Publications by Federico Grasselli

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The effects of the finite size of the simulation box in equilibrium molecular dynamics simulations are investigated for prototypical superionic conductors of different types, namely, the fluorite-structure materials PbF2, CaF2, and UO2 (type II), and the a ...

Thouless' quantization of adiabatic particle transport permits one to associate an integer topological charge with each atom of an electronically gapped material. If these charges are additive and independent of atomic positions, they provide a rigorous de ...

The impact of the inner structure and thermal history of planets on their observable features, such as luminosity or magnetic field, crucially depends on the poorly known heat and charge transport properties of their internal layers. The thermal and electr ...

Despite governing heat management in any realistic device, the microscopic mechanisms of heat transport in all-solid-state electrolytes are poorly known: existing calculations, all based on simplistic semi-empirical models, are unreliable for superionic co ...

Machine-learning models have emerged as a very effective strategy to sidestep time-consuming electronic-structure calculations, enabling accurate simulations of greater size, time scale, and complexity. Given the interpolative nature of these models, the r ...

The electrostatic screening properties of ionic fluids are of paramount importance in countless physical processes. Yet the screening behavior of ionic conductors out of thermal equilibrium has to date mainly been studied in the context of thermoelectric p ...

The electronic density of states (DOS) provides information regarding the distribution of electronic energy levels in a material, and can be used to approximate its optical and electronic properties and therefore guide computational materials design. Given ...

Recent theoretical advances, based on a combination of concepts from Thouless' topological theory of adiabatic charge transport and a newly introduced gauge-invariance principle for transport coefficients, have permitted to connect (and reconcile) Faraday' ...

Machine learning (ML) models for molecules and materials commonly rely on a decomposition of the global target quantity into local, atom-centered contributions. This approach is convenient from a computational perspective, enabling large-scale ML-driven si ...

The dynamics of (few) electrons dissolved in an ionic fluid-as when a small amount of metal is added to a solution while upholding its electronic insulation-manifests interesting properties that can be ascribed to nontrivial topological features of particl ...

The vast amount of computational studies on electrical conduction in solid-state electrolytes is not mirrored by comparable efforts addressing thermal conduction, which has been scarcely investigated despite its relevance to thermal management and (over)he ...

Quantifying the uncertainty of regression models is essential to ensure their reliability, particularly since their application often extends beyond their training domain. Based on the solution of a constrained optimization problem, this work proposes ‘pre ...

Machine-learning potentials are usually trained on the ground-state, Born-Oppenheimer energy surface, which depends exclusively on the atomic positions and not on the simulation temperature. This disregards the effect of thermally excited electrons, that i ...

In this work, we elaborate on two recently discovered invariance principles, according to which transport coefficients are, to a large extent, independent of the microscopic definition of the densities and currents of the conserved quantities being transpo ...

Lithium ortho-thiophosphate (Li3PS4) has emerged as a promising candidate for solid-state electrolyte batteries, thanks to its highly conductive phases, cheap components, and large electrochemical stability range. Nonetheless, the microscopic mechanisms of ...

The widespread application of machine learning (ML) to the chemical sciences is making it very important to understand how the ML models learn to correlate chemical structures with their properties, and what can be done to improve the training efficiency w ...

Federico Grasselli

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Investigating finite-size effects in molecular dynamics simulations of ion diffusion, heat transport, and thermal motion in superionic materials

Oxidation States, Thouless' Pumps, and Nontrivial Ionic Transport in Nonstoichiometric Electrolytes

Heat and charge transport in H2O at ice-giant conditions from ab initio molecular dynamics simulations

Temperature- and vacancy-concentration-dependence of heat transport in Li3ClO from multi-method numerical simulations

Uncertainty estimation for molecular dynamics and sampling

Effect of a temperature gradient on the screening properties of ionic fluids

Adaptive energy reference for machine-learning models of the electronic density of states

Topology, Oxidation States, and Charge Transport in Ionic Conductors

Robustness of Local Predictions in Atomistic Machine Learning Models

Self-interaction and transport of solvated electrons in molten salts

Thermal conductivity of Li 3 PS 4 solid electrolytes with ab initio accuracy

A prediction rigidity formalism for low-cost uncertainties in trained neural networks

Predicting hot-electron free energies from ground-state data

Invariance principles in the theory and computation of transport coefficients

Mechanism of Charge Transport in Lithium Thiophosphate

Prediction rigidities for data-driven chemistry