Machine Learning Framework for Characterizing Processing–Structure Relationship in Block Copolymer Thin Films

Lamb, Bradley; Upreti, Saroj; Wang, Yunfei; Struble, Daniel; Zhu, Chenhui; Freychet, Guillaume; Gu, Xiaodan; Ma, Boran

doi:10.1021/acs.macromol.5c03272

Title: Machine Learning Framework for Characterizing Processing–Structure Relationship in Block Copolymer Thin Films

Journal Article · Sun Jan 11 00:00:00 UTC 2026 · Macromolecules

DOI: https://doi.org/10.1021/acs.macromol.5c03272 · OSTI ID:3015413

Lamb, Bradley ^[1]; Upreti, Saroj ^[1];

^[1]; Struble, Daniel ^[1];

^[2];

^[3];

^[1];

^[1]

Univ. of Southern Mississippi, Hattiesburg, MS (United States)
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). Advanced Light Source (ALS)
Brookhaven National Laboratory (BNL), Upton, NY (United States). National Synchrotron Light Source II (NSLS-II); Univ. of Grenoble Alpes, Grenoble (France)

The morphology of block copolymers (BCPs) critically influences material properties and applications. This work introduces a machine learning (ML)-enabled, high-throughput framework for analyzing grazing incidence small-angle X-ray scattering (GISAXS) data and atomic force microscopy (AFM) images to characterize BCP thin film morphology. A convolutional neural network was trained to classify AFM images by surface features, achieving 97% testing accuracy. Classified images were then analyzed to extract 2D grain size measurements from the samples in a high-throughput manner. ML models were trained to predict domain orientation based on processing parameters such as solvent ratio, additive type, and additive ratio. GISAXS-based properties were predicted with strong performances (R² > 0.75), while AFM-based property predictions were less accurate (R² < 0.60), likely due to the localized nature of AFM measurements compared to the bulk information captured by GISAXS. Beyond model performance, interpretability was addressed using SHapley Additive exPlanations (SHAP). SHAP analysis revealed that the additive ratio had the largest impact on morphological predictions, where additive provides the BCP chains with increased volume to rearrange into thermodynamically favorable morphologies. This interpretability helps validate model predictions and offers insight into parameter importance. Altogether, the presented framework combining high-throughput characterization and interpretable ML offers an approach to exploring and optimizing BCP thin film morphology across a broad processing landscape.

View Accepted Manuscript (DOE)

Cite

Export

Save

Research Organization:: Brookhaven National Laboratory (BNL), Upton, NY (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); US Department of Energy (USDOE), Washington, DC (United States). Office of Science, Basic Energy Sciences (BES)

Sponsoring Organization:: National Science Foundation (NSF); USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF)

Grant/Contract Number:: AC02-05CH11231; SC0012704; SC0024432

OSTI ID:: 3015413

Report Number(s):: BNL--229366-2026-JAAM

Journal Information:: Macromolecules, Journal Name: Macromolecules Journal Issue: 2 Vol. 59; ISSN 1520-5835; ISSN 0024-9297

Publisher:: American Chemical SocietyCopyright Statement

Country of Publication:: United States

Language:: English

References (81)

