Gd12Co5.3Bi and Gd12Co5Bi, Crystalline Doppelgänger with Low Thermal Conductivities

Oliynyk, Anton O.; Sparks, Taylor D.; Gaultois, Michael W.; Ghadbeigi, Leila; Mar, Arthur

doi:10.1021/acs.inorgchem.6b00826

Cited by 20 publications

(16 citation statements)

References 23 publications

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“…The use of machine learning as a tool for materials discovery is rapidly growing. Examples can be found in the fields of thermoelectrics [4][5][6], superhard materials [7], thermochemical data [8,9], electronic properties [10][11][12][13][14], structural materials [15], functional materials [16][17][18], and structure classification [19][20][21][22][23]. Given the history of success of ML methods, it is natural to want to apply them to battery materials research.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Studies of Li-Ion-Battery Materials

2019

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Batteries are a critical component of modern society. The growing demand for new battery materials—coupled with a historically long materials development time—highlights the need for advances in battery materials development. Understanding battery systems has been frustratingly slow for the materials science community. In particular, the discovery of more abundant battery materials has been difficult. In this paper, we describe how machine learning tools can be exploited to predict the properties of battery materials. In particular, we report the challenges associated with a data-driven investigation of battery systems. Using a dataset of cathode materials and various statistical models, we predicted the specific discharge capacity at 25 cycles. We discuss the present limitations of this approach and propose a paradigm shift in the materials research process that would better allow data-driven approaches to excel in aiding the discovery of battery materials.

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Section: Introductionmentioning

confidence: 99%

Data-Driven Studies of Li-Ion-Battery Materials

2019

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“…These predictions were then experimentally validated with two new compounds. They specifically focus on a set of compounds derived from the engine, RE 12 Co 5 Bi (RE = Gd, Er), which exhibited high thermoelectric performance [80]. The engine successfully predicted that this set of materials had low thermal and high electrical conductivities, but modest Seebeck coefficients, all of which were then additionally verified experimentally.…”

Section: Random Forest (Rf)mentioning

confidence: 95%

Data-Driven Materials Discovery and Synthesis using Machine Learning Methods

Baird¹,

Marianne²,

Sayeed³

et al. 2022

Preprint

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Experimentally and computationally [39][40][41][42][43][44][45][46][47][48][49][50] validated machine learning (ML) articles are sorted based on size of the training data: 1-100, 101-10 000, and 10 000+ in a comprehensive set summarizing legacy and recent advances in the field. The review emphasizes the interrelated fields of synthesis, characterization, and prediction. Size range 1-100 consists mostly of Bayesian optimization (BO) articles, whereas 101-10 000 consists mostly of support vector machine (SVM) articles. The articles often use combinations of ML, feature selection (FS), adaptive design (AD), high-throughput (HiTp) techniques, and domain knowledge to enhance predictive performance and/or model interpretability. Grouping cross-validation (G-CV) techniques curb overly optimistic extrapolative predictive performance. Smallerdatasets relying on AD are typically able to identify new materials with desired properties but do so in a constrained design space. In larger datasets, the low-hanging fruit of materials optimization are typically already discovered, and the models are generally less successful at extrapolating to new mate-

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“…Machine learning and data mining have been successfully applied to materials problems across various domains. For example, they have been used to successfully identify new shape memory alloys, 22 ferroelectric materials, 23 and novel thermoelectrics, [24][25][26] to make property predictions for heat capacity, [27][28] band gap of crystalline solids, 29 and elastic moduli, 30 to optimize solar cells, 31 predict new phosphor materials, 32 and to classify crystal structures of inorganic compounds. [33][34][35][36][37][38] These methods generate predictions for unknown examples based on statistical relationships and patterns discovered using reliable data, informative descriptions of that data, and machine-learning algorithms.…”

Section: Foundation Of the Scar Methods And Creation Of Data Driven Momentioning

confidence: 99%

Single Crystal Automated Refinement (SCAR): A Data-Driven Method for Solving Inorganic Structures

Viswanathan¹,

Oliynyk²,

Antono³

et al. 2019

Preprint

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Single crystal diffraction is one of the most common experimental techniques in chemistry for determining a crystal structure. However, the process of crystal structure solution and refinement is not always straightforward. Methods to simplify and rationalize the path to the most optimal crystal structure model have been incorporated into various data processing and crystal structure solution software, with the focus generally on aiding macromolecular or protein structure solution. In this work, we propose a new method that uses single crystal data to solve the crystal structures of inorganic, extended solids called “Single Crystal Automated Refinement (SCAR).” The approach was developed using data mining and machine-learning methods and considers several structural features common in inorganic solids, like atom assignment based on physically reasonable distances, atomic statistical mixing, and crystallographic site deficiency. The output is a tree of possible solutions for the data set with a corresponding fit score indicating the most reasonable crystal structure. Here, the foundation for SCAR is presented followed by the implementation of SCAR to solve two newly synthesized and previously unreported phases, ZrAu0.5Os0.5 and Nd4Mn2AuGe4. The structure solutions are found to be comparable with manually solving the data set, including the same refined mixed occupancies and atomic deficiency, supporting the validity of this automatic structure solution method. The proposed SCAR program is thusly verified to be a fast and reliable assistant in solving even complex single crystal diffraction data for extended inorganic solids.

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Gd₁₂Co_5.3Bi and Gd₁₂Co₅Bi, Crystalline Doppelgänger with Low Thermal Conductivities

Cited by 20 publications

References 23 publications

Data-Driven Studies of Li-Ion-Battery Materials

Data-Driven Studies of Li-Ion-Battery Materials

Data-Driven Materials Discovery and Synthesis using Machine Learning Methods

Single Crystal Automated Refinement (SCAR): A Data-Driven Method for Solving Inorganic Structures

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