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High pressure hydrogen by machine learning and quantum Monte Carlo
We have developed a technique combining the accuracy of quantum Monte Carlo in describing the electron correlation with the efficiency of a Machine Learning Potential (MLP).
High-$T_c$ superconducting hydrides formed by LaH$_{24}$ and YH$_{24}$ cage structures as basic blocks
Based on recent studies regarding high-temperature (high-$T_c$) La-Y ternary hydrides (e. g., $P{\bar{1}}$-La$_2$YH$_{12}$, $Pm{\bar{3}}m$-LaYH$_{12}$, and $Pm{\bar{3}}m$-(La, Y)H$_{10}$ with a maximum $T_c \sim 253$ K), we examined the phase and structural stabilities of the (LaH$_6$)(YH$_6$)$_y$ series as high-$T_c$ ternary hydride compositions using a genetic algorithm and $\it ab$ $\it initio$ calculations.
Superconductivity Computational Physics
