their structure and composition, continuous efforts are being made to enhance these descriptors to better represent and predict polymer properties. For the purpose of predicting a range of polymer properties, Ramprasad et al developed a hierarchical fingerprint encompassing three distinct scales: atomic, quantitative structure–property relationship, and morphological levels .…”

Section: Introductionmentioning

confidence: 99%

Structure-Based Multilevel Descriptors for High-throughput Screening of Elastomers

Deng,

Chen,

et al. 2023

J. Phys. Chem. B

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To discover new materials, high-throughput screening (HTS) with machine learning (ML) requires universally available descriptors that can accurately predict the desired properties. For elastomers, experimental and simulation data in current descriptors may not be available for all candidates of interest, hindering elastomer discovery through HTS. To address this challenge, we introduce structure-based multilevel (SM) descriptors of elastomers derived solely from molecular structure that is universally available. Our SM descriptors are hierarchically organized to capture both local soft and hard segment structures as well as the global structures of elastomers. With the SM-Morgan Fingerprint (SM-MF) descriptor, one of our SM descriptors, a machine learning model accurately predicts elastomer toughness with a remarkable accuracy of 0.91. Furthermore, an HTS pipeline is established to swiftly screen elastomers with targeted toughness. We also demonstrate the generality and applicability of SM descriptors by using them to construct HTS pipelines for screening elastomers with a targeted critical strain or Young's modulus. The user-friendliness and low computational cost of SM descriptors make them a promising tool to significantly enhance HTS in the search for novel materials.

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Exploring the relationship between urban residents' emotional changes and built environment before and during the COVID-19 pandemic from the perspective of resilience

Dai

Wang

et al. 2023

Cities

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Predicting single-phase solid solutions in as-sputtered high entropy alloys: High-throughput screening with machine-learning model

Cited by 21 publications

References 54 publications

Data-driven analysis and prediction of stable phases for high-entropy alloy design

Data-driven analysis and prediction of stable phases for high-entropy alloy design

Structure-Based Multilevel Descriptors for High-throughput Screening of Elastomers

Exploring the relationship between urban residents' emotional changes and built environment before and during the COVID-19 pandemic from the perspective of resilience

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