Unraveling the Main Chain and Side Chain Effects on Thin Film Morphology and Charge Transport in Quinoidal Conjugated Polymers Liu, Xuncheng; He, Bo; Garzón-Ruiz, Andrés Advanced Functional Materials, Vol. 28, Issue 31 https://doi.org/10.1002/adfm.201801874	journal	June 2018
Application of Digital Methods in Polymer Science and Engineering Schuett, Timo; Endres, Patrick; Standau, Tobias Advanced Functional Materials, Vol. 34, Issue 8 https://doi.org/10.1002/adfm.202309844	journal	October 2023
An In Situ Grazing Incidence X-Ray Scattering Study of Block Copolymer Thin Films During Solvent Vapor Annealing Gu, Xiaodan; Gunkel, Ilja; Hexemer, Alexander Advanced Materials, Vol. 26, Issue 2 https://doi.org/10.1002/adma.201302562	journal	November 2013
25th Anniversary Article: Bulk Heterojunction Solar Cells: Understanding the Mechanism of Operation Heeger, Alan J. Advanced Materials, Vol. 26, Issue 1 https://doi.org/10.1002/adma.201304373	journal	December 2013
Light-Modulation of the Charge Injection in a Polymer Thin-Film Transistor by Functionalizing the Electrodes with Bistable Photochromic Self-Assembled Monolayers Mosciatti, Thomas; del Rosso, Maria G.; Herder, Martin Advanced Materials, Vol. 28, Issue 31 https://doi.org/10.1002/adma.201600651	journal	May 2016
Nanoscale Block Copolymer Self‐Assembly and Microscale Polymer Film Dewetting: Progress in Understanding the Role of Interfacial Energies in the Formation of Hierarchical Nanostructures Brassat, Katharina; Lindner, Jörg K. N. Advanced Materials Interfaces, Vol. 7, Issue 5 https://doi.org/10.1002/admi.201901565	journal	December 2019
Machine learning in materials science Wei, Jing; Chu, Xuan; Sun, Xiang‐Yu InfoMat, Vol. 1, Issue 3 https://doi.org/10.1002/inf2.12028	journal	August 2019
Machine learning in polymer informatics Sha, Wuxin; Li, Yan; Tang, Shun InfoMat, Vol. 3, Issue 4 https://doi.org/10.1002/inf2.12167	journal	January 2021
Accurate Prediction of θ (Lower Critical Solution Temperature) in Polymer Solutions Based on 3D Descriptors and Artificial Neural Networks Xu, Jie; Chen, Biao; Liang, Hao Macromolecular Theory and Simulations, Vol. 17, Issue 2-3 https://doi.org/10.1002/mats.200700067	journal	April 2008
Gas separation using polymer membranes: an overview Ghosal, Kanchan; Freeman, Benny D. Polymers for Advanced Technologies, Vol. 5, Issue 11 https://doi.org/10.1002/pat.1994.220051102	journal	November 1994
Morphologies of strongly segregated polystyrene-poly(dimethylsiloxane) diblock copolymers Chu, Jennifer H.; Rangarajan, Pratima; Adams, J. LaMonte Polymer, Vol. 36, Issue 8 https://doi.org/10.1016/0032-3861(95)99001-B	journal	January 1995
Thermal annealing of roll-cast triblock copolymer films Albalak, Ramon J.; Thomas, Edwin L.; Capel, Malcolm S. Polymer, Vol. 38, Issue 15 https://doi.org/10.1016/S0032-3861(96)00938-X	journal	July 1997
Solvent swelling of roll-cast triblock copolymer films Albalak, Ramon J.; Capel, Malcolm S.; Thomas, Edwin L. Polymer, Vol. 39, Issue 8-9 https://doi.org/10.1016/S0032-3861(97)00497-7	journal	January 1998
Automation and data-driven design of polymer therapeutics Upadhya, Rahul; Kosuri, Shashank; Tamasi, Matthew Advanced Drug Delivery Reviews, Vol. 171 https://doi.org/10.1016/j.addr.2020.11.009	journal	April 2021
