Materials Science, Computational Theory and Mathematics
16
Scopus Publications
Scopus Publications
Towards understanding structure–property relations in materials with interpretable deep learning Tien-Sinh Vu, Minh-Quyet Ha, Duong-Nguyen Nguyen, Viet-Cuong Nguyen, Yukihiro Abe, et al. Npj Computational Materials, 2023 Deep learning (DL) models currently employed in materials research exhibit certain limitations in delivering meaningful information for interpreting predictions and comprehending the relationships between structure and material properties. To address these limitations, we propose an interpretable DL architecture that incorporates the attention mechanism to predict material properties and gain insights into their structure–property relationships. The proposed architecture is evaluated using two well-known datasets (the QM9 and the Materials Project datasets), and three in-house-developed computational materials datasets. Train–test–split validations confirm that the models derived using the proposed DL architecture exhibit strong predictive capabilities, which are comparable to those of current state-of-the-art models. Furthermore, comparative validations, based on first-principles calculations, indicate that the degree of attention of the atoms’ local structures to the representation of the material structure is critical when interpreting structure–property relationships with respect to physical properties. These properties encompass molecular orbital energies and the formation energies of crystals. The proposed architecture shows great potential in accelerating material design by predicting material properties and explicitly identifying crucial features within the corresponding structures.
Machine learning-aided Genetic algorithm in investigating the structure-property relationship of SmFe12-based structures Duong-Nguyen Nguyen, Hieu-Chi Dam Journal of Applied Physics, 2023 We investigate the correlation between geometrical information, stability, and magnetization of SmFe12-based structures using machine learning-aided genetic algorithm structure generation and first-principle calculation. In parallel with structure generation inherited using the USPEX program, a pool of structures is created for every population using the sub-symmetry perturbation method. A framework using embedded orbital field matrix representation as structure fingerprint and Gaussian process as a predictor has been applied to ranking the most potential stability structures. As a result, the original structure SmFe12 with the well-known tetragonal I4/mmm symmetry is investigated with a parabolic dependence between formation energy and its magnetization by continuous distortions of the unit-cell lattice parameter and individual sites. Notably, a SmFe12 structure with I4/mmm symmetry is found with 7.5% increasing magnetization while keeping the similar formation energy with the most stable structures in this family. With SmFe11CoN family, structures with N interstitial position in the center of Sm and Fe octahedron show outperform all other structures in both ability of stabilization and remaining high magnetization of the original structure. Finally, further investigation using metric learning embedding space brings valuable insight into the correlation between geometrical arrangement, stability, and magnetization of this structure family.
Evidence-based data mining method to reveal similarities between materials based on physical mechanisms Minh-Quyet Ha, Duong-Nguyen Nguyen, Viet-Cuong Nguyen, Hiori Kino, Yasunobu Ando, et al. Journal of Applied Physics, 2023 Measuring the similarity between materials is essential for estimating their properties and revealing the associated physical mechanisms. However, current methods for measuring the similarity between materials rely on theoretically derived descriptors and parameters fitted from experimental or computational data, which are often insufficient and biased. Furthermore, outliers and data generated by multiple mechanisms are usually included in the dataset, making the data-driven approach challenging and mathematically complicated. To overcome such issues, we apply the Dempster–Shafer theory to develop an evidential regression-based similarity measurement (eRSM) method, which can rationally transform data into evidence. It then combines such evidence to conclude the similarities between materials, considering their physical properties. To evaluate the eRSM, we used two material datasets, including 3d transition metal–4f rare-earth binary and quaternary high-entropy alloys with target properties, Curie temperature, and magnetization. Based on the information obtained on the similarities between the materials, a clustering technique is applied to learn the cluster structures of the materials that facilitate the interpretation of the mechanism. The unsupervised learning experiments demonstrate that the obtained similarities are applicable to detect anomalies and appropriately identify groups of materials whose properties correlate differently with their compositions. Furthermore, significant improvements in the accuracies of the predictions for the Curie temperature and magnetization of the quaternary alloys are obtained by introducing the similarities, with the reduction in mean absolute errors of 36% and 18%, respectively. The results show that the eRSM can adequately measure the similarities and dissimilarities between materials in these datasets with respect to mechanisms of the target properties.
