The successful characterization of high energy density (HED) phenomena in laboratories using pulsed power facilities and coherent light sources is possible only with numerical modeling for design, diagnostic development, and data interpretation. The persistence of electron correlation in HED matter is one of the greatest challenges for accurate numerical modeling and has hitherto impeded our...
While the high efficiency of Density Functional Theory (DFT) calculations has enabled many important materials science application over the past decades, modern scientific problems require accurate electronic structure data beyond the scales attainable with DFT. For instance, the modeling of materials at extreme conditions across multiple length and time scales, which is important for the...
The physical and chemical state of Earth’s core is challenging to match using only pure iron. An additional amount of light element(s) is required to account for its geophysical features (e.g., core density deficit), and among various candidates proposed in the Earth’s core [1], hydrogen attracts special attention: (i) It is the most abundant element in the universe and, due to pressure...
Improving the predictive capability of molecular properties in ab initio simulations is essential for advanced material discovery. Despite recent progress making use of machine learning, utilizing deep neural networks to improve quantum chemistry modelling remains severely limited by the scarcity and heterogeneity of appropriate experimental data. Here we show how training a neural network to...
