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Atomistic models are computational models that mimic the behaviour of a complex system by explicitly taking its smallest constituent parts into account. In materials science and chemistry, an atomistic model is a model of the collective behaviour of atoms in larger systems, such as molecules and crystals.
This study reveals that traditional manufacturing can create nonequilibrium short-range order in metallic alloys, offering an additional dimension for tailoring alloy properties beyond composition and microstructure.
Mixed-cation perovskites promise improved solar cell stability. Here, the authors reveal a morphotropic phase boundary in MA1−xFAxPbI3 that links structural dynamics to electronic behavior, offering new design routes for high-performance devices.
Existing Moiré materials are mostly van der Waals heterostructures. Here the authors show that hydrogen-bond adaptability allows spontaneous formation of twisted bilayer ice at magic angles in 2D confinement, establishing a new class of Moiré materials.
Long-range interactions are challenging for machine learning interatomic potentials (MLIPs). Here, authors show that, by just learning from energies and forces, MLIPs can accurately capture electrostatics and predict atomic charges.
Atomistic simulations are important for phase-change materials and devices. Here, the authors present fast and accurate machine-learned potentials, enabling full-cycle device-scale simulations and showcasing applications in studying memory and neuromorphic computing devices.
A recent study proposed ZeoBind, an AI-accelerated workflow enabling the discovery and experimental verification of hits within chemical spaces containing hundreds of millions of zeolites.
Predicting the macroscopic properties of molecular liquids from first principles is a major challenge owing to the disordered nature of liquids and the weak link between microscopic forces and thermodynamic observables. A new workflow called BAMBOO produces accurate and transferable machine learning interatomic potential simulations of liquid electrolytes.
Identifying promising synthesis targets and designing routes to their synthesis is a grand challenge in chemistry and materials science. Recent work employing machine learning in combination with traditional approaches is opening new ways to address this truly Herculean task.
An apparent quirk of mathematics draws on a symmetry and resolves the issue of how to determine the equilibrium shape of crystals of two-dimensional materials with asymmetric terminations.
