A Model Ensemble Approach Enables Data-Driven Property Prediction for Chemically Deconstructable Thermosets in the Low-Data Regime

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 (T_g) 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.

Synopsis

A machine learning model that predicts T_g for deconstructable thermosets with varied bifunctional silyl ether comonomer loadings and structures is introduced and experimentally validated.

Introduction

Materials discovery is often cited as a bottleneck in addressing time-critical challenges in medicine, sustainable chemistry, and engineering design. (1−3) Even when a class of materials is found to be promising for a given application, traditional experimental approaches for screening can be arduous, costly, and inefficient for zeroing in on a desired combination of properties. Recent computational advances in molecular representations, machine learning architectures like graph neural networks, (1,4,5) and high-throughput virtual screening have enabled data-driven materials discovery in specific materials contexts (e.g., for molecular crystals, organic light-emitting diodes, (6) or zeolite catalysts); (7−10) however, these methods remain difficult to apply to other common classes of materials such as compositionally complex, amorphous polymers and polymer networks. For example, while curated databases like PoLyInfo, (11) Polymer Genome, (12) or the PI1M library (13) have allowed researchers to train machine learning models to predict the glass transition temperature (T_g), (14−16) thermal conductivity, (17) dielectric constant, (18) crystallization tendency, (19) and bandgap (20) for linear homopolymers (i.e., polymers of a single composition) and some copolymers, (21−25) there is little curated data available for three-dimensional (co)polymers such as thermosets. (26−29) Representing the chemical and topological diversity of thermosets in a way that enables extrapolation to new (co)monomers and cross-linking patterns is a challenge to ML strategies that are typically overfit to known monomer chemistries and linear architectures.

Thermosets are covalently cross-linked, three-dimensional polymer networks that irreversibly adopt a given shape upon curing. These materials make up ∼20% of manufactured polymers today, and they are often designed for high-temperature, chemically harsh, and/or mechanically extreme environments, e.g., in the aerospace industry, automotive design, renewable energy, and marine applications. (30−32) Thus, by their very nature, they are difficult to chemically deconstruct; most are mechanically downcycled, incinerated, or disposed of in landfills, where they persist for decades/centuries. The strategic introduction of cleavable bonds at the time of manufacturing could enable the production of deconstructable thermosets with properties on par with existing materials yet new end-of-life options. (33−40)

We have leveraged cleavable comonomers containing bifunctional silyl ethers (BSEs) as additives to facilitate the manufacturing of deconstructable variants of the industrial thermoset polydicyclopentadiene (pDCPD) (Figure 1A). (41−47) BSEs are versatile cleavable bonds for deconstructable thermosets, as they are easy to synthesize from low-cost components (e.g., diols and dichlorosilanes), they feature thermally and oxidatively stable Si–O bonds, and they have diverse mechanisms for selective Si–O bond cleavage. (42−46,48) Moreover, the properties of BSEs are closely linked to their Si substituents, of which there are hundreds of possible combinations that are synthesizable from commercially available reagents (e.g., a search of the keyword “dichlorosilane” on the Gelest web-catalog returns >100 unique products). This advantage also raises key challenges: it is impractical to synthesize and experimentally test all possible BSEs and their combinations experimentally, and it is difficult to predict through intuition alone how BSE comonomer structures and loadings will impact the properties of thermosets.

[Image omitted: See PDF]

Here, we propose a closed-loop strategy to overcome the challenges associated with applying data science to thermosets in the low-data regime. In particular, we focus on mapping the choice of BSE-based comonomer and/or cross-linker to T_g, a critical property of thermosets that determines their operating temperature range. Our strategy addresses two main challenges: (i) the unavailability of structure-based representations, by representing only the chemistry of the precursors, and (ii) the scarcity of data, by training heavily regularized model ensembles that enable uncertainty quantification and less noisy predictions.

Linear homopolymers or ordered copolymers have well-defined chemical structures and hence can be represented through an ordered sequence of per-monomer chemical features, or through graph convolutional neural networks acting on the whole repeat unit. (49) 3D stochastic polymers, however, cannot be easily represented as the numerical vectors that ML models require as inputs. Recently, hierarchical ML models have been applied to tackle the representation of 3D networks of known structure or stochastic linear polymers, but no tools are available for polymers of complex, stochastic, and unknown topologies. (50−53) Our approach relies on representing the individual chemical precursors as small molecules through low-dimensional physicochemical features, such as hydrophobicity and partial charges. Doing so captures domain-specific heuristics, lowers the dimensionality of the chemical input space, and most importantly avoids the need to represent the unknown network topology of the polymer. We also utilize model ensembling, that is, using the consensus prediction from multiple models instead of single prediction, to increase the transferability of our predictions to inputs away from the training data. (47) We combine ensembling over random seeds for model initialization, over data subsets (bagging, boosting), and over hyperparameter choices, i.e., varying the overall architecture of the models themselves, combining different classes of regressors from linear models to decision trees and support vector machines (Figure 1B). (54) A key advantage of model ensembles, which are widely used in the general ML literature but less so in the predictive design of three-dimensional polymer networks, is that each individual model’s prediction tends to differ when predicting far from the training data, so their variance can be used as a measure of uncertainty and their mean will provide higher accuracy and more robustness than any particular member of the ensemble. (55−58)

We show that our resulting model, which is trained on only 101 data points (i.e., the “low data regime”) taken from the literature and results from our combined laboratories, enables predictions of how BSE-based cleavable comonomer and cross-linker loadings and Si substituents impact the T_g of deconstructable pDCPD thermosets within ±15 °C experimental accuracy across a wide temperature range (0–220 °C). Thus, this work provides a route to predict the impacts of cleavable comonomer/cross-linker composition and loading on the T_g of deconstructable pDCPD thermosets and outlines a framework for predictive (co)polymer and polymer network design in the low-data regime.