Explainable active learning in investigating structure–stability of SmFe12-α-β X α Y β structures X, Y {Mo, Zn, Co, Cu, Ti, Al, Ga} Duong-Nguyen Nguyen, Hiori Kino, Takashi Miyake, Hieu-Chi Dam MRS Bulletin, 2023 In this article, we propose a query-and-learn active learning approach combined with first-principles calculations to rapidly search for potentially stable crystal structure via elemental substitution, to clarify their stabilization mechanism, and integrate this approach to SmFe$$_{12}$$ 12 -based compounds with ThMn$$_{12}$$ 12 structure, which exhibits prominent magnetic properties. The proposed method aims to (1) accurately estimate formation energies with limited first-principles calculation data, (2) visually monitor the progress of the structure search process, (3) extract correlations between structures and formation energies, and (4) recommend the most beneficial candidates of SmFe$$_{12}$$ 12 -substituted structures for the subsequent first-principles calculations. The structures of SmFe$$_{12-\\upalpha -\\upbeta }\\mathsf {X}_{\\upalpha }\\mathsf {Y}_{\\upbeta }$$ 12 - α - β X α Y β before optimization are prepared by substituting $$\\mathsf {X}, \\mathsf {Y}$$ X , Y elements—Mo, Zn, Co, Cu, Ti, Al, Ga—in the region of $$\\upalpha +\\upbeta <4$$ α + β < 4 into iron sites of the original SmFe$$_{12}$$ 12 structures. Using the optimized structures and formation energies obtained from the first-principles calculations after each active learning cycle, we construct an embedded two-dimensional space to rationally visualize the set of all the calculated and not-yet-calculated structures for monitoring the progress of the search. Our machine learning model with an embedding representation attained a prediction error for the formation energy of $$1.25\\times 10^{-2}$$ 1.25 × 10 - 2 (eV/atom) and required only one-sixth of the training data compared to other learning methods. Moreover, the time required to recall most potentially stable structures was nearly four times faster than the random search. The formation energy landscape visualized using the embedding representation revealed that the substitutions of Al and Ga have the highest potential to stabilize the SmFe$$_{12}$$ 12 structure. In particular, SmFe$$_{9}$$ 9 [Al/Ga]$$_{2}$$ 2 Ti showed the highest stability among the investigated structures. Finally, by quantitatively measuring the change in the structures before and after optimization using OFM descriptors, the correlations between the coordination number of substitution sites and the resulting formation energy are revealed. The negative-formation-energy-family SmFe$$_{12-\\upalpha -\\upbeta }$$ 12 - α - β [Al/Ga]$$_{\\upalpha }\\mathsf {Y}_{\\upbeta }$$ α Y β structures show a common trend of increasing coordination number at substituted sites, whereas structures with positive formation energy show a corresponding decreasing trend. Impact statement Seeking the next generation of high-performance magnets is a crucial demand for replacing the widely accepted Nd-Fe-B magnets developed in the middle 80s. The iron-rich compounds with the original tetragonal ThMn12 structure appear as the most potential candidates except for the hard synthesizing it in nature due to its high energy of formation. Stabilization for this material system is expected by substituting new elements, but the vast number of possible structures makes the exploration difficult even for theoretical calculations. This article proposes an integration of first-principles calculations and explainable active learning to efficiently explore the crystal structure space of this material system. In particular, the explored crystal structure space can be rationally visualized, on which the relationship between substitution elements, substitution sites, and crystal structure stabilization can be intuitively interpreted.
Variation of Local Structure and Reactivity of Pt/C Catalyst for Accelerated Degradation Test of Polymer Electrolyte Fuel Cell Visualized by Operando 3D CT-XAFS Imaging Hirosuke Matsui, Nozomu Ishiguro, Yuanyuan Tan, Naoyuki Maejima, Yuta Muramoto, et al. Chemnanomat, 2022 In practical applications of polymer electrolyte fuel cells (PEFC), the wide variations of local structures and environment of electrocatalysts bring about complex reaction behaviours inside a membrane electrode assembly (MEA). The variations of local coordination structure and redox response of a Pt/C cathode catalyst in an MEA before and after a typical accelerated degradation test (ADT) were three‐dimensionally visualized by operando Pt LIII‐edge computed‐tomography X‐ray absorption fine structure (CT‐XAFS) imaging under PEFC operating conditions for the first time. The set of operando CT‐XANES and CT‐EXAFS analyses visualized changes in the local coordination structure and redox reactivity of the Pt catalyst by a typical ADT process.
