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Deng Z, Wang W, Xu L, Bai H, Tang H. A Novel Method for Full-Section Assessment of High-Speed Railway Subgrade Compaction Quality Based on ML-Interval Prediction Theory. SENSORS (BASEL, SWITZERLAND) 2024; 24:3661. [PMID: 38894454 PMCID: PMC11175314 DOI: 10.3390/s24113661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Revised: 05/13/2024] [Accepted: 05/15/2024] [Indexed: 06/21/2024]
Abstract
The high-speed railway subgrade compaction quality is controlled by the compaction degree (K), with the maximum dry density (ρdmax) serving as a crucial indicator for its calculation. The current mechanisms and methods for determining the ρdmax still suffer from uncertainties, inefficiencies, and lack of intelligence. These deficiencies can lead to insufficient assessments for the high-speed railway subgrade compaction quality, further impacting the operational safety of high-speed railways. In this paper, a novel method for full-section assessment of high-speed railway subgrade compaction quality based on ML-interval prediction theory is proposed. Firstly, based on indoor vibration compaction tests, a method for determining the ρdmax based on the dynamic stiffness Krb turning point is proposed. Secondly, the Pso-OptimalML-Adaboost (POA) model for predicting ρdmax is determined based on three typical machine learning (ML) algorithms, which are back propagation neural network (BPNN), support vector regression (SVR), and random forest (RF). Thirdly, the interval prediction theory is introduced to quantify the uncertainty in ρdmax prediction. Finally, based on the Bootstrap-POA-ANN interval prediction model and spatial interpolation algorithms, the interval distribution of ρdmax across the full-section can be determined, and a model for full-section assessment of compaction quality is developed based on the compaction standard (95%). Moreover, the proposed method is applied to determine the optimal compaction thicknesses (H0), within the station subgrade test section in the southwest region. The results indicate that: (1) The PSO-BPNN-AdaBoost model performs better in the accuracy and error metrics, which is selected as the POA model for predicting ρdmax. (2) The Bootstrap-POA-ANN interval prediction model for ρdmax can construct clear and reliable prediction intervals. (3) The model for full-section assessment of compaction quality can provide the full-section distribution interval for K. Comparing the H0 of 50~60 cm and 60~70 cm, the compaction quality is better with the H0 of 40~50 cm. The research findings can provide effective techniques for assessing the compaction quality of high-speed railway subgrades.
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Affiliation(s)
- Zhixing Deng
- Department of Civil Engineering, Central South University, Changsha 410075, China; (Z.D.)
| | - Wubin Wang
- National Engineering Research Center of Geological Disaster Prevention Technology in Land Transportation, Southwest Jiaotong University, Chengdu 611731, China
| | - Linrong Xu
- Department of Civil Engineering, Central South University, Changsha 410075, China; (Z.D.)
| | - Hao Bai
- Sichuan Expressway Construction & Development Group Co., Ltd., Chengdu 610041, China
| | - Hao Tang
- Sichuan Expressway Construction & Development Group Co., Ltd., Chengdu 610041, China
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2
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Jain A, Armstrong CD, Joseph VR, Ramprasad R, Qi HJ. Machine-Guided Discovery of Acrylate Photopolymer Compositions. ACS APPLIED MATERIALS & INTERFACES 2024; 16:17992-18000. [PMID: 38534124 PMCID: PMC11009904 DOI: 10.1021/acsami.4c00759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2024] [Revised: 03/14/2024] [Accepted: 03/15/2024] [Indexed: 03/28/2024]
Abstract
Additive manufacturing (AM) can be advanced by the diverse characteristics offered by thermoplastic and thermoset polymers and the further benefits of copolymerization. However, the availability of suitable polymeric materials for AM is limited and may not always be ideal for specific applications. Additionally, the extensive number of potential monomers and their combinations make experimental determination of resin compositions extremely time-consuming and costly. To overcome these challenges, we develop an active learning (AL) approach to effectively choose compositions in a ternary monomer space ranging from rigid to elastomeric. Our AL algorithm dynamically suggests monomer composition ratios for the subsequent round of testing, allowing us to efficiently build a robust machine learning (ML) model capable of predicting polymer properties, including Young's modulus, peak stress, ultimate strain, and Shore A hardness based on composition while minimizing the number of experiments. As a demonstration of the effectiveness of our approach, we use the ML model to drive material selection for a specific property, namely, Young's modulus. The results indicate that the ML model can be used to select material compositions within at least 10% of a targeted value of Young's modulus. We then use the materials designed by the ML model to 3D print a multimaterial "hand" with soft "skin" and rigid "bones". This work presents a promising tool for enabling informed AM material selection tailored to user specifications and accelerating material discovery using a limited monomer space.
