New work on machine-learning interatomic potentials
A study in ACS Nano connects potential-based molecular dynamics with flexibility in solid–liquid nanofluidic friction.
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De novo inverse design of superhard C–N compounds
A global machine-learning potential and multiobjective optimization workflow explores new superhard compounds.
Discovery of 2D Invar and anti-Invar monolayers
Machine learning broadens the search for two-dimensional materials with unusual thermal expansion behaviour.
Crystal structure prediction with graph networks and optimization
A combined strategy brings learned structural representations and global optimization into one prediction workflow.
Kinetic pathway of the γ-to-δ phase transition in CsPbI₃
First-principles analysis follows a structural pathway relevant to the stability of perovskite materials.
Perovskite soft lattice and carrier lifetime study
A computational study links lattice softness and carrier dynamics in hybrid perovskites.
Symbolic regression for catalytic descriptor discovery
Data-driven symbolic regression is used to search for compact, interpretable physical descriptors.
Br-alloying and carrier dynamics in perovskite solar cells
The work examines how alloying changes the atomistic pathways that shape carrier behaviour.