RadonPy: automated physical property calculation using all-atom classical molecular dynamics simulations for polymer informatics
The spread of data-driven materials research has increased the need for systematically
designed materials property databases. However, the development of polymer databases …
designed materials property databases. However, the development of polymer databases …
Machine learning-assisted exploration of thermally conductive polymers based on high-throughput molecular dynamics simulations
Finding amorphous polymers with higher thermal conductivity is important, as they are
ubiquitous in a wide range of applications where heat transfer is important. With recent …
ubiquitous in a wide range of applications where heat transfer is important. With recent …
High-throughput screening of amorphous polymers with high intrinsic thermal conductivity via automated physical feature engineering
The informatics algorithm-driven approach overcomes the high-cost and time-consuming
drawbacks of conventional trial-and-error procedures and enables efficient exploration of …
drawbacks of conventional trial-and-error procedures and enables efficient exploration of …
High-throughput molecular dynamics simulations and validation of thermophysical properties of polymers for various applications
Recent advances in graphics processing unit (GPU) hardware and improved efficiencies of
atomistic simulation programs allow for the screening of a large number of polymers to …
atomistic simulation programs allow for the screening of a large number of polymers to …
A computational structure–property relationship study of glass transition temperatures for a diverse set of polymers
M Chen, F Jabeen, B Rasulev… - Journal of Polymer …, 2018 - Wiley Online Library
The glass transition temperature (Tg) is one of the most important properties affecting the
stability of a polymeric material. A cheminformatics‐based approach has been employed to …
stability of a polymeric material. A cheminformatics‐based approach has been employed to …
Exploring high thermal conductivity polymers via interpretable machine learning with physical descriptors
The efficient and economical exploitation of polymers with high thermal conductivity (TC) is
essential to solve the issue of heat dissipation in organic devices. Currently, the …
essential to solve the issue of heat dissipation in organic devices. Currently, the …
Understanding the thermal properties of amorphous solids using machine-learning-based interatomic potentials
GC Sosso, VL Deringer, SR Elliott… - Molecular Simulation, 2018 - Taylor & Francis
Understanding the thermal properties of disordered systems is of fundamental importance
for condensed matter physics-and for practical applications as well. While quantities such as …
for condensed matter physics-and for practical applications as well. While quantities such as …
Simple theoretical model for thermal conductivity of crystalline polymers
The thermal conductivity of crystalline polymers is higher than their amorphous counterparts
yet still spans three orders of magnitude. In this study, by quantifying intrinsic properties …
yet still spans three orders of magnitude. In this study, by quantifying intrinsic properties …
Exploring high thermal conductivity amorphous polymers using reinforcement learning
Developing amorphous polymers with desirable thermal conductivity has significant
implications, as they are ubiquitous in applications where thermal transport is critical …
implications, as they are ubiquitous in applications where thermal transport is critical …
Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis
We present the Python Materials Genomics (pymatgen) library, a robust, open-source
Python library for materials analysis. A key enabler in high-throughput computational …
Python library for materials analysis. A key enabler in high-throughput computational …