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Affiliation(s)
- Ayush Jain
- School
of Material Science and Engineering, Georgia
Institute of Technology, Atlanta, Georgia 30332, United States
- College
of Computing, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Connor D. Armstrong
- School
of Mechanical Engineering, Georgia Institute
of Technology, Atlanta, Georgia 30332, United States
- Renewable
Bioproducts Institute, Georgia Institute
of Technology, Atlanta, Georgia 30332, United States
| | - V. Roshan Joseph
- H.
Milton Stewart School of Industrial
and Systems Engineering, Georgia Institute
of Technology, Atlanta, Georgia 30332, United States
| | - Rampi Ramprasad
- School
of Material Science and Engineering, Georgia
Institute of Technology, Atlanta, Georgia 30332, United States
| | - H. Jerry Qi
- School
of Mechanical Engineering, Georgia Institute
of Technology, Atlanta, Georgia 30332, United States
- Renewable
Bioproducts Institute, Georgia Institute
of Technology, Atlanta, Georgia 30332, United States
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3
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Patel RA, Webb MA. Data-Driven Design of Polymer-Based Biomaterials: High-throughput Simulation, Experimentation, and Machine Learning. ACS APPLIED BIO MATERIALS 2024; 7:510-527. [PMID: 36701125 DOI: 10.1021/acsabm.2c00962] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Polymers, with the capacity to tunably alter properties and response based on manipulation of their chemical characteristics, are attractive components in biomaterials. Nevertheless, their potential as functional materials is also inhibited by their complexity, which complicates rational or brute-force design and realization. In recent years, machine learning has emerged as a useful tool for facilitating materials design via efficient modeling of structure-property relationships in the chemical domain of interest. In this Spotlight, we discuss the emergence of data-driven design of polymers that can be deployed in biomaterials with particular emphasis on complex copolymer systems. We outline recent developments, as well as our own contributions and takeaways, related to high-throughput data generation for polymer systems, methods for surrogate modeling by machine learning, and paradigms for property optimization and design. Throughout this discussion, we highlight key aspects of successful strategies and other considerations that will be relevant to the future design of polymer-based biomaterials with target properties.
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Affiliation(s)
- Roshan A Patel
- Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey 08540, United States
| | - Michael A Webb
- Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey 08540, United States
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4
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AlFaraj Y, Mohapatra S, Shieh P, Husted KEL, Ivanoff DG, Lloyd EM, Cooper JC, Dai Y, Singhal AP, Moore JS, Sottos NR, Gomez-Bombarelli R, Johnson JA. A Model Ensemble Approach Enables Data-Driven Property Prediction for Chemically Deconstructable Thermosets in the Low-Data Regime. ACS CENTRAL SCIENCE 2023; 9:1810-1819. [PMID: 37780353 PMCID: PMC10540282 DOI: 10.1021/acscentsci.3c00502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Indexed: 10/03/2023]
Abstract
Thermosets present sustainability challenges that could potentially be addressed through the design of deconstructable variants with tunable properties; however, the combinatorial space of possible thermoset molecular building blocks (e.g., monomers, cross-linkers, and additives) and manufacturing conditions is vast, and predictive knowledge for how combinations of these molecular components translate to bulk thermoset properties is lacking. Data science could overcome these problems, but computational methods are difficult to apply to multicomponent, amorphous, statistical copolymer materials for which little data exist. Here, leveraging a data set with 101 examples, we introduce a closed-loop experimental, machine learning (ML), and virtual screening strategy to enable predictions of the glass transition temperature (Tg) of polydicyclopentadiene (pDCPD) thermosets containing cleavable bifunctional silyl ether (BSE) comonomers and/or cross-linkers with varied compositions and loadings. Molecular features and formulation variables are used as model inputs, and uncertainty is quantified through model ensembling, which together with heavy regularization helps to avoid overfitting and ultimately achieves predictions within <15 °C for thermosets with compositionally diverse BSEs. This work offers a path to predicting the properties of thermosets based on their molecular building blocks, which may accelerate the discovery of promising plastics, rubbers, and composites with improved functionality and controlled deconstructability.
