Accelerating the prediction of stable materials with machine learning
Despite the rise in computing power, the large space of possible combinations of elements
and crystal structure types makes large-scale high-throughput surveys of stable materials …
and crystal structure types makes large-scale high-throughput surveys of stable materials …
Phase stability through machine learning
R Arróyave - Journal of Phase Equilibria and Diffusion, 2022 - Springer
Understanding the phase stability of a chemical system constitutes the foundation of
materials science. Knowledge of the equilibrium state of a system under arbitrary …
materials science. Knowledge of the equilibrium state of a system under arbitrary …
Inverse design of metasurfaces with non-local interactions
Conventional metasurfaces have demonstrated efficient wavefront manipulation by using
thick and high-aspect-ratio nanostructures in order to eliminate interactions between …
thick and high-aspect-ratio nanostructures in order to eliminate interactions between …
Metastable materials discovery in the age of large-scale computation
Computational materials discovery has been successful in predicting novel, technologically
relevant materials. However, it has remained focused almost exclusively on finding ground …
relevant materials. However, it has remained focused almost exclusively on finding ground …
Machine learning of phase transitions in nonlinear polariton lattices
D Zvyagintseva, H Sigurdsson, VK Kozin… - Communications …, 2022 - nature.com
Polaritonic lattices offer a unique testbed for studying nonlinear driven-dissipative physics.
They show qualitative changes of their steady state as a function of system parameters …
They show qualitative changes of their steady state as a function of system parameters …