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19962026
most citedA General-Purpose Machine Learning Framework for Predicting Properties of Inorganic Materials

1.8k citations · 6.6k across the 53 of their papers we have counts for

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Showing 2016Show all

5 papers · 1 filter

cond-mat.mtrl-sci2016★ 376 cited

Computational Investigation of Half-Heusler Compounds for Spintronics Applications

Jianhua Ma, Vinay I. Hegde, Kamaram Munira +6

We present first-principles density functional calculations of the electronic structure, magnetism, and structural stability of 378 half-Heusler compounds (with …

cond-mat.mtrl-sci2016

Prediction of superconducting iron-bismuth intermetallic compounds at high pressure

Maximilian Amsler, S. Shahab Naghavi, Chris Wolverton

The synthesis of materials in high-pressure experiments has recently attracted increasing attention, especially since the discovery of record breaking superconducting temperatures…

cond-mat.mtrl-sci2016★ 139 cited

Lithium transport through Lithium-ion battery cathode coatings

Shenzhen Xu, Ryan M. Jacobs, Ha M. Nguyen +4

The surface coating of cathodes using insulator films has proven to be a promising method for high-voltage cathode stabilization in Li-ion batteries. However, there is still substa…

cond-mat.mtrl-sci2016★ 1.8k cited

A General-Purpose Machine Learning Framework for Predicting Properties of Inorganic Materials

Logan Ward, Ankit Agrawal, Alok Choudhary +1

A very active area of materials research is to devise methods that use machine learning to automatically extract predictive models from existing materials data. While prior example…

cond-mat.mtrl-sci2016★ 247 cited

Ultralow Thermal Conductivity in Full-Heusler Semiconductors

Jiangang He, Maximilian Amsler, Yi Xia +6

Semiconducting half- and, to a lesser extent, full-Heusler compounds are promising thermoelectric materials due to their compelling electronic properties with large power factors.…