Design of polyimides with targeted glass transition temperature using a graph neural network

Qiu, Haoke; Qiu, Xuepeng; Dai, Xuemin; Sun, Zhaoyan

doi:10.1039/d2tc05174e

Cited by 10 publications

(17 citation statements)

References 65 publications

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“…The properties of polymers are multi-faceted and oen sparse in specic property datasets. Therefore, we focused on four signicant polymer properties with publicly available datasets 21,[52][53][54] for training PolyNC. The included tasks consist of three general problems in a polymer domain (regression tasks) and one critical task specic to a particular polymer (classication task).…”

Section: 1d Enition Of Polymer-specic Tasksmentioning

confidence: 99%

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PolyNC: a natural and chemical language model for the prediction of unified polymer properties

Qiu,

Liu,

Qiu

et al. 2024

Chem. Sci.

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Section: 1d Enition Of Polymer-specic Tasksmentioning

confidence: 99%

“…To strive for endto-end learning, there has been a growing interest in graphbased approaches. [20][21][22][23] Capturing end-to-end representations of polymer structures offers great exibility for ML models to directly learn from raw data. 24 Among these approaches, the graph convolutional neural network (GCN) is commonly employed.…”

Section: Introductionmentioning

confidence: 99%

PolyNC: a natural and chemical language model for the prediction of unified polymer properties

Qiu,

Liu,

Qiu

et al. 2024

Chem. Sci.

Self Cite

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“…Then, they utilized the synthetic accessibility score (SAscore) to further screen the PIs that can be easily synthesized (e.g., SAscore < 3.8). 42 The SAscore represents synthesis accessibility on a scale from 1 (easy) to 10 (difficult) based on the difficulty of synthesizing the corresponding molecules, which is related to molecular segment contributions and molecular complexity. As shown in Figure 7B, 9191 PIs with high T g values and good synthetic accessibility were screened out.…”

Section: Virtual Design and High-throughput Screening Of Polymeric Ma...mentioning

confidence: 99%

Machine Learning-Assisted Design of Advanced Polymeric Materials

Gao,

Lin,

Wang

et al. 2024

Acc. Mater. Res.

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Conspectus Polymeric material research is encountering a new paradigm driven by machine learning (ML) and big data. The ML-assisted design has proven to be a successful approach for designing novel high-performance polymeric materials. This goal is mainly achieved through the following procedure: structure representation and database construction, establishment of a ML-based property prediction model, virtual design and high-throughput screening. The key to this approach lies in training ML models that delineate structure–property relationships based on available polymer data (e.g., structure, component, and property data), enabling the screening of promising polymers that satisfy the targeted property requirements. However, the relative scarcity of high-quality polymer data and the complex polymeric multiscale structure–property relationships pose challenges for this ML-assisted design method, such as data and modeling challenges. In this Account, we summarize the state-of-the-art advancements concerning the ML-assisted design of polymeric materials. Regarding structure representation and database construction, the digital representations of polymers are the predominant methods in cheminformatics along with some newly developed methods that integrate the polymeric multiscale structure characteristics. When establishing a ML-based property prediction model, the key is choosing and optimizing ML models to attain high-precision predictions across a vast chemical structure space. Advanced ML algorithms, such as transfer learning and multitask learning, have been utilized to address the data and modeling challenges. During the ML-assisted screening process, by defining and combining polymer genes, virtual polymer candidates are generated, and subsequently, their properties are predicted and high-throughput screened using ML property prediction models. Finally, the promising polymers identified through this approach are verified by computer simulations and experiments. We provide an overview of our recent efforts toward developing ML-assisted design approaches for discovering advanced polymeric materials and emphasize the intricate nature of polymer structural design. To well describe the multiscale structures of polymers, new structure representation methods, such as polymer fingerprint and cross-linking descriptors, were developed. Moreover, a multifidelity learning method was proposed to leverage the multisource isomerous polymer data from experiments and simulations. Additionally, graph neural networks and Bayesian optimization methods have been developed and applied for predicting polymer properties as well as designing polymer structures and compositions. Finally, we identify the current challenges and point out the development directions in this emerging field. It is highly desirable to establish new structure representation and advanced ML modeling methods for polymeric materials, particularly when constructing polymer large models based on chemical language. Through this Account, we seek to stimulate further ...

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“…14,15 PI adhesives have high curing pressure, poor melt uidity, and low glasstransition temperature. [16][17][18][19][20] The presence of adhesives also reduces the thermal stability and relative thermal dimensional stability of FCCLs. Meanwhile, 2L-FCCLs avoid these unfavorable factors caused by adhesives and reduce the thickness of the adhesive coating layer, thus allowing 5G electronic products to be thin, light, and tiny.…”

Section: Introductionmentioning

confidence: 99%

Progress of research on the bonding-strength improvement of two-layer adhesive-free flexible copper-clad laminates

Tang,

Liu,

Jing

et al. 2024

RSC Adv.

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Design of polyimides with targeted glass transition temperature using a graph neural network

Abstract: Polyimide substrates used in flexible display devices need to withstand very high temperatures and be highly thermally stable. The discovery of polyimides that satisfy these requirements, especially with high glass...

Cited by 10 publications

References 65 publications

PolyNC: a natural and chemical language model for the prediction of unified polymer properties

PolyNC: a natural and chemical language model for the prediction of unified polymer properties

Machine Learning-Assisted Design of Advanced Polymeric Materials

Progress of research on the bonding-strength improvement of two-layer adhesive-free flexible copper-clad laminates

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