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AlN is an ultra-wide bandgap semiconductor which has been developed for applications including power electronics and optoelectronics. Thermal management of these applications is the key for stable device performance and allowing for long…

The fundamental quantity governing the mechanical and thermodynamic properties of a crystalline solid is its electronic charge density. Yet, its direct use for the rapid prediction of materials properties remains challenging due to its high…

Materials Science · Physics 2026-05-11 Kammampati Sai Kumar , Albert Linda , Shubham Kumar Maurya , Somnath Bhowmick

The lattice thermal conductivity of crystalline Si nanowires is calculated. The calculation uses complete phonon dispersions, and does not require any externally imposed frequency cutoffs. No adjustment to nanowire thermal conductivity…

Materials Science · Physics 2007-05-23 N. Mingo

Density functional theory (DFT) underpins modern atomistic simulations of transition-metal surfaces. It can predict key properties linked to catalytic performance, such as adsorption energies and barrier heights, enabling new paradigms in…

Materials Science · Physics 2026-03-23 Benjamin X. Shi , Timothy C. Berkelbach

Thermal and charge transport in the diffusive normal metal(DN) / insulator / $s$-, $d$- and p-wave superconductor junctions are studied for various situations, where we have used the Usadel equation with Nazarov's generalized boundary…

Superconductivity · Physics 2009-11-11 T. Yokoyama , Y. Tanaka , A. A. Golubov , Y. Asano

A fundamental understanding of phonon transport in stanene is crucial to predict the thermal performance in potential stanene-based devices. By combining first-principle calculation and phonon Boltzmann transport equation, we obtain the…

Materials Science · Physics 2016-02-23 Bo Peng , Hao Zhang , Hezhu Shao , Yuchen Xu , Xiangchao Zhang , Heyuan Zhu

Half-Heusler (HH) alloys are potential thermoelectric materials for use at elevated temperatures due to their high Seebeck coefficient and superior mechanical and thermal stability. However, their enhanced lattice thermal conductivity is…

Materials Science · Physics 2025-04-23 Rajeev Ranjan

We use a magnetothermal resistance method to measure lattice thermal conductivity of pure single crystal metals over a wide range of temperatures. Large transverse magnetic fields are applied to suppress electronic thermal conduction. The…

Materials Science · Physics 2017-10-25 Mengliang Yao , Mona Zebarjadi , Cyril P. Opeil

Using the atomic cluster expansion (ACE) framework, we develop a machine learning interatomic potential for fast and accurately modelling the phonon transport properties of wurtzite aluminum nitride. The predictive power of the ACE…

Materials Science · Physics 2024-02-23 Guang Yang , Yuan-Bin Liu , Lei Yang , Bing-Yang Cao

The lattice thermal conductivity ($\kappa$) of two ceramic materials, cerium dioxide (CeO$_2$) and magnesium oxide (MgO), is computed up to 1500 K using first principles and the phonon Boltzmann Transport Equation (PBTE) and compared to…

The lattice thermal conductivity (LTC) of Ga$_2$O$_3$ is an important property due to the challenge in the thermal management of high-power devices. We develop machine-learned neuroevolution potentials for single-crystalline…

Materials Science · Physics 2024-02-14 Xiaonan Wang , Jinfeng Yang , Penghua Ying , Zheyong Fan , Jin Zhang , Huarui Sun

We investigated the accelerated prediction of the thermal conductivity of materials through end- to-end structure-based approaches employing machine learning methods. Due to the non-availability of high-quality thermal conductivity data, we…

Materials Science · Physics 2023-11-07 Yagyank Srivastava , Ankit Jain

Heat management becomes more and more critical, especially in miniaturized modern devices, so the exploration of highly thermally conductive materials with electrical insulation and favorable mechanical properties is of great importance.…

In this study we used nonequilibrium simulation method to investigate the temperature dependent divergence of thermal conductivity in one dimensional momentum conserving system with asymmetric double well nearest-neighbor interaction…

Statistical Mechanics · Physics 2019-02-06 Archana G R , Debashis Barik

This study presents a deep learning approach to predicting structural and electronic properties of materials using Graph Neural Networks (GNNs). Leveraging data from the Materials Project database, we construct graph representations of…

Disordered Systems and Neural Networks · Physics 2024-12-20 Selva Chandrasekaran Selvaraj

The marriage of density functional theory (DFT) and deep learning methods has the potential to revolutionize modern computational materials science. Here we develop a deep neural network approach to represent DFT Hamiltonian (DeepH) of…

Materials Science · Physics 2023-01-02 He Li , Zun Wang , Nianlong Zou , Meng Ye , Runzhang Xu , Xiaoxun Gong , Wenhui Duan , Yong Xu

The lattice thermal conductivity ($\kappa_{\rm L} $) is a critical property of thermoelectrics, thermal barrier coating materials and semiconductors. While accurate empirical measurements of $\kappa_{\rm L} $ are extremely challenging, it…

Materials Science · Physics 2019-08-06 Lihua Chen , Huan Tran , Rohit Batra , Chiho Kim , Rampi Ramprasad

Accurate prediction of electron transport coefficients is crucial for understanding warm dense matter. Utilizing the density functional theory (DFT) with the Kubo-Greenwood formula is widely used to evaluate the electrical and thermal…

Materials Science · Physics 2024-02-27 Qianrui Liu , Mohan Chen

Large-scale simulations of plastic deformation and phase transformations in alloys require reliable classical interatomic potentials. We construct an embedded-atom method potential for niobium as the first step in alloy potential…

Materials Science · Physics 2010-04-27 Michael R. Fellinger , Hyoungki Park , John W. Wilkins

The electronic and transport properties of the half-Heusler compound LaPtSb are investigated by performing first-principles calculations combined with semi-classical Boltzmann theory and deformation potential theory. Compared with many…

Materials Science · Physics 2016-06-13 Q. Y. Xue , H. J. Liu , D. D. Fan , L. Cheng , B. Y. Zhao , J. Shi