Evidence-based recommender system for high-entropy alloys Minh-Quyet Ha, Duong-Nguyen Nguyen, Viet-Cuong Nguyen, Takahiro Nagata, Toyohiro Chikyow, et al. Nature Computational Science, 2021 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.
Explainable machine learning for materials discovery: Predicting the potentially formable Nd-Fe-B crystal structures and extracting the structure-stability relationship Tien-Lam Pham, Duong-Nguyen Nguyen, Minh-Quyet Ha, Hiori Kino, Takashi Miyake, et al. Iucrj, 2020 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 data-driven 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 achieved 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 achieved 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.
Boron cage effects on Nd-Fe-B crystal structure's stability Duong-Nguyen Nguyen, Duc-Anh Dao, Takashi Miyake, Hieu-Chi Dam Journal of Chemical Physics, 2020 In this study, we investigate the structure–stability relationship of hypothetical Nd–Fe–B crystal structures using descriptor-relevance analysis and the t-SNE dimensionality reduction method. 149 hypothetical Nd–Fe–B crystal structures are generated from 5967 LA–T–X host structures in the Open Quantum Materials Database by using the elemental substitution method, with LA denoting lanthanides, T denoting transition metals, and X denoting light elements such as B, C, N, and O. By borrowing the skeletal structure of each of the host materials, a hypothetical 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-principle calculations are applied to evaluate the phase stability of these structures. Twenty of them are found to be potentially formable. As the first investigative result, the descriptor-relevance analysis on the orbital field matrix (OFM) materials’ descriptor reveals the average atomic coordination number as the essential factor in determining the structure stability of these substituted Nd–Fe–B crystal structures. 19 among 20 hypothetical structures that are found potentially formable have an average coordination number larger than 6.5. By applying the t-SNE dimensionality reduction method, all the local structures represented by the OFM descriptors are integrated into a visible space to study the detailed correlation between their characteristics and the stability of the crystal structure to which they belong. We discover that unstable substituted structures frequently carry Nd and Fe local structures with two prominent points: low average coordination numbers and fully occupied B neighboring atoms. Moreover, there are only three popular forms of B local structures appearing on all potentially formable substituted structures: cage networks, planar networks, and interstitial sites. The discovered relationships are promising to speed up the screening process for the new formable crystal structures.
Erratum: Ensemble learning reveals dissimilarity between rare-earth transition metal binary alloys with respect to the Curie temperature (JPhys Materials (2019) 2 (034009) DOI: 10.1088/2515-7639/AB1738) Duong-Nguyen Nguyen, Tien-Lam Pham, Viet-Cuong Nguyen, Hiori Kino, Takashi Miyake, et al. Jphys Materials, 2020 In the original paper [ 1 we introduced a voting method for measuring dissimilarity materials with respect to a given target 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 reference training materials contributed to the prediction model that accurately predicts the physical property value of a speci fi c material, are considered to be similar to that material, or vice versa.
Oxygen-diffusion-driven oxidation behavior and tracking areas visualized by X-ray spectro-ptychography with unsupervised learning Makoto Hirose, Nozomu Ishiguro, Kei Shimomura, Duong-Nguyen Nguyen, Hirosuke Matsui, et al. Communications Chemistry, 2019 Oxygen storage and release with oxygen diffusion in the bulk of the cerium–zirconium solid solution oxide Ce2Zr2Ox (x = 7–8), which possesses an atomically ordered arrangement of cerium and zirconium atoms, is the key to three-way exhaust catalysis. Oxygen storage proceeds via heterogeneous oxygen diffusion into the vacant sites of Ce2Zr2O7 particles, but the heterogeneous oxygen diffusion track is erased after oxygen storage in the Ce2Zr2Ox bulk. Here we show three-dimensional hard X-ray spectro-ptychography to clearly visualize the three-dimensional cerium valence map in Ce2Zr2Ox particles, and unsupervised learning reveals the concealed oxygen-diffusion-driven three-dimensional nanoscale cerium oxidation behavior and tracking areas inside individual mixed-oxide particles during the oxygen storage process. The described approach may permit the nanoscale chemical imaging of reaction tracking areas in solid materials.