Machine learning regression and classification methods for fog events prediction Castillo-Botón, C.; Casillas-Pérez, D.; Casanova-Mateo, C. Atmospheric Research, Vol. 272 https://doi.org/10.1016/j.atmosres.2022.106157	journal	July 2022
Polymer design using genetic algorithm and machine learning Kim, Chiho; Batra, Rohit; Chen, Lihua Computational Materials Science, Vol. 186 https://doi.org/10.1016/j.commatsci.2020.110067	journal	January 2021
High-Dimensional Design-Of-Experiments Extracts Small-Molecule-Only Induction Conditions for Dorsal Pancreatic Endoderm from Pluripotency Bukys, Michael A.; Mihas, Alexander; Finney, Krystal iScience, Vol. 23, Issue 8 https://doi.org/10.1016/j.isci.2020.101346	journal	August 2020
Polymer informatics: Current status and critical next steps Chen, Lihua; Pilania, Ghanshyam; Batra, Rohit Materials Science and Engineering: R: Reports, Vol. 144 https://doi.org/10.1016/j.mser.2020.100595	journal	April 2021
Alternating crystalline lamellar structures from thermodynamically miscible poly(ε-caprolactone) H/D blends Li, Lengwan; Arras, Matthias M. L.; Li, Tianyu Polymer, Vol. 175 https://doi.org/10.1016/j.polymer.2019.05.055	journal	June 2019
Directing the self-assembly of block copolymers Darling, S. B. Progress in Polymer Science, Vol. 32, Issue 10 https://doi.org/10.1016/j.progpolymsci.2007.05.004	journal	October 2007
In situ ellipsometry studies on swelling of thin polymer films: A review Ogieglo, Wojciech; Wormeester, Herbert; Eichhorn, Klaus-Jochen Progress in Polymer Science, Vol. 42 https://doi.org/10.1016/j.progpolymsci.2014.09.004	journal	March 2015
Recent progress on fabrication and applications of advanced block copolymer membranes Wang, Xue-Qi; Wang, Tao; Feng, Ying-Nan Sustainable Materials and Technologies, Vol. 39 https://doi.org/10.1016/j.susmat.2024.e00855	journal	April 2024
Toward Machine Learning-Enhanced High-Throughput Experimentation Eyke, Natalie S.; Koscher, Brent A.; Jensen, Klavs F. Trends in Chemistry, Vol. 3, Issue 2 https://doi.org/10.1016/j.trechm.2020.12.001	journal	February 2021
Design of Experiments for Nanocrystal Syntheses: A How-To Guide for Proper Implementation Williamson, Emily M.; Sun, Zhaohong; Mora-Tamez, Lucía Chemistry of Materials, Vol. 34, Issue 22 https://doi.org/10.1021/acs.chemmater.2c02924	journal	November 2022
High-Throughput Machine-Learning-Driven Synthesis of Full-Heusler Compounds Oliynyk, Anton O.; Antono, Erin; Sparks, Taylor D. Chemistry of Materials, Vol. 28, Issue 20 https://doi.org/10.1021/acs.chemmater.6b02724	journal	October 2016
Benchmarking Machine Learning Models for Polymer Informatics: An Example of Glass Transition Temperature Tao, Lei; Varshney, Vikas; Li, Ying Journal of Chemical Information and Modeling, Vol. 61, Issue 11 https://doi.org/10.1021/acs.jcim.1c01031	journal	October 2021
Deciphering the Scattering of Mechanically Driven Polymers Using Deep Learning Ding, Lijie; Tung, Chi-Huan; Sumpter, Bobby G. Journal of Chemical Theory and Computation, Vol. 21, Issue 8 https://doi.org/10.1021/acs.jctc.5c00409	journal	April 2025
Machine Learning Aided Design of Polymer with Targeted Band Gap Based on DFT Computation Xu, Pengcheng; Lu, Tian; Ju, Lifei The Journal of Physical Chemistry B, Vol. 125, Issue 2 https://doi.org/10.1021/acs.jpcb.0c08674	journal	January 2021
