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Predictive models of thermodynamic properties of mixtures are paramount in chemical engineering and chemistry. Classical thermodynamic models are successful in generalizing over (continuous) conditions like temperature and concentration. On…

In this work, we first perform a systematic search for high-efficiency three-dimensional (3D) and two-dimensional (2D) thermoelectric materials by combining semiclassical transport techniques with density functional theory (DFT)…

Materials Science · Physics 2020-10-28 Kamal Choudhary , Kevin Garrity , Francesca Tavazza

Half Heusler (HH) alloys with 18 valence electron count have attracted significant interest in the area of research related to thermoelectrics. Understanding the novel transport properties exhibited by these systems with semiconducting…

Materials Science · Physics 2022-02-15 Kavita Yadav , Saurabh Singh , Tsunehiro Takeuchi , K. Mukherjee

Calculating thermodynamic potentials and observables efficiently and accurately is key for the application of statistical mechanics simulations to materials science. However, naive Monte Carlo approaches, on which such calculations are…

Statistical Mechanics · Physics 2021-07-15 James Damewood , Daniel Schwalbe-Koda , Rafael Gomez-Bombarelli

This study introduces a language transformer-based machine learning model to predict key mechanical properties of high-entropy alloys (HEAs), addressing the challenges due to their complex, multi-principal element compositions and limited…

Computational Engineering, Finance, and Science · Computer Science 2024-11-08 Spyros Kamnis , Konstantinos Delibasis

Stacking fault energy (SFE) is of the most critical microstructure attribute for controlling the deformation mechanism and optimizing mechanical properties of austenitic steels, while there are no accurate and straightforward computational…

Materials Science · Physics 2020-09-15 Xin Wang , Wei Xiong

We investigated six heavy element bismuth-based 18-VEC half-Heusler alloys CoTiBi, CoZrBi, CoHfBi, FeVBi, FeNbBi, and FeTaBi by first principles approach, in search of better thermoelectric prospects. The motivation is driven by expected…

Materials Science · Physics 2019-04-05 Sapna Singh , Mohd Zeeshan , Jeroen van den Brink , Hem C. Kandpal

Wildfires present intricate challenges for prediction, necessitating the use of sophisticated machine learning techniques for effective modeling\cite{jain2020review}. In our research, we conducted a thorough assessment of various machine…

Machine Learning · Computer Science 2024-04-03 Di Fan , Ayan Biswas , James Paul Ahrens

Half-Heusler (HH) alloys have attracted considerable interest as promising thermoelectric (TE) materials in the temperature range around 700 K and above, which is close to the temperature range of most industrial waste heat sources. The…

Materials Science · Physics 2013-07-09 Wenjie Xie , Anke Weidenkaff , Xinfeng Tang , Qingjie Zhang , Joseph Poon , Terry M. Tritt

Half-Heusler compounds (space group Fm3m) has garnered increasing attention in recent years in the thermoelectric community. Three decades ago, refractory RNiSn half-Heusler compounds (R represents refractory metals such as Hf, Zr, Ti) were…

Materials Science · Physics 2020-01-08 S. Joseph Poon

Accurate knowledge of temperatures in power semiconductor modules is crucial for proper thermal management of such devices. Precise prediction of temperatures allows to operate the system at the physical limit of the device avoiding…

Signal Processing · Electrical Eng. & Systems 2020-06-15 Jakub Ševčík , Václav Šmídl , Ondřej Straka

Ternary half Heusler alloys are under intense investigations recently towards achieving high thermoelectric figure-of-merit (ZT). Of particular interest is the ZrNiPb based half Heusler (HH) alloy where an optimal value of ZT = 0.7 at 773 K…

Materials Science · Physics 2022-08-30 Amardeep Sagar , Aman Bhardwaj , Andrei Novitskii , Vladimir Khovaylo , Satyabrata Patnaik

Half-Heusler (HH) phases (space group F43m, Clb) are increasingly gaining attention as promising thermoelectric materials in view of their thermal stability, scalability, and environmental benignity as well as efficient power output. Until…

Materials Science · Physics 2016-08-24 L. Chen , X. Zeng , T. M. Tritt , S. J. Poon

Predicting and interpreting thermal performance under oscillating flow in porous structures remains a critical challenge due to the complex coupling between fluid dynamics and geometric features. This study introduces a data-driven…

Fluid Dynamics · Physics 2025-09-16 Lichang Zhu , Laura Schaefer , Leitao Chen , Ben Xu

The thermoelectric properties of n type semiconductor, TiNiSn is optimized by partial substitution with metallic, MnNiSb in the half Heusler structure. Herein, we study the transport properties and intrinsic phase separation in the system.…

Disordered Systems and Neural Networks · Physics 2016-12-06 T. Berry , S. Ouardi , G. H. Fecher , B. Balke , G. Kreiner , G. Auffermann , W. Schnelle , C. Felser

Heusler compounds have emerged as important thermoelectric materials due to their combination of promising electronic transport properties, mechanical robustness and chemical stability -- key aspects for practical device integration. While…

While thermoelectric material performances can be estimated using the ZT, predicting the performance of thermoelectric generator modules (TGMs) is complex due to the non-linearity and non-locality of the thermoelectric differential…

Materials Science · Physics 2025-05-19 Byungki Ryu , Jaywan Chung , SuDong Park

We obtained the analytical expression for the effective thermoelectric properties and dimensionless figure of merit of a composite with interfacial electrical and thermal resistances using a micromechanics-based homogenisation. For the…

Materials Science · Physics 2018-12-11 Jiyoung Jung , Sangryun Lee , Byungki Ryu , Seunghwa Ryu

High-temperature alloy design requires a concurrent consideration of multiple mechanisms at different length scales. We propose a workflow that couples highly relevant physics into machine learning (ML) to predict properties of complex…

Materials Science · Physics 2020-09-04 Jian Peng , Yukinori Yamamoto , Jeffrey A. Hawk , Edgar Lara-Curzio , Dongwon Shin

We analyze closed one-dimensional chains of weakly coupled many level systems, by means of the so-called Hilbert space average method (HAM). Subject to some concrete conditions on the Hamiltonian of the system, our theory predicts energy…

Statistical Mechanics · Physics 2007-05-23 Mathias Michel , Jochen Gemmer , Guenter Mahler