Committee machine that votes for similarity between materials

Nguyen, Duong-Nguyen; Lam, Pham Tien; Nguyen, Viet-Cuong; Ho, Tuan-Dung; Tran, Truyen; Takahashi, Keisuke; Dam, H. van

doi:10.1107/s2052252518013519

Cited by 6 publications

(3 citation statements)

References 52 publications

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“…Details about setting parameters are described in table 1 in the supplemental materials. The model selection and relation among variables are discussed in [23,24]. The high prediction accuracy level of this model shows that it is possible to accurately predict the T C values of rare-earth transition bimetal materials with the designed variables.…”

Section: Curie Temperature Analysismentioning

confidence: 99%

“…One of the well-known methods in this research direction is the mixture of experts model [3,4], which learns the gating functions to appropriately partition the descriptive space for identifying the components of mixture models. Further, linear regression-based clustering was recently developed [5][6][7] without partitioning the descriptive space. However, these models are sensitive to parameters setting, including the number of clusters, complexity of the learners (linear model), etc.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Ensemble learning reveals dissimilarity between rare-earth transition-metal binary alloys with respect to the Curie temperature

Nguyen

Lam

Nguyen³

et al. 2019

J. Phys. Mater.

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CORRIGENDUMCorrigendum: Ensemble learning reveals dissimilarity between rareearth transition metal binary alloys with respect to the Curie temperature (2019 J. Phys. Mater. 2 034009) AbstractWe propose a data-driven method to extract dissimilarity between materials, with respect to a given target physical property. The technique is based on an ensemble method with Kernel ridge regression as the predicting model; multiple random subset sampling of the materials is done to generate prediction models and the corresponding contributions of the reference training materials in detail. The distribution of the predicted values for each material can be approximated by a Gaussian mixture models. The reference training materials contributed to the prediction model that accurately predicts the physical property value of a specific material, are considered to be similar to that material, or vice versa. Evaluations using synthesized data demonstrate that the proposed method can effectively measure the dissimilarity between data instances. An application of the analysis method on the data of Curie temperature (T C ) of binary 3d transition metal-4f rare-earth binary alloys also reveals meaningful results on the relations between the materials. The proposed method can be considered as a potential tool for obtaining a deeper understanding of the structure of data, with respect to a target property, in particular.

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Section: Curie Temperature Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ensemble learning reveals dissimilarity between rare-earth transition-metal binary alloys with respect to the Curie temperature

Nguyen

Lam

Nguyen³

et al. 2019

J. Phys. Mater.

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“…Nd-Fe-B and evaluate the relevance [71][72][73] of each element in the OFM descriptor with respect to the formation energy of the crystal structure. We utilize the change in prediction accuracy when removing or adding a descriptor (from the full set of descriptors [74] in the OFM) to search for the descriptors that are strongly relevant [71,75] to the formation energy (i.e., CH distance and phase stability) of the Nd-Fe-B crystal structures.…”

Section: Further We Focus On D Hostmentioning

confidence: 99%

Explainable Machine Learning for Materials Discovery: Predicting the Potentially Formable Nd-Fe-B Crystal Structures and Extracting Structure-Stability Relationship

Lam¹,

Nguyen²,

Ha³

et al. 2020

Preprint

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New Nd-Fe-B crystal structures can be formed via the elemental substitution of LA-T-X host structures, including lanthanides (LA), transition metals (T), and light elements X = B, C, N, and O. The 5967 samples of ternary LA-T-X materials that are collected are then used as the host structures. For each host crystal structure, a substituted crystal structure is created by substituting all lanthanide sites with Nd, all transition metal sites with Fe, and all light element sites with B. High-throughput first-principles calculations are applied to evaluate the phase stability of the newly created crystal structures, and 20 of them are found to be potentially formable. A datadriven approach based on supervised and unsupervised learning techniques is applied to estimate the stability and analyze the structure-stability relationship of the newly created Nd-Fe-B crystal structures. For predicting the stability for the newly created Nd-Fe-B structures, three supervised learning models, kernel ridge regression, logistic classification, and decision tree model, are learned from the LA-T-X host crystal structures; the models achieve the maximum accuracy and recall scores of 70.4% and 68.7%, respectively. On the other hand, our proposed unsupervised learning model based on the integration of descriptor-relevance analysis and a Gaussian mixture model (GMM) achieves an accuracy and recall score of 72.9% and 82.1%, respectively, which are significantly better than those of the supervised models. While capturing and interpreting the structure-stability relationship of the Nd-Fe-B crystal structures, the unsupervised learning model indicates that the average atomic coordination number and coordination number of the Fe sites are the most important factors in determining the phase stability of the new substituted Nd-Fe-B crystal structures.

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Evidence-based recommender system for high-entropy alloys

Nguyen

Nguyen³

et al. 2021

Nat Comput Sci

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Existing data-driven approaches for exploring high-entropy alloys (HEAs) face three challenges: numerous element-combination candidates, designing appropriate descriptors, and limited and biased existing data. To overcome these issues, here we show the development of an evidence-based material recommender system (ERS) that adopts Dempster–Shafer theory, a general framework for reasoning with uncertainty. Herein, without using material descriptors, we model, collect and combine pieces of evidence from data about the HEA phase existence of alloys. To evaluate the ERS, we compared its HEA-recommendation capability with those of matrix-factorization- and supervised-learning-based recommender systems on four widely known datasets of up-to-five-component alloys. The k-fold cross-validation on the datasets suggests that the ERS outperforms all competitors. Furthermore, the ERS shows good extrapolation capabilities in recommending quaternary and quinary HEAs. We experimentally validated the most strongly recommended Fe–Co-based magnetic HEA (namely, FeCoMnNi) and confirmed that its thin film shows a body-centered cubic structure.

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Committee machine that votes for similarity between materials

Cited by 6 publications

References 52 publications

Ensemble learning reveals dissimilarity between rare-earth transition-metal binary alloys with respect to the Curie temperature

Ensemble learning reveals dissimilarity between rare-earth transition-metal binary alloys with respect to the Curie temperature

Explainable Machine Learning for Materials Discovery: Predicting the Potentially Formable Nd-Fe-B Crystal Structures and Extracting Structure-Stability Relationship

Evidence-based recommender system for high-entropy alloys

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