83 citations · 334 across the 11 of their papers we have counts for
12 papers
Vibrational properties of metastable polymorph structures by machine learning
Fleur Legrain, Ambroise van Roekeghem, Stefano Curtarolo +3
Despite vibrational properties being critical for the ab initio prediction of the finite temperature stability and transport properties of solids, their inclusion in ab initio mate…
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…
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…
Materials Screening for the Discovery of New Half-Heuslers: Machine Learning versus Ab Initio Methods
Fleur Legrain, Jesús Carrete, Ambroise van Roekeghem +2
Machine learning (ML) is increasingly becoming a helpful tool in the search for novel functional compounds. Here we use classification via random forests to predict the stability o…
How the Chemical Composition Alone Can Predict Vibrational Free Energies and Entropies of Solids
Fleur Legrain, Jesús Carrete, Ambroise van Roekeghem +2
Computing vibrational free energies () and entropies () has posed a long standing challenge to the high-throughput ab initio investigation of finite temperature p…
A First-Principles Comparative Study of Lithium, Sodium, and Magnesium Storage in Pure and Gallium-Doped Germanium: Competition between Interstitial and Substitutional Sites
Fleur Legrain, Sergei Manzhos
Thermodynamics and kinetics of Li, Na, and Mg storage in Ge is studied ab initio. The most stable configurations can consist of tetrahedral, substitutional, or a combination of the…
Understanding the difference in cohesive energies between alpha and beta tin in DFT calculations
Fleur Legrain, Sergei Manzhos
The transition temperature between the low-temperature alpha phase of tin to beta tin is close to the room temperature (Tab =13C), and the difference in cohesive energy of the two…
Comparison of alpha- and beta- tin for lithium, sodium, and magnesium storage: an ab initio study including phonon contributions
Fleur Legrain, Oleksandr I. Malyi, Clas Persson +1
We present a comparative ab initio study of Li, Na, and Mg storage in tin, including phononic effects and phase competition between alpha and beta Sn. Mg doping at low concentratio…
Amorphous carbon a promising material for sodium ion battery anodes: a first principles study
Fleur Legrain, Konstantinos Kotsis, Sergei Manzhos
We present a comparative ab initio computational study of sodium and lithium storage in amorphous (glassy) carbon (a-C) vs. graphite. Amorphous structures are obtained by fitting s…
Highly Accurate Local Pseudopotentials of Li, Na, and Mg for Orbital Free Density Functional Theory
Fleur Legrain, Sergei Manzhos
We present a method to make highly accurate pseudopotentials for use with orbital-free density functional theory (OF-DFT) with given exchange-correlation and kinetic energy functio…
Aluminum doping improves the energetics of lithium, sodium, and magnesium storage in silicon
Fleur Legrain, Sergei Manzhos
While Si is an effective insertion type anode for Li-ion batteries, crystalline Si has been shown to be unsuitable for Na and Mg storage due, in particular, to insufficient binding…
Comparative Computational Study of the Energetics of Li, Na, and Mg Storage in Amorphous and Crystalline Silicon
Fleur Legrain, Oleksandr I. Malyi, Sergei Manzhos
To assess the potential of amorphous Si (a-Si) as an anode for Li, Na, and Mg-ion batteries, the energetics of Li, Na, and Mg atoms in a-Si are computed from first-principles and c…