We demonstrate how the integration of machine learning (ML) and density functional theory (DFT) approaches accelerates the discovery of advanced intermetallic alloys for solid-state hydrogen storage applications. By developing data-distribution-imbalance-aware ML...
The layered lithium-ion cathode material LiNiO₂ (LNO) has attracted significant attention due to its high energy density, yet challenges related to structural stability and degradation during cycling have hindered its commercial adoption. Materials Chemistry...
Designing crystalline solids with specific functionalities is a challenge due to the complexity of predicting how molecules will assemble in the solid state. Traditional methods for discovering new functional materials rely heavily on trial and error, which is...
In this work, MCC researchers develop a quantum dynamical simulation approach revealing in atomistic detail how the charge carrier wavefunction moves along a temperature gradient in an organic molecular crystal resulting in thermoelectric charge transport....
In this work, MCC researchers present a machine learning method that enables molecular dynamics simulations under finite electric fields at length and time scales previously unattainable with traditional first-principles approaches. Using ARCHER2 they demonstrate its...
