The widespread adoption of machine learning surrogate models has significantly improved the scale and complexity of systems and processes that can be explored accurately and efficiently using atomistic modeling. However, the inherently data-driven nature of machine learning models introduces uncertainties that must be quantified, understood, and effectively managed to ensure reliable predictions and conclusions. Building upon these premises, in this perspective, we first overview state-of-the-art uncertainty estimation methods, from Bayesian frameworks to ensembling techniques, and discuss their application in atomistic modeling. We then examine the interplay between model accuracy, uncertainty, training dataset composition, data acquisition strategies, model transferability, and robustness. In doing so, we synthesize insights from the existing literature and highlight areas of ongoing debate.

Heterostructuring nanocrystals into a modular metal-semiconductor configuration enables tunable and novel functionalities. Such combinations at the nanoscale equip hybrid structures with unique electronic, optical, and catalytic properties unobserved in single-phase materials. Here, we report the hot-injection synthesis of Pd-Cu3Pd13S6.65Te0.35 nanoheterostructures (NHCs) from PdCu nanoalloy seeds. First, the growth of Pd-rich chalcogenide nanocrystals was initiated over the preformed PdCu surface through simultaneous sulfidation and tellurization, followed by their transformation into Pd-Cu3Pd13S6.65Te0.35 NHCs. By strategically employing moderate-temperature annealing, we achieved the complete migration of Cu+ due to the higher reactivity of Cu in comparison to Pd at that temperature, establishing a novel mechanistic relationship between cation mobility and temperature. This strategy enables controlled semiconductor domain formation and targeted metal migration. The NHCs showed efficient and stable electrocatalytic hydrogen evolution with low Tafel values in acidic media, outperforming conventional nanoelectrocatalysts. Computational analysis identified the active sites responsible for the observed catalytic performance.

A current challenge in atomistic machine learning is that of efficiently predicting the response of the electron density under electric fields. We address this challenge with symmetry-adapted kernel functions that are specifically derived to account for the rotational symmetry of a three-dimensional vector field. We demonstrate the equivariance of the method on a set of rotated water molecules and show its high efficiency with respect to number of training configurations and features for liquid water and naphthalene crystals. We conclude showcasing applications for relaxed configurations of gold nanoparticles, reproducing the scaling law of the electronic polarizability with size, up to systems with more than 2000 atoms. By deriving a natural extension to equivariant learning models of the electron density, our method provides an accurate and inexpensive strategy to predict the electrostatic response of molecules and materials.