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The ALICE detector is dedicated to studying the properties of hot and dense matter created in heavy-ion collisions. Among the probes used to investigate these properties are high-momentum particles, which originate in hard-scatterings…
The discovery of novel carbon allotropes with tailored thermal and mechanical properties is critical for advanced thermal management. However, exploring the vast configurational space of carbon using \textit{ab initio} calculations remains…
Magnetic materials, both hard and soft, are used extensively in several components of particle accelerators. Magnetically soft iron-nickel alloys are used as shields for the vacuum chambers of accelerator injection and extraction septa;…
A simplified polycrystalline model (the so-called RL model) is proposed to simulate the anisotropic viscoplastic behavior of metallic materials. A generic method is presented that makes it possible to build a simplified anisotropic material…
Thermoelectric materials can generate clean energy by transforming waste heat into electricity. The effectiveness of thermoelectric materials is measured by the dimensionless figure of merit, ZT. The quest for high ZT materials has drawn…
When two-dimensional atomic layers of different materials are brought into close proximity to form van der Waals (vdW) heterostructures, interactions between adjacent layers significantly influence their physicochemical properties. These…
Building operations represent a significant percentage of the total primary energy consumed in most countries due to the proliferation of Heating, Ventilation and Air-Conditioning (HVAC) installations in response to the growing demand for…
ALICE is the CERN LHC experiment optimised for the study of the strongly interacting matter produced in heavy-ion collisions and devoted to the characterisation of the quark-gluon plasma. To achieve the physics program for LHC Run 3, a…
Development of process-structure-property relationships in materials science is an important and challenging frontier which promises improved materials and reduced time and cost in production. Refractory high entropy alloys (RHEAs) are a…
In studying solidification process by simulations on the atomic scale, the modeling of crystal nucleation or amorphisation requires the construction of interatomic interactions that are able to reproduce the properties of both the solid and…
A thermophysical model is developed that can predict the properties of two lignin mixtures, black liquor and lignosulfonates, up to 50% mass fractions, at hydrothermal conditions. An uncertainty quantification framework linked with classic…
The concept of alloying multiple principal elements at high concentrations has led to the development of High Entropy Alloys (HEAs) with exceptional mechanical properties, making them the focus of major recent scientific endeavors.…
Over the past decade inter-atomic potentials based on machine-learning (ML) techniques have become an indispensable tool in the atomic-scale modeling of materials. Trained on energies and forces obtained from electronic-structure…
The present study aims to detect helium-3 in nickel-based metal nano-composites doped with zirconia, which exhibited anomalous heat generation when exposed to hydrogen gas at approximately 450{\deg}C. Two complementary analytical techniques…
A new computational tool has been developed to model, discover, and optimize new alloys that simultaneously satisfy up to eleven physical criteria. An artificial neural network is trained from pre-existing materials data that enables the…
Superconductivity has been one of the focal points in medium and high-entropy alloys (MEAs-HEAs) since the first discovery of the HEA superconductor in 2014. Until now, most HEAs' superconducting transition temperature (Tc) has not exceeded…
Machine learning promises to accelerate the material discovery by enabling high-throughput prediction of desirable macro-properties from atomic-level descriptors or structures. However, the limited data available about precise values of…
Using the data on the superconducting critical temperature ($T_{C}$) for a number of metal hydrides, we found a rule that makes it possible to predict the maximum $T_{C}$ based only on the information about the electronic structure of metal…
Accurate atomic data and plasma models are essential for interpreting the upcoming high-quality spectra from missions like XRISM and Athena. Estimating physical quantities, like temperature, abundance, turbulence, and resonance scattering…
The development of new manufacturing techniques such as 3D printing have enabled the creation of previously infeasible chemical reactor designs. Systematically optimizing the highly parameterized geometries involved in these new classes of…