Machine Learning Predictions of Block Copolymer Self‐Assembly

Abstract

Directed self‐assembly of block copolymers is a key enabler for nanofabrication of devices with sub‐10 nm feature sizes, allowing patterning far below the resolution limit of conventional photolithography. Among all the process steps involved in block copolymer self‐assembly, solvent annealing plays a dominant role in determining the film morphology and pattern quality, yet the interplay of the multiple parameters during solvent annealing, including the initial thickness, swelling, time, and solvent ratio, makes it difficult to predict and control the resultant self‐assembled pattern. Here, machine learning tools are applied to analyze the solvent annealing process and predict the effect of process parameters on morphology and defectivity. Two neural networks are constructed and trained, yielding accurate prediction of the final morphology in agreement with experimental data. A ridge regression model is constructed to identify the critical parameters that determine the quality of line/space patterns. These results illustrate the potential of machine learning to inform nanomanufacturing processes.

Integration of Machine Learning and Coarse-Grained Molecular Simulations for Polymer Materials: Physical Understandings and Molecular Design

“…In this situation, the existence of experimental data plays an important role. Recent research shows that integrating machine learning with experimental data allows us to accurately predict the areal proportion of each of the four morphologies in block-copolymer phase separation, identify critical process parameters, and predict the experimental outcomes ( Tu et al, 2020 ). The experimental data is considered as a small set of high-fidelity data ( Chen et al, 2021c ), while the CGMD simulation provides a larger set of lower-fidelity data.…”

A Machine Learning Method for Material Property Prediction: Example Polymer Compatibility

“…Researchers have made active and effective attempts to apply ML method in exploring polymer syntheses and polymer materials [22]. The copolymer synthesis and defectivity [23,24,25], mechanical properties of polymer composites [26], liquid crystal behavior of copolyether [27], thermal conductivity [28], dielectric properties [29], glass transition, melting, and degradation temperature and quantum physical and chemical properties [30,31,32,33] have been applied with machine learning and good prediction accuracy is achieved. Muramatsu et al [34] have used ML method to investigate the relationship between the phase separation structure of polymer blend and Young's modulus, and builds a predictive framework based on two-dimensional images of polymer blend as the descriptor.…”

Machine learning for polymeric materials: an introduction

“…Polymer informatics has been applied to essentially every aspect of the polymer lifecycle. It has been used to design new monomers for various applications 12,15,16 ; engineer reactions 17 ; model processing conditions and parameters [18][19][20] ; identify and predict polymer conformations and phases [21][22][23][24][25][26] ; predict materials properties [27][28][29][30][31][32][33][34][35] ; and finally offer insight into wear and end of life. 4,[36][37][38][39] Most polymer informatics literature focuses on property prediction, but recently other aspects of polymer synthesis, processing and lifetime have been gaining attention.…”