Assessing the Local Nanomechanical Properties of Self-Assembled Block Copolymer Thin Films by Peak Force Tapping Lorenzoni, Matteo; Evangelio, Laura; Verhaeghe, Sophie Langmuir, Vol. 31, Issue 42 https://doi.org/10.1021/acs.langmuir.5b02595	journal	October 2015
Mechanical Properties of Ultrathin Polystyrene-b-Polybutadiene-b-Polystyrene Block Copolymer Films: Film Thickness-Dependent Young’s Modulus Saito, Masayuki; Ito, Kohzo; Yokoyama, Hideaki Macromolecules, Vol. 54, Issue 18 https://doi.org/10.1021/acs.macromol.1c01406	journal	September 2021
Predicting Phase Behavior of Linear Polymers in Solution Using Machine Learning Ethier, Jeffrey G.; Casukhela, Rohan K.; Latimer, Joshua J. Macromolecules, Vol. 55, Issue 7 https://doi.org/10.1021/acs.macromol.2c00245	journal	March 2022
Machine Learning-Enhanced Computational Reverse-Engineering Analysis for Scattering Experiments (CREASE) for Analyzing Fibrillar Structures in Polymer Solutions Wu, Zijie; Jayaraman, Arthi Macromolecules, Vol. 55, Issue 24 https://doi.org/10.1021/acs.macromol.2c02165	journal	December 2022
Machine-Learning-Assisted Understanding of Polymer Nanocomposites Composition–Property Relationship: A Case Study of NanoMine Database Ma, Boran; Finan, Nicholas J.; Jany, David Macromolecules, Vol. 56, Issue 11 https://doi.org/10.1021/acs.macromol.2c02249	journal	May 2023
Automation and Machine Learning for Accelerated Polymer Characterization and Development: Past, Potential, and a Path Forward Beaucage, Peter A.; Sutherland, Duncan R.; Martin, Tyler B. Macromolecules, Vol. 57, Issue 18 https://doi.org/10.1021/acs.macromol.4c01410	journal	September 2024
Self-Assembly in Block Copolymer Thin Films upon Solvent Evaporation: A Simulation Study Hao, Jinlong; Wang, Zhan; Wang, Zheng Macromolecules, Vol. 50, Issue 11 https://doi.org/10.1021/acs.macromol.7b00200	journal	May 2017
Roll-to-Roll Scalable Production of Ordered Microdomains through Nonvolatile Additive Solvent Annealing of Block Copolymers Weller, Daniel W.; Galuska, Luke; Wang, Weiyu Macromolecules, Vol. 52, Issue 13 https://doi.org/10.1021/acs.macromol.9b00772	journal	June 2019
Multiple Linear Regression Modeling To Predict the Stability of Polymer–Drug Solid Dispersions: Comparison of the Effects of Polymers and Manufacturing Methods on Solid Dispersion Stability Fridgeirsdottir, Gudrun A.; Harris, Robert J.; Dryden, Ian L. Molecular Pharmaceutics, Vol. 15, Issue 5 https://doi.org/10.1021/acs.molpharmaceut.8b00021	journal	March 2018
A Practical Start-Up Guide for Synthetic Chemists to Implement Design of Experiments (DoE) Wall, Brendan J.; Koeritz, Mason T.; Stanley, Levi M. ACS Catalysis, Vol. 15, Issue 11 https://doi.org/10.1021/acscatal.5c01626	journal	May 2025
How To Optimize Materials and Devices via Design of Experiments and Machine Learning: Demonstration Using Organic Photovoltaics Cao, Bing; Adutwum, Lawrence A.; Oliynyk, Anton O. ACS Nano, Vol. 12, Issue 8 https://doi.org/10.1021/acsnano.8b04726	journal	July 2018
Machine Learning Enhanced Computational Reverse Engineering Analysis for Scattering Experiments (CREASE) to Determine Structures in Amphiphilic Polymer Solutions Wessels, Michiel G.; Jayaraman, Arthi ACS Polymers Au, Vol. 1, Issue 3 https://doi.org/10.1021/acspolymersau.1c00015	journal	July 2021
Data-Driven Methods for Accelerating Polymer Design Patra, Tarak K. ACS Polymers Au, Vol. 2, Issue 1 https://doi.org/10.1021/acspolymersau.1c00035	journal	December 2021
