Talk 1 — Mechanism of water deprotonation at aqueous TiO2 interfaces

Giorgio Palermo, Alfredo Pasquarello
Chaire de simulation à l'échelle atomique - CSEA, EPFL

We study the process of deprotonation at the aqueous−TiO2 anatase (101) interface using machine learning driven molecular dynamics coupled with the on-the-fly probability enhanced sampling scheme (OPES). We train a machine learning potential at the hybrid functional level of theory in the presence of a positive charge. Through nanosecond-scale OPES simulations we reconstruct the free energy surface of the deprotonation reaction as a function of suitable collective variables and determine a free energy barrier for water deprotonation of 0.35 eV, in agreement with previous investigations. We verify that the deprotonation process leads to the hydroxilation of undercoordinated surface oxygen sites and find that the abstracted proton is lost to the liquid. Through the reconstruction of the free energy surface of multiple proton transfer channels, we determine that the most favored mechanism involves the transfer of a proton from an adsorbed molecule to the undercoordinated oxygen sites via the mediation of a solvent water molecule.

Talk 2 — Chemical site bases and average-atom potentials for the atomic cluster expansion

Lorenzo Piersante, Anirudh Raju Natarajan
Laboratoire de conception et simulation des matériaux - MADES, EPFL

Interatomic potentials are central tools in the atomistic modeling of materials. The atomic cluster expansion (ACE) parameterizes such potentials from ab initio data, conventionally encoding the chemical degrees of freedom with a one-hot representation that yields chemically stratified models. The alternative chemical representations used in on-lattice configurational cluster expansions have not been assessed for interatomic potentials. Here we revisit the multicomponent ACE for an arbitrary chemical site basis. We then establish an exact analytical mapping between a fitted linear ACE and the average-atom potential that describes a perfectly random alloy. We benchmark potentials built on the occupational, Chebyshev, and conventional ACE bases against solute binding and vacancy formation energies in Mg-Nd, and against the mixing enthalpies of the Mo-Nb and Cr-W solid solutions. When training data are scarce, the occupational basis converges fastest and offers the best control over targeted material properties, while the conventional and Chebyshev bases face challenges in reproducing these properties. The occupational basis likewise yields the most reliable average-atom description of disordered alloy thermodynamics. In the large-data limit the three bases perform identically. The chemical basis is therefore a design choice that governs data efficiency. Its explicit treatment opens a route to average-atom potentials for the thermodynamic, mechanical, and kinetic properties of concentrated alloys.