Materials under extreme pressure: combining theoretical and experimental techniques

Artificial Intelligence for Energy Conversion

“…Analyzing a single-atom catalyst (SAC) and utilizing Machine learning techniques to investigate the affinity and specificity of a single atom catalyst the proportional connections between the Gibbs free energy and the proportionality connections in between the (OOH) and carboxylic group illustrate the variations in G (O) and G (OOH) on the 31 studied SACs ML explains why SAC selectivity and functionality differ and how the development of a more effective and reliable SAC for hydrogen peroxide generation. As can be shown, ML may significantly aid in establishing the link between material structure and attributes [28]. The technique was utilized to pick distinctive variables and provided the catalytic efficiency prediction equation.…”

DFT-D3 study of two-dimensional polymerization of C60 crystal under high pressure

“…[23][24][25] This is also true for high-pressure studies which lead to novel materials different under ambient conditions. 26,27) Our previous studies reported that the volume (at ambient pressure) is the dominant factor in determining the high-pressure reaction in oxide systems. 28,29) This is because deformation does not play an important role in high-pressure reactions owing to the large bulk modulus of oxides.…”