Results and Discussion

Data Collection for the Machine Learning Model

Robust T_g prediction for deconstructable pDCPD requires a training data set with information on the chemical compositions of cleavable comonomers and cross-linkers as well as the thermoset curing parameters. To build an appropriate training data set, we collected data from existing literature examples and in-house experiments (Figure 2A, Figures S1–S87, Tables S1–S3), (59−66) providing 101 different combinations of cleavable additives (9 BSE-based and 1 acetal-based), ratios of comonomers and/or cross-linkers to the curing initiator, and the initiator type (first- or second-generation Grubbs catalyst), along with their experimentally measured T_g values obtained from either differential scanning calorimetry (DSC) or estimated by dynamic mechanical analysis (DMA) after curing. For samples that displayed multiple tan(δ) peaks, which occurred in some cases at high comonomer loading or with non-BSE-based monomers (i.e., acetal-based), we used the global maximum of tan(δ) as the T_g for training our model. (43,46)

[Image omitted: See PDF]

Machine Learning Model Ensemble Architecture for Low-Data Regime Prediction

Machine learning models for materials design are often trained to map synthesis parameters and affordable, high-information descriptors, which can be obtained for materials before they are synthesized, to measured experimental target properties. (67,68) Because important synthetic details for polymers are missing in publicly available data sets such as PoLyInfo, (11) data-driven prediction models struggle to reach high accuracy despite the availability of over 5000 data points. For example, a recent review benchmarking machine learning models to predict T_g values for homopolymers reported an accuracy of only ±39 °C. (14) Ramprasad and co-workers recently reported an error of 29 °C when combining a total of 9500 homopolymer and copolymer data points, combining data ensembles and a metalearner model using only chemical formulas as inputs. (21,57) Moreover, limitations in materials data accessibility often result in lower predictive accuracy as a result of overfitting to the available training data. For example, Hu et al. applied a materials genome strategy to control the mechanical properties of cross-linked epoxies. This approach requires the definition of “gene” substructures of interest (epoxides and amines in this case) and uses a graph convolution network to extrapolate information about these small molecules. Subsequently, transfer learning was employed alongside polymer descriptors including molecular weight and an estimated cross-linking density to predict the properties of the bulk material. They provided experimental validation of one prediction and molecular dynamics simulations of additional ones. The models struggled with overfitting and with extrapolation due to high performance metrics, with both ML predictions and validations being close to the mean performance of the training data. (69)

In this work, we instead utilize model ensembles across data splits and architectural choices to achieve robust predictions for thermoset copolymers in the low-data regime using chemical features such as physicochemical descriptors, as well as structural features based on molecular connectivity, as inputs. The model ensemble has the following independent variables: cleavable BSE comonomer and cross-linker representations and respective concentrations in mol %, monomer to initiator ratio, and initiator type. The cleavable comonomers were represented as extended connectivity fingerprints, which encode the chemical substructures in a barcode-like representation unique to each molecule. (70−72) This approach captures both the common and unique attributes of the individual molecules. The cleavable comonomer mol % and monomer to initiator ratios were represented as numbers, while the initiator type was treated as a categorical variable. Another model was trained with the cleavable comonomers represented with physicochemical descriptors, such as electro-topological state indices (ESI), partial charge, number of carbocycles and saturated rings, to benchmark the fingerprint representations (see Supporting Information Figures S88 and S89, Table S1).

As noted above, many machine learning models require large data sets─often more than 1000 data points─which poses a major challenge in materials research. (73,74) Cross-validation and data-ensembling are typically used to help overfitting to a singular section of the data set in the process. (74) To enable the robust prediction of T_g for pDCPD thermosets in the low-data regime, we used a model ensemble consisting of multiple models with different architectures and hyperparameters, i.e., parameters that define the model architecture and cannot be estimated from the data. The model ensemble consisted of 5 hyperparameter-optimized model architectures, each trained on 10 random splits of the data set, for a total of 50 individual models. For each model, 60% of the data were used for training, 20% were used to validate the model during training, and 20% were held-out to test the model ensemble after training. (75−79) The top 5 model architectures were chosen based on the least root mean squared error (RMSE) on the test data set after running a similar experiment for more than 10 model architectures, including Gaussian process regression, decision trees, linear models, and multilayer perceptrons. For the final model ensemble, the RMSE of the test set was 0.30 of the standard deviation of the training data, or 14.9 °C (Figure 2B). It is noteworthy that the model exhibited lower error and uncertainty around 150 °C, which is an expected feature, as the glass transition of deconstructable pDCPD thermosets in the training set primarily exhibit T_g values close to this value. These results suggest that the model ensemble was able to balance the bias-variance trade-off, with a mixture of underfitting high bias and low variance simpler models and overfitting low bias and high variance complex models. By focusing on one class of highly diverse compounds, our approach achieves lower error than large models trained on thousands of data points across homo- and copolymer space, which have errors closer to 30 °C or higher. (14,21)

In addition to the fingerprint representation for the cleavable comonomers and cross-linkers, we benchmarked the descriptor representation. The descriptor-based model ensemble performed slightly worse than fingerprint-based models, with test set RMSE of 0.35 of the standard deviation of the training data, or 17.5 °C (see Supporting Information Figure S90). The fingerprint-based models depend on the substructures and their implicit influence on the chemical properties of the molecules to map the molecules to their properties, while the descriptor-based models explicitly provide information about the different chemical properties of the molecules in the model. The fingerprint representation could be easily obtained for new molecules; however, descriptors might not be readily available and may require experiments or quantum chemical calculations. Additionally, the hand-crafted list of descriptors might not always include all possible features that affect the property of interest, while fingerprints do not have such a problem.

To understand the decision-making process of the model ensemble, we analyzed the importance of input features for prediction of T_g in both the fingerprint- and descriptor-based models through so-called attribution techniques (Figure 2C, see Supporting Information Figures S88 and S89). Cleavable comonomer mol %, initiator type, and monomer to initiator ratios were observed to be important across both models. The importance attributed to these features is in line with known experimental observations that high concentrations of BSE-based cleavable comonomers lead to decreases in T_g. The fingerprint-based models associated higher T_g with aryl substituents and norbornene-based cross-linkers. We interpret this prediction to the possibility that aryl substituents may induce stronger noncovalent interactions between cleavable comonomers, while the norbornene-based cross-linkers introduce rigid, inflexible structures that decrease the free volume. The descriptor-based models, which rely on physicochemical features rather than molecular graphs, highlighted the number of aliphatic and aryl rings as being critical for T_g. The Electrotopological State Index, a combined structure and electronic descriptor commonly used in pharmaceuticals, (77) and partial charges were also noted as key features, indicating the influence of the overall molecular topology and electronic charge distribution on T_g. These observations help in elucidating design principles for cleavable comonomers in high-T_g pDCPD thermosets.