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Affiliation(s)
- Yasmeen
S. AlFaraj
- Department
of Chemistry, Massachusetts Institute of
Technology, Cambridge, Massachusetts 02139, United States of America
| | - Somesh Mohapatra
- Department
of Materials Science and Engineering, Massachusetts
Institute of Technology, Cambridge, Massachusetts 02139, United States of America
| | - Peyton Shieh
- Department
of Chemistry, Massachusetts Institute of
Technology, Cambridge, Massachusetts 02139, United States of America
| | - Keith E. L. Husted
- Department
of Chemistry, Massachusetts Institute of
Technology, Cambridge, Massachusetts 02139, United States of America
| | - Douglass G. Ivanoff
- Department
of Materials Science and Engineering, University
of Illinois at Urbana—Champaign, Urbana, Illinois 61801, United States of America
- The
Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana—Champaign, Urbana, Illinois 61801, United States
of America
| | - Evan M. Lloyd
- The
Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana—Champaign, Urbana, Illinois 61801, United States
of America
- Department
of Chemistry, University of Illinois at
Urbana—Champaign, Urbana, Illinois 61801, United States of America
| | - Julian C. Cooper
- The
Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana—Champaign, Urbana, Illinois 61801, United States
of America
- Department
of Chemistry, University of Illinois at
Urbana—Champaign, Urbana, Illinois 61801, United States of America
| | - Yutong Dai
- Department
of Chemistry, Massachusetts Institute of
Technology, Cambridge, Massachusetts 02139, United States of America
| | - Avni P. Singhal
- Department
of Materials Science and Engineering, Massachusetts
Institute of Technology, Cambridge, Massachusetts 02139, United States of America
| | - Jeffrey S. Moore
- Department
of Materials Science and Engineering, University
of Illinois at Urbana—Champaign, Urbana, Illinois 61801, United States of America
- The
Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana—Champaign, Urbana, Illinois 61801, United States
of America
| | - Nancy R. Sottos
- Department
of Materials Science and Engineering, University
of Illinois at Urbana—Champaign, Urbana, Illinois 61801, United States of America
- The
Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana—Champaign, Urbana, Illinois 61801, United States
of America
| | - Rafael Gomez-Bombarelli
- Department
of Materials Science and Engineering, Massachusetts
Institute of Technology, Cambridge, Massachusetts 02139, United States of America
| | - Jeremiah A. Johnson
- Department
of Chemistry, Massachusetts Institute of
Technology, Cambridge, Massachusetts 02139, United States of America
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5
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Himanshu, Chakraborty K, Patra TK. Developing efficient deep learning model for predicting copolymer properties. Phys Chem Chem Phys 2023; 25:25166-25176. [PMID: 37712405 DOI: 10.1039/d3cp03100d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/16/2023]
Abstract
Deep learning models are gaining popularity and potency in predicting polymer properties. These models can be built using pre-existing data and are useful for the rapid prediction of polymer properties. However, the performance of a deep learning model is intricately connected to its topology and the volume of training data. There is no facile protocol available to select a deep learning architecture, and there is a lack of a large volume of homogeneous sequence-property data of polymers. These two factors are the primary bottleneck for the efficient development of deep learning models for polymers. Here we assess the severity of these factors and propose strategies to address them. We show that a linear layer-by-layer expansion of a neural network can help in identifying the best neural network topology for a given problem. Moreover, we map the discrete sequence space of a polymer to a continuous one-dimensional latent space using a feature extraction technique to identify minimal data points for training a deep learning model. We implement these approaches for two representative cases of building sequence-property surrogate models, viz., the single-molecule radius of gyration of a copolymer and copolymer compatibilizer. This work demonstrates efficient methods for building deep learning models with minimal data and hyperparameters for predicting sequence-defined properties of polymers.
