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 …
Data-centric learning from unlabeled graphs with diffusion model
Graph property prediction tasks are important and numerous. While each task offers a small
size of labeled examples, unlabeled graphs have been collected from various sources and …
size of labeled examples, unlabeled graphs have been collected from various sources and …
An improved model for performance predicting and optimization of wearable thermoelectric generators with radiative cooling
H Pan, D Zhao - Energy Conversion and Management, 2023 - Elsevier
Wearable thermoelectric generators (WTEGs) have shown great potential for harvesting low-
grade body heat. However, inappropriate design of cold side heat sinks leads to …
grade body heat. However, inappropriate design of cold side heat sinks leads to …
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 …
Advances in data‐assisted high‐throughput computations for material design
D Xu, Q Zhang, X Huo, Y Wang… - Materials Genome …, 2023 - Wiley Online Library
Extensive trial and error in the variable space is the main cause of low efficiency and high
cost in material development. The experimental tasks can be reduced significantly in the …
cost in material development. The experimental tasks can be reduced significantly in the …
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 …
Deep learning-based prediction and interpretability of physical phenomena for metaporous materials
In this paper, we propose a fast, accurate, and interpretable deep learning (DL)-based
method for predicting and interpreting the sound absorption characteristics of metaporous …
method for predicting and interpreting the sound absorption characteristics of metaporous …
Unlocking enhanced thermal conductivity in polymer blends through active learning
Polymers play an integral role in various applications, from everyday use to advanced
technologies. In the era of machine learning (ML), polymer informatics has become a vital …
technologies. In the era of machine learning (ML), polymer informatics has become a vital …
In situ enhancing thermal and mechanical properties of novel green WPAI nanocomposite membrane via artificially cultivated biomass-based diatom frustules
Y Shi, B Li, X Jiang, C Yu, T Li, H Sun, S Chen… - … Composites and Hybrid …, 2023 - Springer
Water-based polyamide-imide (WPAI) resin has received extensive attention due to its
greenness, safety, and favorable comprehensive properties. However, its low degree of …
greenness, safety, and favorable comprehensive properties. However, its low degree of …
Electrically regulated thermal conductivity of aramid polymer systems
Aramid polymers, renowned for their electronic insulation and thermal conductive properties,
are widely adopted as thermal management materials in power electronics. However, the …
are widely adopted as thermal management materials in power electronics. However, the …