T_g Prediction for Mixtures of BSE-Based Cleavable Comonomers and Cross-Linkers

We previously showed that treatment of iPrSi-based pDCPD thermosets with a fluoride or acid source can facilitate deconstruction of the thermoset to soluble products, while networks containing cleavable cross-linkers show a decline in cross-linking density without solubilization under similar cleavage conditions (Figure 3A). (43) Here, we first assessed our model ensemble’s performance using mixtures of iPrSi8, which was present in the training set at different mol % values, and the cleavable cross-linker didicyclopentadiene methyl silyl ether (DDMS), which was assessed at a fixed loading (10 mol %) and thus comprised one data point in the training set (Figure 3A, Figures S79–81 and S91–S107, Tables S3 and S4). iPrSi8 copolymerizes with DCPD to introduce cleavable bonds within the strands of the cross-linked pDCPD network without introducing any new cross-links, thereby lowering the cross-linking density of the material and the T_g. (43) By contrast, DDMS introduces new cleavable cross-links, leading to an overall increased cross-linking density that is expected to increase T_g. Here, we sought to combine iPrSi8 and DDMS to enable pDCPD deconstruction while also tuning the cross-link density. Figure 3B shows predicted T_g values from our fingerprint-based model ensemble as a function of the mol % of DDMS and iPrSi. As expected, increasing the mol % of iPrSi8 lowers T_g (x-axis, blue), while increasing DDMS incorporation raises T_g (y-axis, red) relative to virgin pDCPD (lower left corner). To test the validity of these predictions, we synthesized pDCPD thermosets bearing 0–20 mol % iPrSi8 and 10–20 mol % DDMS and estimated their T_g values as the peaks in the tan(δ) curves as measured by DMA. The predicted and experimental results were within the expected confidence interval as indicated by the standard deviation of the machine learning model, suggesting that the model captures key aspects of how the molecular features of iPrSi8 and DDMS impact the T_g of these thermosets (Figure 3C). For example, in a sample comprising 10% iPrSi8 and 20% DDMS, the measured T_g value was 152 ± 6 °C, which agrees well with the predicted T_g value of 158 ± 17 °C. Full chemical deconstructability of this sample was confirmed by exposure to tetrabutylammonium fluoride (TBAF) in tetrahydrofuran (THF) at 50 °C for 24 h. Thus, this predicted combination of iPrSi8 and DDMS provides a pDCPD thermoset that is chemically deconstructable, with a T_g that is 13 °C greater than our previously reported deconstructable pDCPD based on iPrSi8 alone and close to the value of virgin pDCPD (166 ± 5 °C).

[Image omitted: See PDF]

Screening, Identifying, And Experimentally Validating Predicted Properties of New BSE-Based Cleavable Comonomers

Having shown that our model can predict the T_g values of pDCPD thermosets using various concentrations of iPrSi8 mixed with DDMS, we sought to explore how the Si substituents in BSE-based cleavable comonomers could be leveraged to tune this property further. BSE comonomers such as iPrSi8 can be readily prepared through substitution reactions of an appropriate diol (e.g., (Z)-pent-2-ene-1,5-diol or (Z)-but-2-ene-1,4-diol) and a dichlorosilane (SiCl₂R₂) (Figure 4A). As noted above, there are over 100 commercially available dichlorosilanes; synthesizing all possible BSE comonomers to study their effect on the T_g of pDCPD would be impractical. Thus, we used the same model described above to screen a library of BSE comonomer candidates that could, in principle, be synthesized from commercially available starting materials (Figure 4A). Our model suggested three BSEs, which were not in the training data set, with desirable predicted T_g values: the previously reported comonomer PhSi7 and two novel BSE comonomers LinF7 and PFP7, which were synthesized on the ∼2 g scale through cyclization of the corresponding dichlorosilane with (Z)-but-2-ene-1,4-diol in the presence of imidazole in dichloromethane (Figures S108–S114). ¹H NMR, ¹³C NMR, and high-resolution mass spectrometry confirmed the structures of these comonomers. The thermal stabilities of pDCPD thermosets formed with 10% BSE comonomer loadings were assessed using thermogravimetric analysis (TGA) prior to thermomechanical characterization. Sufficient stability at the elevated temperatures required for DMA analysis was observed for materials containing PhSi7 and LinF7, but not PFP7 (Figure S127), and hence all subsequent experimental studies were carried out on samples containing LinF7 and PhSi7. Deconstructable pDCPD samples (Figure 4B) were prepared by mixing 10 mol % of each BSE comonomer into neat DCPD, followed by addition of Grubbs second-generation catalyst at a final concentration of 2 mg/mL (∼3100:1 monomer to catalyst ratio) and curing in an oven at 120 °C for 30 min. Samples with 10 mol % of each comonomer along with 10 or 20 mol % DDMS cross-linker were also prepared for comparison (Tables S4 and S5). T_g values were determined experimentally for each sample through DMA analysis (Figure 4C, Figures S116–S136) for comparison to our model predictions.

[Image omitted: See PDF]

The measured T_g value for each pDCPD sample containing 10 mol % LinF7 was within the expected error as presented by the model’s standard deviation for the predicted T_g (predicted, 141 ± 19 °C; experimentally measured, 143 ± 1 °C). Agreement between the prediction and experiment was also observed for LinF7 samples containing DDMS. For example, samples with 10 mol % LinF7 and 10 mol % DDMS had a predicted T_g of 174 ± 10 °C and an experimental T_g within the predicted error at 166 ± 4 °C. This T_g value is ∼20 °C greater than that of iPrSi8 samples loaded with similar concentrations of DDMS, showing that our method not only accurately predicts T_g values in these systems but also can be used to fine-tune and explore molecular subspaces to provide deconstructable thermosets with improved thermomechanical properties. Finally, we were interested in targeting deconstructable thermosets with predicted T_g values similar to that of virgin, nondeconstructable pDCPD. Our model predicted a T_g of 180 ± 9 °C for samples containing 10% LinF7 and 20% DDMS. Experimental verification showed that these samples did match the T_g of virgin pDCPD (167 ± 5 °C), with T_g values of 170 ± 1 °C.

