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Machine learning in soft matter: from simulations to experiments
K Zhang, X Gong, Y Jiang - Advanced Functional Materials, 2024 - Wiley Online Library
Soft matter with diverse functionalities that are easily designable has fascinated tremendous
research interests in the past several decades. Nevertheless, the inherent confluence of time …
research interests in the past several decades. Nevertheless, the inherent confluence of time …
Machine Learning Approaches in Polymer Science: Progress and Fundamental for a New Paradigm
Machine learning (ML), material genome, and big data approaches are highly overlapped in
their strategies, algorithms, and models. They can target various definitions, distributions …
their strategies, algorithms, and models. They can target various definitions, distributions …
Using active learning for the computational design of polymer molecular weight distributions
The design of the reaction conditions is essential for controlling polymerization to synthesize
polymers with desired properties. However, the experimental screening of the reaction …
polymers with desired properties. However, the experimental screening of the reaction …
Ensemble transfer learning assisted soft sensor for distributed output inference in chemical processes
J Zhu, W Zhu, Y Liu - Computers & Chemical Engineering, 2025 - Elsevier
Chemical processes with distributed outputs are characterized by various operating
conditions, and the scarcity of labeled data poses challenges to the prediction of product …
conditions, and the scarcity of labeled data poses challenges to the prediction of product …
Temporal graph convolutional network soft sensor for molecular weight distribution prediction
In chemical processes with distributed outputs, characteristics of products are influenced by
their distributions and significantly correlated with process variables. It is crucial for an …
their distributions and significantly correlated with process variables. It is crucial for an …
Emerging trends in the optimization of organic synthesis through high-throughput tools and machine learning
PQ Velasco, K Hippalgaonkar… - Beilstein Journal of …, 2025 - beilstein-journals.org
The discovery of the optimal conditions for chemical reactions is a labor-intensive, time-
consuming task that requires exploring a high-dimensional parametric space. Historically …
consuming task that requires exploring a high-dimensional parametric space. Historically …
Data‐driven deep learning prediction of full molecular weight distribution in polymerization processes
D Mora‐Mariano, A Flores‐Tlacuahuac… - The Canadian Journal … - Wiley Online Library
The mathematical modelling of the full molecular weight distribution (MWD) results in a large
set of ordinary differential equations (ODEs), which usually requires considerable …
set of ordinary differential equations (ODEs), which usually requires considerable …