Navigating the Expansive Landscapes of Soft Materials: A User Guide for High-Throughput Workflows Day, Erin C.; Chittari, Supraja S.; Bogen, Matthew P. ACS Polymers Au, Vol. 3, Issue 6 https://doi.org/10.1021/acspolymersau.3c00025	journal	December 2023
Computational Reverse-Engineering Analysis for Scattering Experiments on Amphiphilic Block Polymer Solutions Beltran-Villegas, Daniel J.; Wessels, Michiel G.; Lee, Jee Young Journal of the American Chemical Society, Vol. 141, Issue 37 https://doi.org/10.1021/jacs.9b08028	journal	September 2019
Quantitative SAXS Analysis of Oriented 2D Hexagonal Cylindrical Silica Mesostructures in Thin Films Obtained from Nonionic Surfactants Smarsly, Bernd; Gibaud, Alain; Ruland, Wilhelm Langmuir, Vol. 21, Issue 9 https://doi.org/10.1021/la046916r	journal	April 2005
Hexagonal mesophases between lamellae and cylinders in a diblock copolymer melt Hamley, Ian W.; Koppi, Kurt A.; Rosedale, Jeffrey H. Macromolecules, Vol. 26, Issue 22 https://doi.org/10.1021/ma00074a018	journal	October 1993
Well-Ordered Microdomain Structures in Polydisperse Poly(styrene)−Poly(acrylic acid) Diblock Copolymers from Controlled Radical Polymerization Bendejacq, D.; Ponsinet, V.; Joanicot, M. Macromolecules, Vol. 35, Issue 17 https://doi.org/10.1021/ma020158z	journal	August 2002
Adjustment of Block Copolymer Nanodomain Sizes at Lattice Defect Sites Hammond, Matthew R.; Sides, Scott W.; Fredrickson, Glenn H. Macromolecules, Vol. 36, Issue 23 https://doi.org/10.1021/ma026001o	journal	November 2003
Block Copolymer Theory. 4. Narrow Interphase Approximation Helfand, Eugene; Wasserman, Z. R. Macromolecules, Vol. 9, Issue 6 https://doi.org/10.1021/ma60054a001	journal	November 1976
Theory of Microphase Separation in Block Copolymers Leibler, Ludwik Macromolecules, Vol. 13, Issue 6 https://doi.org/10.1021/ma60078a047	journal	November 1980
Perpendicular Orientation of Domains in Cylinder-Forming Block Copolymer Thick Films by Controlled Interfacial Interactions Han, Eungnak; Stuen, Karl O.; Leolukman, Melvina Macromolecules, Vol. 42, Issue 13 https://doi.org/10.1021/ma9002903	journal	July 2009
Unifying Weak- and Strong-Segregation Block Copolymer Theories Matsen, M. W.; Bates, F. S. Macromolecules, Vol. 29, Issue 4 https://doi.org/10.1021/ma951138i	journal	January 1996
Effect of Film Thickness on the Phase Behaviors of Diblock Copolymer Thin Film Jung, Jueun; Park, Hae-Woong; Lee, Sekyung ACS Nano, Vol. 4, Issue 6 https://doi.org/10.1021/nn1003309	journal	May 2010
Systematic Study on the Effect of Solvent Removal Rate on the Morphology of Solvent Vapor Annealed ABA Triblock Copolymer Thin Films Albert, Julie N. L.; Young, Wen-Shiue; Lewis, Ronald L. ACS Nano, Vol. 6, Issue 1 https://doi.org/10.1021/nn203776c	journal	December 2011
Effect of Film Thickness and Domain Spacing on Defect Densities in Directed Self-Assembly of Cylindrical Morphology Block Copolymers Mishra, Vindhya; Fredrickson, Glenn H.; Kramer, Edward J. ACS Nano, Vol. 6, Issue 3 https://doi.org/10.1021/nn205120j	journal	February 2012
Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning Lu, Shuaihua; Zhou, Qionghua; Ouyang, Yixin Nature Communications, Vol. 9, Issue 1 https://doi.org/10.1038/s41467-018-05761-w	journal	August 2018