Predictions for samples containing PhSi7 deviated more than justified by the model variance, suggesting a model bias. Nevertheless, the predictions remained within reasonable agreement with experiment, particularly compared to existing computational methods. For example, for thermosets containing 10 mol % PhSi7, the predicted T_g was 118 ± 6 °C, while the experimental value was 127 ± 2 °C. Additionally, for thermosets containing 10% PhSi7 and 20% DDMS, the predicted T_g was 174 ± 6 °C, while the experimentally observed value was 153 ± 3 °C. (Figure 4D).

Finally, deconstruction of the samples containing the newly identified BSE-based cleavable comonomers into soluble fragments was confirmed upon treatment with TBAF for 24 h (Supporting Information Figures S137 and S138). For both materials, >97% of the mass of the puck fully dissolved to yield soluble fragments (Figure 4B). NMR spectroscopic analysis of the soluble fragments isolated from the deconstructed materials confirmed their composition (Figures S139–S142). We note that while LinF7 was identified by the model to be a promising CCA based on T_g, its use could result in the release of perfluoroalkylated substance (PFAS) waste. This fact highlights that our model is designed to predict a specific property (T_g); the potential environmental ramifications of any predicted additives should also be considered in parallel with the property prediction before translation to real-world applications. With more data, models like ours could incorporate these considerations and others like cost in the future.

Retrospective Prediction for Strand-Cleaving Cross-Linkers (SCCs)

In an effort to further assess our model’s performance, we conducted a retrospective analysis to predict the T_g values of a different class of previously reported cleavable additives for pDCPD that we refer to as “strand-cleaving crosslinkers” (SCCs, Figure 5), which were not in the training data. (46) The model, despite having never seen any of these hybrid molecules, predicts T_g values comparable to the experimental values for all but one composition: a 100% v/v % NbMeSi thermoset that lacks DCPD. Given that this material is not a pDCPD thermoset, it is perhaps not surprising that our model inaccurately predicts its properties. Additionally, the prediction uncertainty for this sample, as quantified from the ensemble variance, was much higher than all other cases, demonstrating that the model exhibited a very low confidence in the predicted T_g.

[Image omitted: See PDF]

Conclusions

Herein, we have demonstrated the use of data-driven strategies to streamline the discovery of BSE-based cleavable comonomers and cross-linkers to manufacture deconstructable pDCPD thermosets with targetable T_g values. Experimental validation showed good agreement with model predictions of T_g values for cleavable comonomer and cross-linker structures that were not in the model’s training set. Importantly, this process allows for the deconstruction of pDCPD variants with predictable, tailorable properties. Moreover, when combined with recycling and upcycling schemes that leverage the rich functionality of pDCPD deconstruction fragments, (41,80) this approach could enhance the end-of-life options for pDCPD. Additionally, extension of these concepts to pDCPD composites may offer a powerful way to reuse the components of composites (e.g., fillers). (41,80)

This research also provides a framework to leverage closed-loop data-driven design and optimization in other areas where access to larger data sets may be limited, potentially allowing for the discovery of related deconstructable materials, including thermoplastics, other thermosets, and composites. Finally, extensions of this approach could enable predictions of additional thermomechanical properties for polymeric materials, such as elastic modulus, yield stress, and cross-link density, which could accelerate strategies for improving the functionality and sustainability of these materials.

Supporting Information

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acscentsci.3c00502.

Synthetic methods, characterization data, and machine learning model training and featurization data (PDF)

Terms & Conditions

Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.

Author Contributions

Indicates equal contributions to the manuscript.

Notes

The authors declare no competing financial interest.

Acknowledgments

This work was supported as part of the Center for Regenerative Energy-Efficient Manufacturing of Thermoset Polymeric Materials (REMAT), an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences under award no. DE-SC0023457.

References

1 Xie, T.; Grossman, J. C. Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties. Phys. Rev. Lett. 2018, 120 (14), 145301 DOI: 10.1103/PhysRevLett.120.145301

2 Tabor, D. P.; Roch, L. M.; Saikin, S. K.; Kreisbeck, C.; Sheberla, D.; Montoya, J. H.; Dwaraknath, S.; Aykol, M.; Ortiz, C.; Tribukait, H.; Amador-Bedolla, C.; Brabec, C. J.; Maruyama, B.; Persson, K. A.; Aspuru-Guzik, A. Accelerating the Discovery of Materials for Clean Energy in the Era of Smart Automation. Nat. Rev. Mater. 2018, 3 (5), 5– 20, DOI: 10.1038/s41578-018-0005-z

3 Pyzer-Knapp, E. O.; Pitera, J. W.; Staar, P. W. J.; Takeda, S.; Laino, T.; Sanders, D. P.; Sexton, J.; Smith, J. R.; Curioni, A. Accelerating Materials Discovery Using Artificial Intelligence, High Performance Computing and Robotics. npj Comput. Mater. 2022, 8 (1), 84, DOI: 10.1038/s41524-022-00765-z

4 Duvenaud, D.; Maclaurin, D.; Aguilera-Iparraguirre, J.; Gómez-Bombarelli, R.; Hirzel, T.; Aspuru-Guzik, A.; Adams, R. P. Convolutional Networks on Graphs for Learning Molecular Fingerprints. arXiv (Computer Science.Machine Learning) November 3, 2015, 1509.09292. DOI: 10.48550/arXiv.1509.09292 (accessed 2022-12-25).

