activity
20062025
most citedExpanding the horizon of automated metamaterials discovery via quantum annealing

190 citations · 2.3k across the 53 of their papers we have counts for

collaborators

53 papers

cond-mat.mtrl-sci2025

ALATDYN: a set of Anharmonic LATtice DYNamics codes to compute thermodynamic and thermal transport properties of crystalline solids

Keivan Esfarjani, Harold Stokes, Safoura Nayeb Sadeghi +6

We introduce a lattice dynamics package which calculates elastic, thermodynamic and thermal transport properties of crystalline materials from data on their force and potential ene…

cond-mat.mes-hall2023

Electron-phonon coupling and non-equilibrium thermal conduction in ultrafast heating systems

Chuang Zhang, Rulei Guo, Meng Lian +1

The electron-phonon coupling in ultrafast heating systems is studied within the framework of Boltzmann transport equation (BTE) with coupled electron and phonon transport. A discre…

cond-mat.mtrl-sci2022★ 136 cited

RadonPy: Automated Physical Property Calculation using All-atom Classical Molecular Dynamics Simulations for Polymer Informatics

Yoshihiro Hayashi, Junichiro Shiomi, Junko Morikawa +1

The rapid growth of data-driven materials research has made it necessary to develop systematically designed, open databases of material properties. However, there are few open data…

cond-mat.mtrl-sci2021★ 33 cited

Phase-transition-induced giant Thomson effect for thermoelectric cooling

Rajkumar Modak, Masayuki Murata, Dazhi Hou +7

The Seebeck and Peltier effects have been widely studied and used in various thermoelectric technologies, including thermal energy harvesting and solid-state heat pumps. However, b…

cond-mat.mtrl-sci2021★ 2 cited

Descriptors of intrinsic hydrodynamic thermal transport: screening a phonon database in a machine learning approach

Pol Torres, Stephen Wu, Shenghong Ju +4

Machine learning techniques are used to explore the intrinsic origins of the hydrodynamic thermal transport and to find new materials interesting for science and engineering. The h…

cond-mat.mtrl-sci2021

Machine Learning-Assisted Exploration of Thermally Conductive Polymers Based on High-Throughput Molecular Dynamics Simulations

Ruimin Ma, Hanfeng Zhang, Jiaxin Xu +4

Finding amorphous polymers with higher thermal conductivity is important, as they are ubiquitous in heat transfer applications. With recent progress in material informatics, machin…