From local explanations to global understanding with explainable AI for trees Lundberg, Scott M.; Erion, Gabriel; Chen, Hugh Nature Machine Intelligence, Vol. 2, Issue 1 https://doi.org/10.1038/s42256-019-0138-9	journal	January 2020
Influence of film casting method on block copolymer ordering in thin films Zhang, Xiaohua; Douglas, Jack F.; Jones, Ronald L. Soft Matter, Vol. 8, Issue 18 https://doi.org/10.1039/C2SM07308K	journal	January 2012
Machine-learning-assisted low dielectric constant polymer discovery Liang, Jiechun; Xu, Shangqian; Hu, Linfeng Materials Chemistry Frontiers, Vol. 5, Issue 10 https://doi.org/10.1039/D0QM01093F	journal	January 2021
Structure evolution during phase separation in spin-coated ethylcellulose/hydroxypropylcellulose films Carmona, Pierre; Röding, Magnus; Särkkä, Aila Soft Matter, Vol. 17, Issue 14 https://doi.org/10.1039/D1SM00044F	journal	January 2021
Machine learning of atomic force microscopy images of organic solar cells Kobayashi, Yasuhito; Miyake, Yuta; Ishiwari, Fumitaka RSC Advances, Vol. 13, Issue 22 https://doi.org/10.1039/D3RA02492J	journal	January 2023
Machine learning for analyzing atomic force microscopy (AFM) images generated from polymer blends Paruchuri, Aanish; Wang, Yunfei; Gu, Xiaodan Digital Discovery, Vol. 3, Issue 12 https://doi.org/10.1039/D4DD00215F	journal	January 2024
Small angle scattering of diblock copolymers profiled by machine learning Tung, Chi-Huan; Chang, Shou-Yi; Chen, Hsin-Lung The Journal of Chemical Physics, Vol. 156, Issue 13 https://doi.org/10.1063/5.0086311	journal	April 2022
Mathematical regression models for rheological behavior of interaction between polymer-surfactant binary mixtures and electrolytes Banerjee, Tandrima; Samanta, Abhijit; Mandal, Ajay Journal of Dispersion Science and Technology, Vol. 43, Issue 9 https://doi.org/10.1080/01932691.2020.1857261	journal	December 2020
MLExchange: A web-based platform enabling exchangeable machine learning workflows for scientific studies Zhao, Zhuowen; Chavez, Tanny; Holman, Elizabeth A. 2022 4th Annual Workshop on Extreme-scale Experiment-in-the-Loop Computing (XLOOP) https://doi.org/10.1109/XLOOP56614.2022.00007	conference	November 2022
Self-driving laboratory for accelerated discovery of thin-film materials MacLeod, B. P.; Parlane, F. G. L.; Morrissey, T. D. Science Advances, Vol. 6, Issue 20 https://doi.org/10.1126/sciadv.aaz8867	journal	May 2020
Autonomous discovery of emergent morphologies in directed self-assembly of block copolymer blends Doerk, Gregory S.; Stein, Aaron; Bae, Suwon Science Advances, Vol. 9, Issue 2 https://doi.org/10.1126/sciadv.add3687	journal	January 2023
Controlling Polymer-Surface Interactions with Random Copolymer Brushes Mansky, P.; Liu, Y.; Huang, E. Science, Vol. 275, Issue 5305, p. 1458-1460 https://doi.org/10.1126/science.275.5305.1458	journal	March 1997
Machine learning: Trends, perspectives, and prospects Jordan, M. I.; Mitchell, T. M. Science, Vol. 349, Issue 6245 https://doi.org/10.1126/science.aaa8415	journal	July 2015
Hexagonal Patterns in a Simplified Model for Block Copolymers Bourne, D. P.; Peletier, M. A.; Roper, S. M. SIAM Journal on Applied Mathematics, Vol. 74, Issue 5 https://doi.org/10.1137/130922732	journal	January 2014