5 Zaverkin, V.; Kästner, J. Exploration of Transferable and Uniformly Accurate Neural Network Interatomic Potentials Using Optimal Experimental Design. Mach. Learn.: Sci. Technol. 2021, 2 (3), 035009 DOI: 10.1088/2632-2153/abe294

6 Gómez-Bombarelli, R.; Aguilera-Iparraguirre, J.; Hirzel, T. D.; Duvenaud, D.; Maclaurin, D.; Blood-Forsythe, M. A.; Chae, H. S.; Einzinger, M.; Ha, D.-G.; Wu, T.; Markopoulos, G.; Jeon, S.; Kang, H.; Miyazaki, H.; Numata, M.; Kim, S.; Huang, W.; Hong, S. I.; Baldo, M.; Adams, R. P.; Aspuru-Guzik, A. Design of Efficient Molecular Organic Light-Emitting Diodes by a High-Throughput Virtual Screening and Experimental Approach. Nat. Mater. 2016, 15 (10), 1120– 1127, DOI: 10.1038/nmat4717

7 Gómez-Bombarelli, R.; Wei, J. N.; Duvenaud, D.; Hernández-Lobato, J. M.; Sánchez-Lengeling, B.; Sheberla, D.; Aguilera-Iparraguirre, J.; Hirzel, T. D.; Adams, R. P.; Aspuru-Guzik, A. Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules. ACS Cent. Sci. 2018, 4 (2), 268– 276, DOI: 10.1021/acscentsci.7b00572

8 Chen, H.; Engkvist, O.; Wang, Y.; Olivecrona, M.; Blaschke, T. The Rise of Deep Learning in Drug Discovery. Drug Discovery Today 2018, 23 (6), 1241– 1250, DOI: 10.1016/j.drudis.2018.01.039

9 Schwalbe-Koda, D.; Gómez-Bombarelli, R. Generative Models for Automatic Chemical Design. In Machine Learning Meets Quantum Physics; Schütt, K. T., Chmiela, S., von Lilienfeld, O. A., Tkatchenko, A., Tsuda, K., Müller, K.-R., Eds.; Lecture Notes in Physics, Vol. 968; 2020; pp 445– 467. DOI: 10.1007/978-3-030-40245-7_21 .

10 Schwalbe-Koda, D.; Kwon, S.; Paris, C.; Bello-Jurado, E.; Jensen, Z.; Olivetti, E.; Willhammar, T.; Corma, A.; Román-Leshkov, Y.; Moliner, M.; Gómez-Bombarelli, R. A Priori Control of Zeolite Phase Competition and Intergrowth with High-Throughput Simulations. Science 2021, 374 (6565), 308– 315, DOI: 10.1126/science.abh3350

11 Otsuka, S.; Kuwajima, I.; Hosoya, J.; Xu, Y.; Yamazaki, M. PoLyInfo: Polymer Database for Polymeric Materials Design. In Proceedings of the 2011 International Conference on Emerging Intelligent Data and Web Technologies, Tirana, Albania, September 7–9, 2011; IEEE, 2011; pp 22– 29. DOI: 10.1109/EIDWT.2011.13 .

12 Kim, C.; Chandrasekaran, A.; Huan, T. D.; Das, D.; Ramprasad, R. Polymer Genome: A Data-Powered Polymer Informatics Platform for Property Predictions. J. Phys. Chem. C 2018, 122 (31), 17575– 17585, DOI: 10.1021/acs.jpcc.8b02913

13 Ma, R.; Luo, T. PI1M: A Benchmark Database for Polymer Informatics. J. Chem. Inf. Model. 2020, 60 (10), 4684– 4690, DOI: 10.1021/acs.jcim.0c00726

14 Tao, L.; Varshney, V.; Li, Y. Benchmarking Machine Learning Models for Polymer Informatics: An Example of Glass Transition Temperature. J. Chem. Inf. Model. 2021, 61 (11), 5395– 5413, DOI: 10.1021/acs.jcim.1c01031

15 Zhang, Y.; Xu, X. Machine Learning Glass Transition Temperature of Polymers. Heliyon 2020, 6 (10), e05055 DOI: 10.1016/j.heliyon.2020.e05055

16 Tao, L.; Chen, G.; Li, Y. Machine Learning Discovery of High-Temperature Polymers. Patterns 2021, 2 (4), 100225 DOI: 10.1016/j.patter.2021.100225

17 Wu, S.; Kondo, Y.; Kakimoto, M.; Yang, B.; Yamada, H.; Kuwajima, I.; Lambard, G.; Hongo, K.; Xu, Y.; Shiomi, J.; Schick, C.; Morikawa, J.; Yoshida, R. Machine-Learning-Assisted Discovery of Polymers with High Thermal Conductivity Using a Molecular Design Algorithm. npj Comput. Mater. 2019, 5 (1), 66, DOI: 10.1038/s41524-019-0203-2

18 Chen, L.; Kim, C.; Batra, R.; Lightstone, J. P.; Wu, C.; Li, Z.; Deshmukh, A. A.; Wang, Y.; Tran, H. D.; Vashishta, P.; Sotzing, G. A.; Cao, Y.; Ramprasad, R. Frequency-Dependent Dielectric Constant Prediction of Polymers Using Machine Learning. npj Comput. Mater. 2020, 6 (1), 61, DOI: 10.1038/s41524-020-0333-6

19 Venkatram, S.; Batra, R.; Chen, L.; Kim, C.; Shelton, M.; Ramprasad, R. Predicting Crystallization Tendency of Polymers Using Multifidelity Information Fusion and Machine Learning. J. Phys. Chem. B 2020, 124 (28), 6046– 6054, DOI: 10.1021/acs.jpcb.0c01865

20 Batra, R.; Dai, H.; Huan, T. D.; Chen, L.; Kim, C.; Gutekunst, W. R.; Song, L.; Ramprasad, R. Polymers for Extreme Conditions Designed Using Syntax-Directed Variational Autoencoders. Chem. Mater. 2020, 32 (24), 10489– 10500, DOI: 10.1021/acs.chemmater.0c03332

21 Kuenneth, C.; Schertzer, W.; Ramprasad, R. Copolymer Informatics with Multitask Deep Neural Networks. Macromolecules 2021, 54 (13), 5957– 5961, DOI: 10.1021/acs.macromol.1c00728

22 Tao, L.; Byrnes, J.; Varshney, V.; Li, Y. Machine Learning Strategies for the Structure-Property Relationship of Copolymers. iScience 2022, 25 (7), 104585 DOI: 10.1016/j.isci.2022.104585

