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20052022
most citedSuperconductivity to 262 kelvin via catalyzed hydrogenation of yttrium at high pressures

271 citations · 452 across the 11 of their papers we have counts for

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Showing cond-mat.mtrl-sciShow all

9 papers · 1 filter

cond-mat.mtrl-sci2021

Structural Motifs and Bonding in Two Families of Boron Structures Predicted at Megabar Pressures

Katerina P. Hilleke, Eva Zurek, Tadashi Ogitsu +1

The complex crystal chemistry of elemental boron has led to numerous proposed structures with distinctive motifs as well as contradictory findings. Herein, evolutionary structure s…

cond-mat.mtrl-sci20203 cited

The Li-F-H Ternary System at High Pressures

Tiange Bi, Andrew Shamp, Tyson Terpstra +2

Evolutionary crystal structure prediction searches have been employed to explore the ternary Li-F-H system at 300 GPa. Metastable phases were uncovered within the static lattice ap…

cond-mat.mtrl-sci202016 cited

Fluorides of silver under large compression

Dominik Kurzydłowski, Mariana Derzsi, Eva Zurek +1

The silver-fluorine phase diagram has been scrutinized as a function of external pressure using theoretical methods. Our results indicate that two novel stoichiometries containing…

cond-mat.mtrl-sci2019

Predicting Superhard Materials via a Machine Learning Informed Evolutionary Structure Search

Patrick Avery, Xiaoyu Wang, Davide M. Proserpio +5

Good agreement was found between experimental Vickers hardnesses, , of a wide range of materials and those calculated by three macroscopic hardness models that employ t…

cond-mat.mtrl-sci2017

The AFLOW Fleet for Materials Discovery

Cormac Toher, Corey Oses, David Hicks +48

The traditional paradigm for materials discovery has been recently expanded to incorporate substantial data driven research. With the intent to accelerate the development and the d…

cond-mat.mtrl-sci2017

AFLOW-ML: A RESTful API for machine-learning predictions of materials properties

Eric Gossett, Cormac Toher, Corey Oses +8

Machine learning approaches, enabled by the emergence of comprehensive databases of materials properties, are becoming a fruitful direction for materials analysis. As a result, a p…