Templating strategies for 3D-structured thermally conductive composites: recent advances and thermal energy applications
Thermally conductive polymer nanocomposites are enticing candidates for not only thermal
managements in electronics but also functional components in emerging thermal energy …
managements in electronics but also functional components in emerging thermal energy …
Emerging trends in machine learning: a polymer perspective
In the last five years, there has been tremendous growth in machine learning and artificial
intelligence as applied to polymer science. Here, we highlight the unique challenges …
intelligence as applied to polymer science. Here, we highlight the unique challenges …
Artificial intelligence driven design of catalysts and materials for ring opening polymerization using a domain-specific language
Advances in machine learning (ML) and automated experimentation are poised to vastly
accelerate research in polymer science. Data representation is a critical aspect for enabling …
accelerate research in polymer science. Data representation is a critical aspect for enabling …
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 …
PolyNC: a natural and chemical language model for the prediction of unified polymer properties
H Qiu, L Liu, X Qiu, X Dai, X Ji, ZY Sun - Chemical Science, 2024 - pubs.rsc.org
Language models exhibit a profound aptitude for addressing multimodal and multidomain
challenges, a competency that eludes the majority of off-the-shelf machine learning models …
challenges, a competency that eludes the majority of off-the-shelf machine learning models …
Methods, progresses, and opportunities of materials informatics
C Li, K Zheng - InfoMat, 2023 - Wiley Online Library
As an implementation tool of data intensive scientific research methods, machine learning
(ML) can effectively shorten the research and development (R&D) cycle of new materials by …
(ML) can effectively shorten the research and development (R&D) cycle of new materials by …
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 …
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 …
Deep learning to reveal the distribution and diffusion of water molecules in fuel cell catalyst layers
Water management in the catalyst layers (CLs) of proton-exchange membrane fuel cells is
crucial for its commercialization and popularization. However, the high experimental or …
crucial for its commercialization and popularization. However, the high experimental or …