相关论文: Unlocking Thermoelectric Potential: A Machine Lear…
A deterministic multiscale toy model is studied in which a chaotic fast subsystem triggers rare transitions between slow regimes, akin to weather or climate regimes. Using homogenization techniques, a reduced stochastic parametrization…
Machine learning (ML) is widely used to explore crystal materials and predict their properties. However, the training is time-consuming for deep-learning models, and the regression process is a black box that is hard to interpret. Also, the…
The electronic and magnetic bulk properties of half-metallic Heusler alloys such as Co$_{2}$FeSi, Co$_{2}$FeAl, Co$_{2}$MnSi and Co$_{2}$MnAl are investigated by means of {\em ab initio} calculations in combination with Monte Carlo…
High-fidelity simulations are essential for predicting material behavior under high-velocity impact (HVI), but their accuracy depends on material models and parameters that are often calibrated by manual fitting to multiple costly…
We present a comprehensive first-principles investigation of optical, transport, and thermoelectric properties of pure and doped hexagonal Si$_x$Ge$_{1-x}$ alloys based on density-functional theory calculations, the Boltzmann transport…
Warm forming processes have been successfully applied at laboratory level to overcome some important drawbacks of the Al-Mg alloys, such as poor formability and large springback. However, the numerical simulation of these processes requires…
Real-world physical systems, like composite materials and porous media, exhibit complex heterogeneities and multiscale nature, posing significant computational challenges. Computational homogenization is useful for predicting macroscopic…
One considers linearly thermoelastic composite media, which consist of a homogeneous matrix containing a statistically homogeneous random set of ellipsoidal uncoated or coated inclusions. Effective properties (such as compliance and thermal…
The discovery of high-performance thermoelectric (TE) materials for advancing green energy harvesting from waste heat is an urgent need in the context of looming energy crisis and climate change. The rapid advancement of machine learning…
Machine learning technologies are expected to be great tools for scientific discoveries. In particular, materials development (which has brought a lot of innovation by finding new and better functional materials) is one of the most…
Disorder, though naturally present in experimental samples and strongly influencing a wide range of material phenomena, remains underexplored in first-principles studies due to the computational cost of sampling the large supercell and…
Through a combined first-principles and Boltzmann transport theory, we systematically investigate the thermal and electrical transport properties of the unexplored ternary quasi two-dimensional KMgSb system of KMgX (X = P, As, Sb, and Bi)…
Although crystalline solids are characterized by their periodic structures, some are only periodic on average and deviate on a local scale. Distinct local orderings can exist with identical periodic structures making their differences…
Half-Heusler compounds are known for their various compositions and multifunctional properties including topological phases. In this study, we investigate the topological classification of this class of materials based on the ordering of…
Resorbable magnesium (Mg) alloys are promising candidates for temporary medical devices due to their biodegradability and favorable mechanical properties. To accelerate the design of diluted Mg alloys for implants, we developed a…
Porosity has been identified as the key indicator of the durability properties of concrete exposed to aggressive environments. This paper applies ensemble learning to predict porosity of high-performance concrete containing supplementary…
Computational screening in heterogeneous catalysis relies increasingly on machine learning models for predicting key input parameters due to the high cost of computing these directly using first-principles methods. This becomes especially…
In this paper we discuss the use of linked thermal and electrical network models to predict the behaviour of a complex silicon detector system. We use the silicon strip detector for the ATLAS Phase-II upgrade to demonstrate the application…
Machine learning has revolutionized materials design, yet predicting complex properties like alloy ductility remains challenging due to the influence of processing conditions and microstructural features that resist quantification through…
Heusler alloys, renowned for their multifunctionality and capacity for vast elemental customization, are primarily classified into half-Heusler (XYZ) and full-Heusler (X2YZ) structural types. Typically, the 18-electron half-Heusler and the…