Exploring high-performance viscosity index improver polymers via high-throughput molecular dynamics and explainable AI
Abstract Data-driven material innovation has the potential to revolutionize the traditional Edisonian process and significantly shorten development cycles. However, the scarcity of data in materials science and the poor interpretability of machine learning pose serious obstacles to the adoption of t...
Saved in:
| Main Authors: | Rui Zhou, Luyao Bao, Weifeng Bu, Feng Zhou |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-03-01
|
| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-025-01539-z |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Advances in high‐throughput experiments of polymer crystallization for developing polymer processing
by: Bao Deng, et al.
Published: (2025-03-01) -
Monitoring of polymer viscosity by simultaneous ultrasonic and rheological measurements at high and varying temperatures
by: Nesrine Houhat, et al.
Published: (2025-03-01) -
Understanding Polymers Through Transfer Learning and Explainable AI
by: Luis A. Miccio
Published: (2024-11-01) -
High-throughput screening of CO2 cycloaddition MOF catalyst with an explainable machine learning model
by: Xuefeng Bai, et al.
Published: (2025-01-01) -
StreaMD: the toolkit for high-throughput molecular dynamics simulations
by: Aleksandra Ivanova, et al.
Published: (2024-11-01)