arXiv subjects

Daniela Polino

Publications and source records attributed to Daniela Polino.

5 recordsLinked to original sources

arXiv2023

Innate Dynamics and Identity Crisis of a Metal Surface Unveiled by Machine Learning of Atomic Environments

Metals are traditionally considered hard matter. However, it is well known that their atomic lattices may become dynamic and undergo reconfigurations even well-below the melting temperature. The innate atomic dynamics of metals is directly related to their bulk and surface properties. Understanding their complex structural dynamics is thus important for many applications but is not easy. Here we report deep-potential molecular dynamics simulations allowing to resolve at atomic-resolution the complex dynamics of various types of copper (Cu) surfaces, used as an example, near the Hüttig ($\sim1/3$ of melting) temperature. The development of a deep neural network potential trained on DFT calculations provides a dynamically-accurate force field that we use to simulate large atomistic models of different Cu surface types. A combination of high-dimensional structural descriptors and unsupervised machine learning allows identifying and tracking all the atomic environments (AEs) emerging in the surfaces at finite temperatures. We can directly observe how AEs that are non-native in a specific (ideal) surface, but that are instead typical of other surface types, continuously emerge/disappear in that surface in relevant regimes in dynamic equilibrium with the native ones. Our analyses allow estimating the lifetime of all the AEs populating these Cu surfaces and to reconstruct their dynamic interconversions networks. This reveals the elusive identity of these metal surfaces, which preserve their identity only in part and in part transform into something else in relevant conditions. This also proposes a concept of "statistical identity" for metal surfaces, which is key for understanding their behaviors and properties.

cond-mat.mtrl-sci↗

arXiv2021

Using metadynamics to build neural network potentials for reactive events: the case of urea decomposition in water

The study of chemical reactions in aqueous media is very important for its implications in several fields of science, from biology to industrial processes. Modelling these reactions is however difficult when water directly participates in the reaction. Since it requires a fully quantum mechanical description of the system, $\textit{ab-initio}$ molecular dynamics is the ideal candidate to shed light on these processes. However, its scope is limited by a high computational cost. A popular alternative is to perform molecular dynamics simulations powered by machine learning potentials, trained on an extensive set of quantum mechanical calculations. Doing so reliably for reactive processes is difficult because it requires including very many intermediate and transition state configurations. In this study, we used an active learning procedure accelerated by enhanced sampling to harvest such structures and to build a neural-network potential to study the urea decomposition process in water. This allowed us to obtain the free energy profiles of this important reaction in a wide range of temperatures, to discover a number of novel metastable states and to improve the accuracy of the kinetic rates calculations. Furthermore, we found that the formation of the zwitterionic intermediate has the same probability of occurring via an acidic or a basic pathway, which could be the cause of the insensitivity of reaction rates to the pH solution.

physics.chem-ph↗

arXiv2020

How collective phenomena impact CO2 reactivity and speciation in different media

CO2 has attracted considerable attention in the recent years due to its role in the greenhouse effect and environmental management. While its reaction with water has been studied extensively, the same cannot be said for reactivity in supercritical CO2 phase, where the conjugate acid/base equilibria proceed through different mechanisms and activation barriers. In spite of the apparent simplicity of the CO2 + H2O reaction, the collective effect of different environments has drastic influence on the free energy profile. Enhanced sampling techniques and well-tailored collective variables provide a detailed picture of the enthalpic and entropic drivers underscoring the differences in the formation mechanism of carbonic acid in the gas, aqueous and supercritical CO2 phases.

physics.chem-ph↗

arXiv2019

Kinetics of aqueous media reactions via ab initio enhanced molecular dynamics: the case of urea decomposition

Aqueous solutions provide a medium for many important reactions in chemical synthesis, industrial processes, environmental chemistry, and biological functions. It is an accepted fact that aqueous solvents can be direct participants to the reaction process and not act only as simple passive dielectrics. Assisting water molecules and proton wires are thus essential for the efficiency of many reactions. Here we study the decomposition of urea into ammonia and isocyanic acid by means of enhanced ab initio molecular dynamics simulations. We highlight the role of the solvent molecules and their interactions with the reactants providing a proper description of the reaction mechanism and how the water hydrogen-bond network affects the reaction dynamics. Reaction free energy and rates have been calculated taking into account this important effect.

physics.chem-ph↗

arXiv2019

The onset of dehydrogenation in solid Ammonia Borane, an ab-initio metadynamics study

The discovery of effective hydrogen storage materials is fundamental for the progress of a clean energy economy. Ammonia borane ($\mathrm{H_3BNH_3}$) has attracted great interest as a promising candidate but the reaction path that leads from its solid phase to hydrogen release is not yet fully understood. To address the need for insights in the atomistic details of such a complex solid state process, in this work we use \textit{ab-initio} molecular dynamics and metadynamics to study the early stages of AB dehydrogenation. We show that the formation of ammonia diborane ($\mathrm{H_3NBH_2(}$$μ$$\mathrm{-H)BH_3}$) leads to the release of $\mathrm{NH_4^+}$, which in turn triggers an autocatalytic $\mathrm{H_2}$ production cycle. Our calculations provide a model for how complex solid state reactions can be theoretically investigated and rely upon the presence of multiple ammonia borane molecules, as substantiated by standard quantum-mechanical simulations on a cluster.

physics.chem-ph↗