23 Patel, R. A.; Borca, C. H.; Webb, M. A. Featurization Strategies for Polymer Sequence or Composition Design by Machine Learning. Mol. Syst. Des. Eng. 2022, 7 (6), 661– 676, DOI: 10.1039/D1ME00160D

24 Reis, M.; Gusev, F.; Taylor, N. G.; Chung, S. H.; Verber, M. D.; Lee, Y. Z.; Isayev, O.; Leibfarth, F. A. Machine-Learning-Guided Discovery of 19 F MRI Agents Enabled by Automated Copolymer Synthesis. J. Am. Chem. Soc. 2021, 143 (42), 17677– 17689, DOI: 10.1021/jacs.1c08181

25 Wilbraham, L.; Sprick, R. S.; Jelfs, K. E.; Zwijnenburg, M. A. Mapping Binary Copolymer Property Space with Neural Networks. Chem. Sci. 2019, 10 (19), 4973– 4984, DOI: 10.1039/C8SC05710A

26 Jin, K.; Luo, H.; Wang, Z.; Wang, H.; Tao, J. Composition Optimization of a High-Performance Epoxy Resin Based on Molecular Dynamics and Machine Learning. Materials & Design 2020, 194, 108932 DOI: 10.1016/j.matdes.2020.108932

27 Pugar, J. A.; Childs, C. M.; Huang, C.; Haider, K. W.; Washburn, N. R. Elucidating the Physicochemical Basis of the Glass Transition Temperature in Linear Polyurethane Elastomers with Machine Learning. J. Phys. Chem. B 2020, 124 (43), 9722– 9733, DOI: 10.1021/acs.jpcb.0c06439

28 Yan, C.; Feng, X.; Wick, C.; Peters, A.; Li, G. Machine Learning Assisted Discovery of New Thermoset Shape Memory Polymers Based on a Small Training Dataset. Polymer 2021, 214, 123351 DOI: 10.1016/j.polymer.2020.123351

29 Meier, S.; Albuquerque, R. Q.; Demleitner, M.; Ruckdäschel, H. Modeling Glass Transition Temperatures of Epoxy Systems: A Machine Learning Study. J. Mater. Sci. 2022, 57 (29), 13991– 14002, DOI: 10.1007/s10853-022-07372-9

30 Quaranta, E.; Davies, P. Emerging and Innovative Materials for Hydropower Engineering Applications: Turbines, Bearings, Sealing, Dams and Waterways, and Ocean Power. Engineering 2022, 8, 148– 158, DOI: 10.1016/j.eng.2021.06.025

31 Verrey, J.; Wakeman, M. D.; Michaud, V.; Månson, J.-A. E. Manufacturing Cost Comparison of Thermoplastic and Thermoset RTM for an Automotive Floor Pan. Composites Part A: Applied Science and Manufacturing 2006, 37 (1), 9– 22, DOI: 10.1016/j.compositesa.2005.05.048

32 Hamerton, I.; Kratz, J. The Use of Thermosets in Modern Aerospace Applications. In Thermosets, 2nd ed.; Elsevier, 2018; pp 303– 340. DOI: 10.1016/B978-0-08-101021-1.00009-5 .

33 Di, J.; Reck, B. K.; Miatto, A.; Graedel, T. E. United States Plastics: Large Flows, Short Lifetimes, and Negligible Recycling. Resources, Conservation and Recycling 2021, 167, 105440 DOI: 10.1016/j.resconrec.2021.105440

34 Geyer, R.; Jambeck, J. R.; Law, K. L. Production, Use, and Fate of All Plastics Ever Made. Sci. Adv. 2017, 3 (7), e1700782 DOI: 10.1126/sciadv.1700782

35 Jehanno, C.; Alty, J. W.; Roosen, M.; De Meester, S.; Dove, A. P.; Chen, E. Y.-X.; Leibfarth, F. A.; Sardon, H. Critical Advances and Future Opportunities in Upcycling Commodity Polymers. Nature 2022, 603 (7903), 803– 814, DOI: 10.1038/s41586-021-04350-0

36 Law, K. L.; Narayan, R. Reducing Environmental Plastic Pollution by Designing Polymer Materials for Managed End-of-Life. Nat. Rev. Mater. 2022, 7 (2), 104– 116, DOI: 10.1038/s41578-021-00382-0

37 Plastics Give and Plastics Take. Nat. Rev. Mater. 2022, 7(2), 67– 67. DOI: 10.1038/s41578-022-00419-y .

38 Garcia, J. M.; Robertson, M. L. The Future of Plastics Recycling. Science 2017, 358 (6365), 870– 872, DOI: 10.1126/science.aaq0324

39 Körner, H.; Shiota, A.; Ober, C. K. Controlled-Order Thermosets for Electronic Packaging. In Imaging and Image Analysis Applications for Plastics; Pourdeyhimi, B., Ed.; Elsevier, 1999; pp 283– 287. DOI: 10.1016/B978-188420781-5.50035-8 .

40 Pesenti, T.; Nicolas, J. 100th Anniversary of Macromolecular Science Viewpoint: Degradable Polymers from Radical Ring-Opening Polymerization: Latest Advances, New Directions, and Ongoing Challenges. ACS Macro Lett. 2020, 9 (12), 1812– 1835, DOI: 10.1021/acsmacrolett.0c00676

41 Robertson, I. D.; Yourdkhani, M.; Centellas, P. J.; Aw, J. E.; Ivanoff, D. G.; Goli, E.; Lloyd, E. M.; Dean, L. M.; Sottos, N. R.; Geubelle, P. H.; Moore, J. S.; White, S. R. Rapid Energy-Efficient Manufacturing of Polymers and Composites via Frontal Polymerization. Nature 2018, 557 (7704), 223– 227, DOI: 10.1038/s41586-018-0054-x

42 Ma, S.; Webster, D. C. Degradable Thermosets Based on Labile Bonds or Linkages: A Review. Prog. Polym. Sci. 2018, 76, 65– 110, DOI: 10.1016/j.progpolymsci.2017.07.008

