Uncovering the effects of interface-induced ordering of liquid on crystal growth using machine learning

R Freitas, EJ Reed - Nature communications, 2020 - nature.com
The process of crystallization is often understood in terms of the fundamental microstructural
elements of the crystallite being formed, such as surface orientation or the presence of …

From electrons to phase diagrams with machine learning potentials using pyiron based automated workflows

S Menon, Y Lysogorskiy, ALM Knoll… - npj Computational …, 2024 - nature.com
We present a comprehensive and user-friendly framework built upon the pyiron integrated
development environment (IDE), enabling researchers to perform the entire Machine …

Free energies of iron phases at high pressure and temperature: Molecular dynamics study

AB Belonoshko, J Fu, G Smirnov - Physical Review B, 2021 - APS
The crystal structure of iron, the major component of the Earth's inner core (IC), is unknown
under the IC high pressure (P)(3.3–3.6 Mbar) and temperature (T)(5000–7000 K) …

Nonequilibrium free-energy calculations of fluids using LAMMPS

RP Leite, M de Koning - Computational Materials Science, 2019 - Elsevier
We present a guide to compute the absolute free energies of classical fluids using
nonequilibrium free-energy techniques within the LAMMPS (Large-scale Atomic/Molecular …

Automated free-energy calculation from atomistic simulations

S Menon, Y Lysogorskiy, J Rogal, R Drautz - Physical Review Materials, 2021 - APS
We devise automated workflows for the calculation of Helmholtz and Gibbs free energies
and their temperature and pressure dependence and provide the corresponding …

Calculation of Melting Temperature Using Nonequilibrium Thermodynamic Integration Methods

CT Nguyen, DT Ho, VH Ho… - Advanced Theory and …, 2024 - Wiley Online Library
Melting temperature is a fundamental material property and is defined as the temperature at
which the solid and liquid phases have the same free energy. However, there is no …

Viewing high entropy alloys through glasses: Linkages between solid solution and glass phases in multicomponent alloys

R Alvarez-Donado, S Papanikolaou… - Physical Review …, 2023 - APS
High entropy alloys (HEAs) represent highly promising multicomponent crystals that form
concentrated solid solutions (CSSs) and may violate traditional thermodynamic rules of …

Efficient mapping of phase diagrams with conditional Boltzmann Generators

M Schebek, M Invernizzi, F Noé… - … Learning: Science and …, 2024 - iopscience.iop.org
The accurate prediction of phase diagrams is of central importance for both the fundamental
understanding of materials as well as for technological applications in material sciences …

From electrons to phase diagrams with classical and machine learning potentials: automated workflows for materials science with pyiron

S Menon, Y Lysogorskiy, ALM Knoll… - arXiv preprint arXiv …, 2024 - arxiv.org
We present a comprehensive and user-friendly framework built upon the pyiron integrated
development environment (IDE), enabling researchers to perform the entire Machine …

[HTML][HTML] Solid-liquid phase boundary of oxide solid solutions using neural network potentials

K Hyodo, K Hongo, T Ichibha, R Maezono - Journal of Alloys and …, 2024 - Elsevier
We propose a cost-effective computational approach for predicting the phase boundary of
oxide solid solutions, ie, melting point, by identifying the point where the free energies of the …