Not Just Par for the Course: 73 Quaternary Germanides RE4M2XGe4 (RE = La–Nd, Sm, Gd–Tm, Lu; M = Mn–Ni; X = Ag, Cd) and the Search for Intermetallics with Low Thermal Conductivity

Zhang, Dong; Oliynyk, Anton O.; Duarte, Gabriel M.; Iyer, Abishek K.; Ghadbeigi, Leila; Kauwe, Steven K.; Sparks, Taylor D.; Mar, Arthur

doi:10.1021/acs.inorgchem.8b02279

Cited by 9 publications

(6 citation statements)

References 47 publications

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“…Other more successful experimental verification included the Heusler TiRu 2 Ga, whose preliminary results indicated that it had low thermal conductivity, and transition‐metal germanides Mn(Ru 0.4 Ge 0.6 ), whose thermal conductivity could be as low as 2 W m −1 K −1 . In addition, two quaternary rare‐earth germanides RE‐M‐M′‐Ge, i.e., Nd 4 Mn 2 InGe 4 and Nd 4 Mn 2 AgCe 4 , have also been synthesized and confirmed to have low thermal conductivities, in agreement with the engine prediction.…”

Section: Applicationmentioning

confidence: 83%

A Critical Review of Machine Learning of Energy Materials

Chen

Zuo

et al. 2020

Advanced Energy Materials

399

281

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Machine learning (ML) is rapidly revolutionizing many fields and is starting to change landscapes for physics and chemistry. With its ability to solve complex tasks autonomously, ML is being exploited as a radically new way to help find material correlations, understand materials chemistry, and accelerate the discovery of materials. Here, an in‐depth review of the application of ML to energy materials, including rechargeable alkali‐ion batteries, photovoltaics, catalysts, thermoelectrics, piezoelectrics, and superconductors, is presented. A conceptual framework is first provided for ML in materials science, with a broad overview of different ML techniques as well as best practices. This is followed by a critical discussion of how ML is applied in energy materials. This review is concluded with the perspectives on major challenges and opportunities in this exciting field.

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Section: Applicationmentioning

confidence: 83%

A Critical Review of Machine Learning of Energy Materials

Chen

Zuo

et al. 2020

Advanced Energy Materials

399

281

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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, ferroelectric materials, and novel thermoelectrics, − to make property predictions for heat capacity, , the band gap of crystalline solids, and elastic moduli, to optimize solar cells, to predict new phosphor materials, and to classify crystal structures of inorganic compounds. − These methods generate predictions for unknown examples based on statistical relationships and patterns discovered using reliable data, informative descriptions of those data, and machine learning algorithms. In this work, we use a machine learning approach to build predictive models for interatomic distances.…”

Section: Resultsmentioning

confidence: 95%

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

et al. 2019

Self Cite

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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 determination and refinement is not always straightforward. Methods for simplifying and rationalizing 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 determination. In this work, we propose a new method that uses singlecrystal data to determine 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 determine two newly synthesized and previously unreported phases, ZrAu 0.5 Os 0.5 and Nd 4 Mn 2 AuGe 4 . The structure solutions are found to be comparable with those produced by 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 thus verified as being a fast and reliable assistant in determining even complex single-crystal diffraction data for extended inorganic solids.

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“…Hirofumi Hazama,* Daisuke Murai, Naoyuki Nagasako, Masaki Hasegawa, and Nobuhiro Ogihara DOI: 10.1002/admt.202000254 understanding, can be constructed using machine learning techniques. While some of these machine learning techniques have been adopted, [1][2][3][4][5][6] most MI investigations are still conducted on the basis of computer simulations, as the simulations can intentionally change parameters without experimental errors. However, as it is difficult to obtain realistic data for complicated materials or systems from simulations, this should be supplemented with data collected from experiments.…”

Section: Optimization Of Materials Composition Of Li-intercalated Metamentioning

confidence: 99%

Optimization of Material Composition of Li‐Intercalated Metal–Organic Framework Electrodes Using a Combination of Experiments and Machine Learning of X‐Ray Diffraction Patterns

Hazama

Murai

Nagasako

et al. 2020

Adv Materials Technologies

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Although individual Li‐intercalated metal–organic frameworks (iMOFs) exhibit properties that make them suitable for use as electrodes in Li‐ion batteries, none of these materials have the ideal combination of electrochemical characteristics. Using the high specific capacity and low polarization of 2,6‐naphthalene dicarboxylate dilithium as a basis for improvement, 26 iMOFs from different combinations of aqueous terephthalic, 2,6‐naphthalene dicarboxylic, and 4,4′‐biphenyl dicarboxylic‐based solutions with Li source are synthesized to obtain a material with the optimal electrochemical performances. For a more comprehensive search of the optimal ratio of raw material solutions, a machine learning‐based prediction model is constructed, using a combination of X‐ray diffraction (XRD) patterns and the experimentally derived characteristics of the 26 iMOFs. With this model, the optimal iMOF ratio is found to be a 2,6‐naphthalene dicarboxylic acid‐rich ternary solution of raw material. It is concluded that the improvement in the electrochemical properties originates from a change in the iMOFs crystal structure caused by synthesis from three solutions. As the model is independent of fabrication parameters such as heating temperature, it can also be used for evaluation of synthesis procedures. Hence, the XRD data‐based machine learning method introduced in this study is a powerful tool for practical material development.

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Not Just Par for the Course: 73 Quaternary Germanides RE₄M₂XGe₄ (RE = La–Nd, Sm, Gd–Tm, Lu; M = Mn–Ni; X = Ag, Cd) and the Search for Intermetallics with Low Thermal Conductivity

Cited by 9 publications

References 47 publications

A Critical Review of Machine Learning of Energy Materials

A Critical Review of Machine Learning of Energy Materials

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

Optimization of Material Composition of Li‐Intercalated Metal–Organic Framework Electrodes Using a Combination of Experiments and Machine Learning of X‐Ray Diffraction Patterns

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