Machine-learning enabled thermodynamic model for the design of new rare-earth compounds

Singh, Prashant; Rose, Tyler Del; Vazquez, G.; Arróyave, Raymundo; Mudryk, Yaroslav

doi:10.1016/j.actamat.2022.117759

Cited by 19 publications

(7 citation statements)

References 94 publications

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“…REFe 2 compounds with a Laves-phase MgCu 2 crystal structure were studied in our earlier study. , Thus, to validate the ML model experimentally, we chose the Ce x Zr 1– x Fe 2 system, which does not contain any critical elements. While Zr is not a rare-earth element, it is an early d-metal and has shown crystallographic and electronic similarities with rare-earth elements.…”

Section: Resultsmentioning

confidence: 99%

Physics-Informed Machine-Learning Prediction of Curie Temperatures and Its Promise for Guiding the Discovery of Functional Magnetic Materials

et al. 2023

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High-performance permanent magnets with a high Curie temperature, containing less critical materials, are integral to zero-carbon energy solutions. We built a machine-learning model trained over available experimentally measured Curie temperature values to predict the T C of multicomponent magnetic materials. We chose two compositions from a pseudo-binary (Zr 1−x Ce x )Fe 2 system, namely, (Zr 0.16 Ce 0.84 )Fe 2 and (Zr 0.94 Ce 0.06 )Fe 2 , to experimentally validate the ability of our model to predict the Curie temperature of novel compounds. We also provided a detailed discussion on the correlation of the Curie temperature with the de Gennes scaling factor in rare-earth intermetallic compounds and its breakdown below a certain rare-earth content. The electronic structure calculations (density of states and Fermi surface) were performed using the density functional theory on selected compounds (Zr 0.16 Ce 0.84 )Fe 2 and (Zr 0.94 Ce 0.06 )Fe 2 to understand the electronic origin of a strong magnetic exchange. We found that the change in the electronic density of states and electron/hole fillings at the Fermi level directly correlate with the Curie temperature. Notably, our model was able to capture these key electronic structure trends, which show that physics-informed machine learning can play a crucial role in designing new high-performance magnets with improved properties for environmentally sustainable applications.

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Section: Resultsmentioning

confidence: 99%

Physics-Informed Machine-Learning Prediction of Curie Temperatures and Its Promise for Guiding the Discovery of Functional Magnetic Materials

et al. 2023

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“…At present, machine learning has been applied in rare earth compound design, rare earth separation, and the prediction of rare earth magnets . Mudryk et al curated an extensive rare earth database comprising over 600+ compounds, employing the machine learning technique based on SISSO , (sure independence screening and sparsifying operator), training and testing models for predicting the formation enthalpies of rare earth compounds. This study offers valuable quantitative insights into compositional considerations for machine learning models and the exploration of metastable materials.…”

Section: Perspective and Conclusionmentioning

confidence: 99%

Development and Prospects in the Structure and Performance of Lanthanide Single-Molecule Magnets

Li,

Yang,

et al. 2024

J. Phys. Chem. C

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Single molecule magnets (SMMs) constitute a pivotal class of nanoscale molecular magnets with promising potential across various domains such as high-density information storage, quantum computing, and spin electron devices. Among these, lanthanide-based SMMs have garnered significant attention in recent research due to their distinctive magnetic and coordination properties, offering prospects for substantial advancements in information storage technologies. Over the past three decades, considerable efforts have been directed toward developing novel SMMs characterized by substantial anisotropy barrier and blocking temperature, leading to significant strides in the field. This perspective delves into the latest developments in the structure and performance of lanthanide SMMs, providing valuable insights into optimizing their functionality. The pivotal roles of ligands and central atoms in modulating the anisotropy of SMMs are emphasized alongside strategies to augment magnetic anisotropy and explore their potential surface applications. Additionally, this perspective explores the fusion of theoretical research with machine learning methodologies to identify highly anisotropic SMMs, thereby outlining a roadmap for optimizing the functionality of lanthanide-based SMMs.

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“…Machine learning offers a fresh approach to this long-standing problem of classifying Laves phases. It has been used recently to predict the effect of alloying on the hydrogen storage properties and formation enthalpies of Laves phases. , Here, we apply machine learning methods to predict whether a Laves phase is likely to be adopted for any given composition AB 2 , taking into account variations caused by the solid solubility. Because the data set of known Laves vs non-Laves phases is well balanced, numerous, and diverse, we hypothesize that machine learning models can be trained that will be capable and suitable for distinguishing between them.…”

Section: Introductionmentioning

confidence: 99%

Ahead by a Century: Discovery of Laves Phases Assisted by Machine Learning

Sikdar,

Roy,

Selvaratnam

et al. 2024

Inorg. Chem.

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Laves phases AB 2 form the most abundant group of intermetallic compounds, consisting of combinations of larger electropositive metals A with smaller metals B. Many practical applications of Laves phases depend on the ability to tune their physical properties through appropriate substitution of either the A or B component. Although simple geometrical and electronic factors have long been thought to control the formation of Laves phases, no single factor alone can make predictions accurately. Several machine learning models have been developed to discover new Laves phases, including variations caused by solid solubility, using elemental properties solely on the basis of chemical composition. These models were trained on a data set comprising about 3700 entries of experimentally known phases AB 2 with Laves and non-Laves structures. Among these models, a decision tree algorithm gave very good performance (average recall of 95%, precision of 94%, and accuracy of 96% on the test set) by using only a small set of descriptors, the most important of which relates to the electron density at the boundary of the Wigner−Seitz cell for the B component. This model provides guidance for new experiments by making predictions on >400000 candidates very quickly. A chemically unintuitive candidate Cd(Cu 1−x Sb x ) 2 with a limited solid solubility of Sb for Cu was targeted; it was successfully synthesized and confirmed to adopt a cubic MgCu 2 -type Laves structure.

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Machine-learning enabled thermodynamic model for the design of new rare-earth compounds

Cited by 19 publications

References 94 publications

Physics-Informed Machine-Learning Prediction of Curie Temperatures and Its Promise for Guiding the Discovery of Functional Magnetic Materials

Physics-Informed Machine-Learning Prediction of Curie Temperatures and Its Promise for Guiding the Discovery of Functional Magnetic Materials

Development and Prospects in the Structure and Performance of Lanthanide Single-Molecule Magnets

Ahead by a Century: Discovery of Laves Phases Assisted by Machine Learning

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