43 Shieh, P.; Zhang, W.; Husted, K. E. L.; Kristufek, S. L.; Xiong, B.; Lundberg, D. J.; Lem, J.; Veysset, D.; Sun, Y.; Nelson, K. A.; Plata, D. L.; Johnson, J. A. Cleavable Comonomers Enable Degradable, Recyclable Thermoset Plastics. Nature 2020, 583 (7817), 542– 547, DOI: 10.1038/s41586-020-2495-2

44 Shieh, P.; Hill, M. R.; Zhang, W.; Kristufek, S. L.; Johnson, J. A. Clip Chemistry: Diverse (Bio)(Macro)Molecular and Material Function through Breaking Covalent Bonds. Chem. Rev. 2021, 121 (12), 7059– 7121, DOI: 10.1021/acs.chemrev.0c01282

45 Cypryk, M.; Apeloig, Y. Mechanism of the Acid-Catalyzed Si–O Bond Cleavage in Siloxanes and Siloxanols. A Theoretical Study. Organometallics 2002, 21 (11), 2165– 2175, DOI: 10.1021/om011055s

46 Husted, K. E. L.; Shieh, P.; Lundberg, D. J.; Kristufek, S. L.; Johnson, J. A. Molecularly Designed Additives for Chemically Deconstructable Thermosets without Compromised Thermomechanical Properties. ACS Macro Lett. 2021, 10 (7), 805– 810, DOI: 10.1021/acsmacrolett.1c00255

47 Fort, S.; Hu, H.; Lakshminarayanan, B. Deep Ensembles: A Loss Landscape Perspective. arXiv (Statistics.Machine Learning) , June 24, 2020, 1912.02757, ver. 2. DOI: 10.48550/arXiv.1912.02757 (accessed 2022-12-25).

48 Brown, C. M.; Husted, K. E. L.; Wang, Y.; Kilgallon, L. J.; Shieh, P.; Zafar, H.; Lundberg, D. J.; Johnson, J. A. Thiol-triggered deconstruction of bifunctional silyl ether terpolymers via an SNAr-triggered cascade. Chem. Sci. 2023, accepted manuscript

49 Gurnani, R.; Kuenneth, C.; Toland, A.; Ramprasad, R. Polymer Informatics at Scale with Multitask Graph Neural Networks. Chem. Mater. 2023, 35 (4), 1560– 1567, DOI: 10.1021/acs.chemmater.2c02991

50 Mohapatra, S.; An, J.; Gómez-Bombarelli, R. Chemistry-Informed Macromolecule Graph Representation for Similarity Computation, Unsupervised and Supervised Learning. Mach. Learn.: Sci. Technol. 2022, 3 (1), 015028 DOI: 10.1088/2632-2153/ac545e

51 Aldeghi, M.; Coley, C. W. A Graph Representation of Molecular Ensembles for Polymer Property Prediction. Chem. Sci. 2022, 13 (35), 10486– 10498, DOI: 10.1039/D2SC02839E

52 Gu, Y.; Zhao, J.; Johnson, J. A. Polymer Networks: From Plastics and Gels to Porous Frameworks. Angew. Chem., Int. Ed. 2020, 59 (13), 5022– 5049, DOI: 10.1002/anie.201902900

53 Gu, Y.; Zhao, J.; Johnson, J. A. A (Macro)Molecular-Level Understanding of Polymer Network Topology. Trends in Chemistry 2019, 1 (3), 318– 334, DOI: 10.1016/j.trechm.2019.02.017

54 Wenzel, F.; Snoek, J.; Tran, D.; Jenatton, R. Hyperparameter Ensembles for Robustness and Uncertainty Quantification. arXiv (Computer Science.Machine Learning) , January 8, 2021, 2006.13570, ver. 3. DOI: 10.48550/arXiv.2006.13570 (accessed 2022-12-25).

55 Kondratyuk, D.; Tan, M.; Brown, M.; Gong, B. When Ensembling Smaller Models Is More Efficient than Single Large Models. arXiv (Computer Science.Machine Learning) , May 1, 2020, 2005.00570, ver. 1. DOI: 10.48550/arXiv.2005.00570 (accessed 2022-12-25).

56 Wang, X.; Kondratyuk, D.; Christiansen, E.; Kitani, K. M.; Alon, Y.; Eban, E. Wisdom of Committees: An Overlooked Approach To Faster and More Accurate Models. arXiv (Computer Science.Computer Vision and Pattern Recognition) , February 17, 2022, 2012.01988, ver. 6. DOI: 10.48550/arXiv.2012.01988 (accessed 2022-12-25).

57 Lobacheva, E.; Chirkova, N.; Kodryan, M.; Vetrov, D. On Power Laws in Deep Ensembles. arXiv (Computer Science.Machine Learning) , June 28, 2021, 2007.08483, ver. 2. DOI: 10.48550/arXiv.2007.08483 (accessed 2022-12-25).

58 Streeter, M. Approximation Algorithms for Cascading Prediction Models. arXiv (Computer Science.Machine Learning) , February 21, 2018, 1802.07697, ver. 1. DOI: 10.48550/arXiv.1802.07697 (accessed 2022-12-25).

59 Long, T. R.; Elder, R. M.; Bain, E. D.; Masser, K. A.; Sirk, T. W.; Yu, J. H.; Knorr, D. B.; Lenhart, J. L. Influence of Molecular Weight between Crosslinks on the Mechanical Properties of Polymers Formed via Ring-Opening Metathesis. Soft Matter 2018, 14 (17), 3344– 3360, DOI: 10.1039/C7SM02407J

60 Saha, S.; Ginzburg, Y.; Rozenberg, I.; Iliashevsky, O.; Ben-Asuly, A.; Gabriel Lemcoff, N. Cross-Linked ROMP Polymers Based on Odourless Dicyclopentadiene Derivatives. Polym. Chem. 2016, 7 (18), 3071– 3075, DOI: 10.1039/C6PY00378H

61 Elder, R. M.; Long, T. R.; Bain, E. D.; Lenhart, J. L.; Sirk, T. W. Mechanics and Nanovoid Nucleation Dynamics: Effects of Polar Functionality in Glassy Polymer Networks. Soft Matter 2018, 14 (44), 8895– 8911, DOI: 10.1039/C8SM01483C

