Molecular dynamics articles from across Nature Portfolio

While clusters in calcium orthophosphate nucleation have long been known, their speciation and mechanistic pathways to hydroxyapatite remain debated. Here the authors report a revision of ion association in the calcium phosphate system and explore the consequences thereof on the early stages of phase separation.

Using machine learning methods to model interatomic potentials enables molecular dynamics simulations with ab initio level accuracy at a relatively low computational cost, but requires a large number of labelled training data obtained through expensive ab initio computations. Cui and colleagues propose a geometric learning framework that leverages self-supervised learning pretraining to enhance existing machine learning based interatomic potential models at a negligible additional computational cost.

Oxide-derived copper is well-known as a CO₂ reduction electrocatalyst, yet the mechanism of its formation and the structure of the active phase remain unclear. Here the reduction of oxide-derived copper is modelled using large-scale molecular dynamics with a neural network potential, providing important insights into the removal of trapped oxygen under operating conditions.

Contact electrification is a widely observed phenomenon in nature and in materials. Here, the authors use molecular dynamics simulations to show the importance of thermodynamic driving forces in contact electrification in insulating materials.

Solvated electrons can be formed through photo-induced charge-transfer-to-solvent electronic states of halide ions in water. Here, the authors use machine learning accelerated molecular dynamics simulations to follow the evolution of these states for aqueous iodide in detail.

Atomistic simulations have a broad range of applications from drug design to materials discovery. Machine learning interatomic potentials (MLIPs) have become an efficient alternative to computationally expensive ab initio simulations. Now a general reactive MLIP (called ANI-1xnr) has been developed and validated against a broad range of condensed-phase reactive systems.

A biasing potential is derived from the uncertainty of a neural network ensemble and used to modify the potential energy surface in molecular dynamics simulations and facilitate the determination of underrepresented structural regions.

Nonadiabatic molecular dynamics is the method of choice for modeling a wide range of excited-state phenomena. Although much progress has been made in improving the usability and efficiency of ground-state calculations, there are still challenges in translating this advance to the excited state.

Dr Arieh Warshel, distinguished professor of chemistry at the University of Southern California and 2013 Nobel laureate in chemistry, discusses with Nature Computational Science past and current research, his Nobel Prize, and the benefits and challenges of using computational modeling in his work.