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Affiliation(s)
- Himanshu
- Department of Chemical Engineering and Center for Atomistic Modeling and Materials Design, Indian Institute of Technology Madras, Chennai, TN 600036, India.
| | - Kaushik Chakraborty
- Department of Chemical Engineering and Center for Atomistic Modeling and Materials Design, Indian Institute of Technology Madras, Chennai, TN 600036, India.
| | - Tarak K Patra
- Department of Chemical Engineering and Center for Atomistic Modeling and Materials Design, Indian Institute of Technology Madras, Chennai, TN 600036, India.
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6
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Lu S, Jayaraman A. Pair-Variational Autoencoders for Linking and Cross-Reconstruction of Characterization Data from Complementary Structural Characterization Techniques. JACS AU 2023; 3:2510-2521. [PMID: 37772182 PMCID: PMC10523369 DOI: 10.1021/jacsau.3c00275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 07/11/2023] [Accepted: 07/11/2023] [Indexed: 09/30/2023]
Abstract
In materials research, structural characterization often requires multiple complementary techniques to obtain a holistic morphological view of a synthesized material. Depending on the availability and accessibility of the different characterization techniques (e.g., scattering, microscopy, spectroscopy), each research facility or academic research lab may have access to high-throughput capability in one technique but face limitations (sample preparation, resolution, access time) with other technique(s). Furthermore, one type of structural characterization data may be easier to interpret than another (e.g., microscopy images are easier to interpret than small-angle scattering profiles). Thus, it is useful to have machine learning models that can be trained on paired structural characterization data from multiple techniques (easy and difficult to interpret, fast and slow in data collection or sample preparation) so that the model can generate one set of characterization data from the other. In this paper we demonstrate one such machine learning workflow, Pair-Variational Autoencoders (PairVAE), that works with data from small-angle X-ray scattering (SAXS) that present information about bulk morphology and images from scanning electron microscopy (SEM) that present two-dimensional local structural information on the sample. Using paired SAXS and SEM data of newly observed block copolymer assembled morphologies [open access data from Doerk G. S.; et al. Sci. Adv.2023, 9 ( (2), ), eadd3687], we train our PairVAE. After successful training, we demonstrate that the PairVAE can generate SEM images of the block copolymer morphology when it takes as input that sample's corresponding SAXS 2D pattern and vice versa. This method can be extended to other soft material morphologies as well and serves as a valuable tool for easy interpretation of 2D SAXS patterns as well as an engine for generating ensembles of similar microscopy images to create a database for other downstream calculations of structure-property relationships.
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Affiliation(s)
- Shizhao Lu
- Department
of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware 19716, United States
| | - Arthi Jayaraman
- Department
of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware 19716, United States
- Department
of Materials Science and Engineering, University
of Delaware, Newark, Delaware 19716, United
States
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7
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Tao L, He J, Arbaugh T, McCutcheon JR, Li Y. Machine learning prediction on the fractional free volume of polymer membranes. J Memb Sci 2023. [DOI: 10.1016/j.memsci.2022.121131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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8
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Tao L, Arbaugh T, Byrnes J, Varshney V, Li Y. Unified machine learning protocol for copolymer structure-property predictions. STAR Protoc 2022; 3:101875. [PMID: 36595914 PMCID: PMC9700038 DOI: 10.1016/j.xpro.2022.101875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Revised: 10/06/2022] [Accepted: 11/01/2022] [Indexed: 11/23/2022] Open
Abstract
Structure-property relationships are extremely valuable when predicting the properties of polymers. This protocol demonstrates a step-by-step approach, based on multiple machine learning (ML) architectures, which is capable of processing copolymer types such as alternating, random, block, and gradient copolymers. We detail steps for necessary software installation and construction of datasets. We further describe training and optimization steps for four neural network models and subsequent model visualization and comparison using training and test values. For complete details on the use and execution of this protocol, please refer to Tao et al. (2022).1.
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Affiliation(s)
- Lei Tao
- Department of Mechanical Engineering, University of Connecticut, Storrs, CT 06269, USA
| | - Tom Arbaugh
- Department of Physics, Wesleyan University, Middletown, CT 06459, USA
| | | | - Vikas Varshney
- Materials and Manufacturing Directorate, Air Force Research Laboratory, Wright-Patterson Air Force Base, Dayton, OH 45433, USA
| | - Ying Li
- Department of Mechanical Engineering, University of Connecticut, Storrs, CT 06269, USA,Department of Mechanical Engineering, University of Wisconsin-Madison, Madison, WI 53706-1572, USA,Corresponding author
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