62 Constable, G. S.; Lesser, A. J.; Coughlin, E. B. Morphological and Mechanical Evaluation of Hybrid Organic–Inorganic Thermoset Copolymers of Dicyclopentadiene and Mono- or Tris(Norbornenyl)-Substituted Polyhedral Oligomeric Silsesquioxanes. Macromolecules 2004, 37 (4), 1276– 1282, DOI: 10.1021/ma034989w

63 He, Z.; Sun, J.; Zhang, L.; Wang, Y.; Ren, H.; Bao, J.-B. Synergistic Reinforcing and Toughening of Polydicyclopentadiene Nanocomposites with Low Loadings Vinyl-Functionalized Multi-Walled Carbon Nanotubes. Polymer 2018, 153, 287– 294, DOI: 10.1016/j.polymer.2018.08.016

64 Knorr, D. B.; Masser, K. A.; Elder, R. M.; Sirk, T. W.; Hindenlang, M. D.; Yu, J. H.; Richardson, A. D.; Boyd, S. E.; Spurgeon, W. A.; Lenhart, J. L. Overcoming the Structural versus Energy Dissipation Trade-off in Highly Crosslinked Polymer Networks: Ultrahigh Strain Rate Response in Polydicyclopentadiene. Compos. Sci. Technol. 2015, 114, 17– 25, DOI: 10.1016/j.compscitech.2015.03.021

65 Rohde, B. J.; Le, K. M.; Krishnamoorti, R.; Robertson, M. L. Thermoset Blends of an Epoxy Resin and Polydicyclopentadiene. Macromolecules 2016, 49 (23), 8960– 8970, DOI: 10.1021/acs.macromol.6b01649

66 Kovačič, S.; Slugovc, C. Ring-Opening Metathesis Polymerisation Derived Poly(Dicyclopentadiene) Based Materials. Mater. Chem. Front. 2020, 4 (8), 2235– 2255, DOI: 10.1039/D0QM00296H

67 Kim, E.; Huang, K.; Saunders, A.; McCallum, A.; Ceder, G.; Olivetti, E. Materials Synthesis Insights from Scientific Literature via Text Extraction and Machine Learning. Chem. Mater. 2017, 29 (21), 9436– 9444, DOI: 10.1021/acs.chemmater.7b03500

68 Kim, E.; Huang, K.; Tomala, A.; Matthews, S.; Strubell, E.; Saunders, A.; McCallum, A.; Olivetti, E. Machine-Learned and Codified Synthesis Parameters of Oxide Materials. Sci. Data 2017, 4 (1), 170127 DOI: 10.1038/sdata.2017.127

69 Hu, Y.; Zhao, W.; Wang, L.; Lin, J.; Du, L. Machine-Learning-Assisted Design of Highly Tough Thermosetting Polymers. ACS Appl. Mater. Interfaces 2022, 14 (49), 55004– 55016, DOI: 10.1021/acsami.2c14290

70 Miccio, L. A.; Schwartz, G. A. From Chemical Structure to Quantitative Polymer Properties Prediction through Convolutional Neural Networks. Polymer 2020, 193, 122341 DOI: 10.1016/j.polymer.2020.122341

71 Rogers, D.; Hahn, M. Extended-Connectivity Fingerprints. J. Chem. Inf. Model. 2010, 50 (5), 742– 754, DOI: 10.1021/ci100050t

72 Landrum, G. RDKit: Open-Source Cheminformatics Software; T5 Informatics, 2016. https://www.rdkit.org/.

73 Himanen, L.; Geurts, A.; Foster, A. S.; Rinke, P. Data-Driven Materials Science: Status, Challenges, and Perspectives. Adv. Sci. 2019, 6 (21), 1900808 DOI: 10.1002/advs.201900808

74 Ramprasad, R.; Batra, R.; Pilania, G.; Mannodi-Kanakkithodi, A.; Kim, C. Machine Learning in Materials Informatics: Recent Applications and Prospects. npj Comput. Mater. 2017, 3 (1), 54, DOI: 10.1038/s41524-017-0056-5

75 Pedregosa, F.; Varoquaux, G.; Gramfort, A.; Michel, V.; Thirion, B.; Grisel, O.; Blondel, M.; Müller, A.; Nothman, J.; Louppe, G.; Prettenhofer, P.; Weiss, R.; Dubourg, V.; Vanderplas, J.; Passos, A.; Cournapeau, D.; Brucher, M.; Perrot, M.; Duchesnay, É. Scikit-Learn: Machine Learning in Python. arXiv (Computer Science.Machine Learning) , June 5, 2018, 1201.0490, ver. 4. DOI: 10.48550/arXiv.1201.0490 (accessed 2022-12-25).

76 Chen, T.; Guestrin, C. XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, August 13–17, 2016; ACM: New York, NY, 2016; pp 785– 794. DOI: 10.1145/2939672.2939785 .

77 Kier, L. B.; Hall, L. H. [No Title Found]. Pharm. Res. 1990, 07 (8), 801– 807, DOI: 10.1023/A:1015952613760

78 Jha, A.; Chandrasekaran, A.; Kim, C.; Ramprasad, R. Impact of Dataset Uncertainties on Machine Learning Model Predictions: The Example of Polymer Glass Transition Temperatures. Modelling Simul. Mater. Sci. Eng. 2019, 27 (2), 024002 DOI: 10.1088/1361-651X/aaf8ca

79 Miccio, L. A.; Schwartz, G. A. Localizing and Quantifying the Intra-Monomer Contributions to the Glass Transition Temperature Using Artificial Neural Networks. Polymer 2020, 203, 122786 DOI: 10.1016/j.polymer.2020.122786

80 Lloyd, E. M.; Cooper, J. C.; Shieh, P.; Ivanoff, D. G.; Parikh, N. A.; Mejia, E. B.; Husted, K. E. L.; Costa, L. C.; Sottos, N. R.; Johnson, J. A.; Moore, J. S. Efficient Manufacture, Deconstruction, and Upcycling of High-Performance Thermosets and Composites. ACS Appl. Eng. Mater. 2023, 1 (1), 477– 485, DOI: 10.1021/acsaenm.2c00115