arXiv subjects

Giulia Sormani

Publications and source records attributed to Giulia Sormani.

3 recordsLinked to original sources

arXiv2026

Beyond the Virial Expansion: Microscopic Origins of Partial Molar Volumes in LiCl Solutions

Although electrolyte density measurements have been reported for over a century, employing them to obtain accurate partial molar volume (PMV) profiles as a function of salt concentration has remained elusive. Obtaining such curves requires precise density measurements combined with a proper treatment of the associated virial expansion. In this work, we obtain PMV profiles for aqueous LiCl solutions. The resulting data enable the development of highly accurate force fields for Li$^+$ and Cl$^-$ ions, revealing a clear progression from isolated ions to ion pairs and ultimately to higher-order chain and ring structures. Because ion clustering emerges from complex, nonlocal interactions, it cannot be easily mapped onto specific virial terms. Instead, a direct structural and volumetric interpretation can be achieved by partitioning molecular dynamic (MD) simulation snapshots into three-dimensional polyhedral regions associated with individual salt ions and water molecules. The corresponding ionic and water volumes from this treatment quantitatively reproduce the experimental PMV curve. The results demonstrate that the PMV for salt increases (while that of water decreases) up to 6.7 M. Above this concentration, the direction reverses as three- and four-body interactions become prominent. Complementary multivariate curve resolution (MCR) Raman spectroscopy and density functional theory (DFT) calculations elucidate the molecular-level details of water electrostriction, which also persists up to 6.7 M. Significantly, the PMV data can be correlated with key thermodynamic properties, including the osmotic coefficient and the eutectic point. The procedures established here provide a general framework for modeling electrolyte solutions and enable the development of a new generation of accurate force fields for aqueous ions.

physics.chem-ph↗

arXiv2025

Opportunities and Challenges in Unsupervised Learning: The Case of Aqueous Electrolyte Solutions

Machine learning has emerged as a powerful tool in atomistic simulations, enabling the identification of complex patterns in molecular systems limiting human intervention and bias. However, the practical implementation of these methods presents significant technical challenges, particularly in the selection of hyperparameters and in the physical interpretability of machine-learned descriptors. In this work, we systematically investigate these challenges by applying an unsupervised learning protocol to a fundamental problem in physical chemistry namely, how ions perturb the local structure of water. Using the Smooth Overlap of Atomic Positions(SOAP) descriptors, we demonstrate how the intrinsic dimension (ID) serves as a guide for selecting hyperparameters and interpreting structural complexity. Furthermore, we construct a high-dimensional free energy landscape encompassing all water environments surrounding different ions. This analysis reveals how the physical properties of ions are intricately reflected in their hydration shells, shaping the landscape through specific connections between different minima. Our findings highlight the difficulty in balancing algorithmic automation with the need of employing both physical and chemical intuition, particularly for the construction of meaningful descriptors and for the interpretation of final results. By critically assessing the methodological hurdles associated with unsupervised learning, we provide a road map for researchers looking to harness these techniques for studying electrolyte and aqueous solutions in general.

physics.chem-ph↗

arXiv2024

Aqueous Solution Chemistry In Silico and the Role of Data Driven Approaches

The use of computer simulations to study the properties of aqueous systems is, today more than ever, an active area of research. In this context, during the last decade there has been a tremendous growth in the use of data-driven approaches to develop more accurate potentials for water as well as to characterize its complexity in chemical and biological contexts. We highlight the progress, giving a historical context, on the path to the development of many-body and reactive potentials to model aqueous chemistry, including the role of machine learning strategies. We focus specifically on conceptual and methodological challenges along the way in performing simulations that seek to tackle problems in modeling the chemistry of aqueous solutions. In conclusion, we summarize our perspectives on the use and integration of advanced data-science techniques to provide chemical insights in physical chemistry and how this will influence computer simulations of aqueous systems in the future.

physics.chem-ph↗