Computational Materials Science: Electronic Structure, Phase Transitions and Machine Learning Potentials
This community develops the theoretical frameworks and computational tools used to predict how solid materials behave under extreme conditions, focusing on their electronic, magnetic, and thermal properties.
The core work involves first-principles calculations, density functional theory, and molecular dynamics simulations to model crystal structures and phase transitions. A significant portion of the research applies machine learning and neural networks to create interatomic potentials, enabling faster simulations of complex systems. Specific material systems frequently studied include boron clusters, transition metal oxides, and rare-earth compounds, with a strong emphasis on superconductivity, thermal expansion, and high-pressure physics.
The largest share of the community's output is found in beryllium research, accounting for 4.4% of all beryllium literature, while boron contributes the highest absolute number of papers with 1,272 entries.
The community comprises 26,144 papers, published primarily in Physical Review B, Physical Review Letters, and Journal of Applied Physics.
Recent work continues to focus on developing universal machine learning interatomic potentials and exploring high-temperature superconductivity in nickelates, alongside studies on altermagnetic materials and hydrogen storage mechanisms.