Absolute standard hydrogen electrode potential and redox potentials of atoms and molecules: machine learning aided first principles calculations.

Constructing a self-consistent first-principles framework that accurately predicts the properties of electron transfer reactions through finite-temperature molecular dynamics simulations is a dream of theoretical electrochemists and physical chemists. Yet, predicting even the absolute standard hydrogen electrode potential, the most fundamental reference for electrode potentials, proves to be extremely challenging. Here, we show that a hybrid functional incorporating 25% exact exchange enables quantitative predictions when statistically accurate phase-space sampling is achieved thermodynamic integrations and thermodynamic perturbation theory calculations, utilizing machine-learned force fields and Δ-machine learning models.

Density-based long-range electrostatic descriptors for machine learning force fields.

This study presents a long-range descriptor for machine learning force fields that maintains translational and rotational symmetry, similar to short-range descriptors while being able to incorporate long-range electrostatic interactions. The proposed descriptor is based on an atomic density representation and is structurally similar to classical short-range atom-centered descriptors, making it straightforward to integrate into machine learning schemes. The effectiveness of our model is demonstrated through comparative analysis with the long-distance equivariant (LODE) [Grisafi and Ceriotti, J.

Density isobar of water and melting temperature of ice: Assessing common density functionals.

We investigate the density isobar of water and the melting temperature of ice using six different density functionals. Machine-learning potentials are employed to ensure computational affordability. Our findings reveal significant discrepancies between various base functionals.

Stoichiometric reconstruction of the AlO(0001) surface.

Macroscopic properties of materials stem from fundamental atomic-scale details, yet for insulators, resolving surface structures remains a challenge. We imaged the basal (0001) plane of α-aluminum oxide (α-AlO) using noncontact atomic force microscopy with an atomically defined tip apex. The surface formed a complex ([Formula: see text] × [Formula: see text])±9° reconstruction.

Derivative learning of tensorial quantities-Predicting finite temperature infrared spectra from first principles.

We develop a strategy that integrates machine learning and first-principles calculations to achieve technically accurate predictions of infrared spectra. In particular, the methodology allows one to predict infrared spectra for complex systems at finite temperatures. The method's effectiveness is demonstrated in challenging scenarios, such as the analysis of water and the organic-inorganic halide perovskite MAPbI3, where our results consistently align with experimental data.