arXiv subjects

Giulio Barletta

Publications and source records attributed to Giulio Barletta.

5 recordsLinked to original sources

arXiv2026

High-Throughput Bayesian Optimization of Cement-Salt Hydrates Composites for Seasonal Thermochemical Energy Storage

Thermochemical energy storage (TCES) based on salt hydrates is a promising route for seasonal heat storage; however, the design of practical sorbent materials remains challenging due to a non-trivial coupling between composition, synthesis feasibility, performance, and cost. Here, focusing on salt-into-matrix cement-based composites, we demonstrate that a high-throughput experimental framework based on Bayesian optimization (BO) can be used to orchestrate the optimization process of composite materials for low-temperature TCES. The explored design space is defined by salt type, salt concentration, water-to-cement ratio, and additive-to-cement ratio, while two competing objectives are pursued in parallel, namely the specific energy and the specific energy cost. The BO-guided campaign identified Pareto-optimal composites based on CaCl$_2$, Zn(NO$_3$)$_2$, and LiCl, highlighting the promise of cement-salt combinations that have been only marginally explored, or not previously reported, in cement-based TCES systems. The best-performing formulation (LiCl-based), achieved an average specific energy of about $\SI{458}{\kilo\joule\per\kilo\gram}$, whereas CaCl$_2$- and Zn(NO$_3$)$_2$-based composites showed lower but still competitive specific energy values combined with more favorable specific energy cost. Overall, the optimized formulations improved the specific energy of previously developed cement-based materials by up to a factor of five, although it remains below that of state-of-the-art composites based on silica gel and expanded vermiculite. Nonetheless, the present materials, notably CaCl$_2$- and Zn(NO$_3$)$_2$-based composites, offer an attractive cost-to-performance balance, highlighting BO as an effective strategy for accelerated TCES materials discovery.

cond-mat.mtrl-sci↗

arXiv2026

Quantifying Perovskite Solar Cell Degradation via Machine Learning from Spatially Resolved Multimodal Luminescence Time Series

Perovskite solar cells achieve remarkable power conversion efficiencies, yet operational stability remains a major barrier to large-scale deployment. Reliable and rapid assessment of device state of health is therefore essential. Conventional electrical diagnostics, such as illuminated current-voltage (J--V) sweeps, provide accurate performance metrics but are time-consuming and do not resolve spatially localized degradation, motivating non-invasive imaging-based alternatives. A deep-learning framework is introduced to estimate PSC efficiency retention, $R_\mathrm{PCE}=\mathrm{PCE}_t/\mathrm{PCE}_0$, directly from multimodal luminescence imaging acquired during device aging. Each sample combines electroluminescence (EL), open-circuit photoluminescence (PLoc), and short-circuit photoluminescence (PLsc) at an aged state with device-specific reference images at $t=0$, enabling learning of degradation-relevant spatial changes. LumPerNet, a compact convolutional neural network, is benchmarked against a spatially homogenized control in which each luminescence channel is replaced by its spatial average while retaining the same learning framework and leakage-aware protocol. The comparison indicates that global luminescence evolution contains most of the predictive signal, while spatial information provides a secondary contribution to robustness. These results establish spatially resolved luminescence imaging as a practical route for accelerated stability testing and non-invasive degradation monitoring in perovskite photovoltaics.

cond-mat.mtrl-sci↗

arXiv2025

Notes on Quantum Computing for Thermal Science

This document explores the potential of quantum computing in Thermal Science. Conceived as a living document, it will be continuously updated with experimental findings and insights for the research community in Thermal Science. By experiments, we refer both to the search for the most effective algorithms and to the performance of real quantum hardware. Those are fields that are evolving rapidly, driving a technological race to define the best architectures. The development of novel algorithms for engineering problems aims at harnessing the unique strengths of quantum computing. Expectations are high, as users seek concrete evidence of quantum supremacy - a true game changer for engineering applications. Among all heat transfer mechanisms (conduction, convection, radiation), we start with conduction as a paradigmatic test case in the field being characterized by a rich mathematical foundation for our investigations.

physics.comp-ph↗

arXiv2024

Energy-GNoME: A Living Database of Selected Materials for Energy Applications

Artificial Intelligence (AI) in materials science is driving significant advancements in the discovery of advanced materials for energy applications. The recent GNoME protocol identifies over 380,000 novel stable crystals. From this, we identify over 33,000 materials with potential as energy materials forming the Energy-GNoME database. Leveraging Machine Learning (ML) and Deep Learning (DL) tools, our protocol mitigates cross-domain data bias using feature spaces to identify potential candidates for thermoelectric materials, novel battery cathodes, and novel perovskites. Classifiers with both structural and compositional features identify domains of applicability, where we expect enhanced accuracy of the regressors. Such regressors are trained to predict key materials properties like, thermoelectric figure of merit (zT), band gap (Eg), and cathode voltage ($ΔV_c$). This method significantly narrows the pool of potential candidates, serving as an efficient guide for experimental and computational chemistry investigations and accelerating the discovery of materials suited for electricity generation, energy storage and conversion.

cond-mat.mtrl-sci↗

arXiv2024

Learning effective good variables from physical data

We assume that a sufficiently large database is available, where a physical property of interest and a number of associated ruling primitive variables or observables are stored. We introduce and test two machine learning approaches to discover possible groups or combinations of primitive variables: The first approach is based on regression models whereas the second on classification models. The variable group (here referred to as the new effective good variable) can be considered as successfully found, when the physical property of interest is characterized by the following effective invariant behaviour: In the first method, invariance of the group implies invariance of the property up to a given accuracy; in the other method, upon partition of the physical property values into two or more classes, invariance of the group implies invariance of the class. For the sake of illustration, the two methods are successfully applied to two popular empirical correlations describing the convective heat transfer phenomenon and to the Newton's law of universal gravitation.

physics.data-an↗