The Rise of Machine Learning in Polymer Discovery

Abstract

In the recent decades, with rapid development in computing power and algorithms, machine learning (ML) has exhibited its enormous potential in new polymer discovery. Herein, the history of ML is described and the basic process of ML accelerated polymer discovery is summarized. Next, the four steps in this process are reviewed, that is, dataset selection, fingerprinting, ML framework, and new polymer generation. Finally, a couple of main challenges for ML accelerated polymer discovery is presented and the outlooks in this field are prospected. It is expected that this review can service as a useful tool for the people who just step into this field and deepen the understanding for the people who are already in this field.

Deep Learning for Determination of Properties of Semidilute Polymer Solutions

“…Thus, the viscosity as a function of cl 3 and N w is represented as a matrix of fixed size. The analysis begins with the representation of the concentration dependence of the viscosity surface in the Rouse regime (η sp < 20) (14) in terms of the ratios η sp /N w (cl 3 ) 1.31 (where ν = 0.588 and B = B g for good solvent) and η sp /N w (cl 3 ) 2 (where ν = 0.5 and B = B th for θ solvent) as functions of cl 3 and N w (step 2). This is required for the determination of the values of the Bparameters in the corresponding solution regimes (Figure 1b).…”

“…The application of artificial intelligence (AI) in establishing structure–property correlations opened a new direction in polymer research. – The majority of research in this direction is focused on predicting polymer properties from a monomer chemical structure or their distribution along the polymer backbone. , In particular, data-driven approaches have utilized unsupervised learning and reverse engineering analysis methods , to expand the analysis of simulation and experimental data. – However, leveraging artificial intelligence to accelerate data analysis remains largely unexplored. , To address these challenges and the big data requirements needed for neural network training, we developed an approach that utilizes a scaling theory of semidilute polymer solutions – in conjunction with a convolutional neural network (CNN). – By generating large theoretical datasets using the confirmed scaling relationships, we eliminated the reliance on extensive experimental datasets. The CNN viability was tested on experimental datasets for the concentration dependence of specific viscosity in solutions of polymers with different molecular weights.…”

Antifouling polymers for nanomedicine and surfaces: recent advances

“…Finally, with the soaring popularity of artificial intelligence (AI) and machine learning (ML), future antifouling polymers will also likely see greater influence from computer-aided molecular and materials design. [154][155][156][157] The use of ML to design new polymers is an emerging field, and researchers are devising methods to represent the statistical nature of polymer structures and encode the field's domain knowledge into ML models. Autonomous experimentation methods are also being used to design novel structures more rapidly and with higher resolution.…”

Analysis of Electrochemical Impedance Data: Use of Deep Neural Networks

“…In recent years, technology has developed rapidly. Deep neural network (DNN) and various architectures of machine learning (ML) have been successfully used in many scientific areas such as physics, [12,13] astronomy, [14][15][16] chemistry, [17][18][19][20] biology, [21][22][23][24] and electrochemistry. [25][26][27][28] For instance, Gareth et al demonstrated identifying reaction mechanisms in the electrochemical system using DNN based on images of cyclic voltammograms and showed high performance in classification tasks.…”