Block Copolymer Thermodynamics: Theory and Experiment Bates, Frank S.; Fredrickson, Glenn H. Annual Review of Physical Chemistry, Vol. 41, Issue 1 https://doi.org/10.1146/annurev.pc.41.100190.002521	journal	October 1990
A theoritical study on spin coating technique Tyona, M. D. Advances in materials Research, Vol. 2, Issue 4 https://doi.org/10.12989/amr.2013.2.4.195	journal	December 2013
Automated Defect and Correlation Length Analysis of Block Copolymer Thin Film Nanopatterns Murphy, Jeffrey N.; Harris, Kenneth D.; Buriak, Jillian M. PLOS ONE, Vol. 10, Issue 7 https://doi.org/10.1371/journal.pone.0133088	journal	July 2015
A prospective on machine learning challenges, progress, and potential in polymer science Struble, Daniel C.; Lamb, Bradley G.; Ma, Boran MRS Communications, Vol. 14, Issue 5 https://doi.org/10.1557/s43579-024-00587-8	journal	July 2024
Comparison between Multiple Regression Analysis, Polynomial Regression Analysis, and an Artificial Neural Network for Tensile Strength Prediction of BFRP and GFRP Kim, Younghwan; Oh, Hongseob Materials, Vol. 14, Issue 17 https://doi.org/10.3390/ma14174861	journal	August 2021
The Applications of Polymers in Solar Cells: A Review Hou, Wenjing; Xiao, Yaoming; Han, Gaoyi Polymers, Vol. 11, Issue 1 https://doi.org/10.3390/polym11010143	journal	January 2019
Predicting Polymers’ Glass Transition Temperature by a Chemical Language Processing Model Chen, Guang; Tao, Lei; Li, Ying Polymers, Vol. 13, Issue 11 https://doi.org/10.3390/polym13111898	journal	June 2021
Rapid Assembly of Block Copolymer Thin Films via Accelerating the Swelling Process During Solvent Annealing Shui, Tian-en; Chang, Tongxin; Wang, Zhe Polymers, Vol. 17, Issue 9 https://doi.org/10.3390/polym17091242	journal	May 2025
Scikit-learn: Machine Learning in Python Pedregosa, Fabian; Varoquaux, Gaël; Gramfort, Alexandre arXiv https://doi.org/10.48550/arXiv.1201.0490	text	January 2012
Very Deep Convolutional Networks for Large-Scale Image Recognition Simonyan, Karen; Zisserman, Andrew arXiv https://doi.org/10.48550/arXiv.1409.1556	preprint	January 2014
A Unified Approach to Interpreting Model Predictions Lundberg, Scott; Lee, Su-In arXiv https://doi.org/10.48550/arXiv.1705.07874	preprint	January 2017
Accelerating Neutron Scattering Data Collection and Experiments Using AI Deep Super-Resolution Learning Chang, Ming-Ching; Wei, Yi; Chen, Wei-Ren arXiv https://doi.org/10.48550/arXiv.1904.08450	preprint	January 2019

Similar Records

Intraatomic correlation correction in the FORS model

Journal Article · Thu May 23 04:00:00 UTC 1985 · J. Phys. Chem.; (United States) · OSTI ID:5133773

Lam, B; Schmidt, M W; Ruedenberg, K

Architecture-Dependent Thin Film Self-Assembly of Star Polystyrene-poly(2-vinylpyridine) Block Copolymers

Journal Article · Tue Apr 01 00:00:00 UTC 2025 · Macromolecules · OSTI ID:2573208

Gan, Xuchen; Chen, Zhan; Steinmetz, Christian; +6 more

A Perturbative Solution for Nonlinear Stratified Upwelling over a Frictional Slope

Journal Article · Sun Oct 01 04:00:00 UTC 2023 · Journal of Physical Oceanography · OSTI ID:2580152

Choi, Jang-Geun; Pringle, James; Lippmann, Thomas

Related Subjects

36 MATERIALS SCIENCE
Additives
Copolymers
Morphology
Solvents
Thin films

Title: Machine Learning Framework for Characterizing Processing–Structure Relationship in Block Copolymer Thin Films

Citation Formats

References (81)

Similar Records

Related Subjects