Linear regression

Numerous ML algorithms have been developed for different types of learning purposes such as supervised learning, semi-supervised learning, and unsupervised learning. Supervised learning (i.e., predictive modeling) is the most widely used learning approach in scientific and engineering fields. Among all supervised learning algorithms, linear regression is the most basic one and has been studied and used extensively. Unlike other more complex (deep) ML models (e.g., NN and CNN), which are often being considered as “black box” models because of their complexity, a linear (regression) model has high interpretability of the relationship between input and output variables. The hypothesis of linear regression is:

(1)$$y = \; {\bf w}^{\rm T}{\bf x}$$

where w represents the learnable parameters or weights (including a bias w ₀), x represents the input variables (including a constant x ₀ for the bias term), and y represents the output variable (dependent) of the model. One common technique to estimate the loss (error) in a regression model is using the mean squared error (MSE), which is used to quantify how close or far the predictions are from the actual quantities. The MSE is computed as follows:

(2)$$E = \displaystyle{1 \over N}\mathop \sum \limits_{n = 1}^N \lpar {y_n-{\hat{y}}_n} \rpar ^2$$

where N is the number of data points (samples) used to calculate the MSE, y _n is the prediction of sample n (from ML models), and $\hat{y}_n$ is the actual quantity of the sample (ground truth). Once the error function of a model is defined, the weights of the model can be calculated by an optimization algorithm such as the classical stochastic gradient descent or the Adam optimization algorithm. Linear regression has been widely applied to various research problems because of its simplicity and high interpretability. For the applications in composite materials, Tiryaki et al. showed that linear regression can be applied to predict the compressive strength of heat-treated wood based on experimental data (Table I).^{[Reference Tiryaki and Aydın71]} In the authors’ work, the input variables include wood species, heat treatment temperature, and exposure time. The output variable is the compressive strength of heat-treated woods. Khademi et al. and Young et al. applied linear regression to predict concrete compressive strength, in which the input variables include experimentally measured concrete characteristics and mixture proportions (Table I).^{[Reference Khademi, Akbari, Jamal and Nikoo72,Reference Young, Hall, Pilon, Gupta and Sant73]} In those studies, in addition to the linear models, the authors also performed more complex ML models such as NN models for their regression tasks. The authors showed that more complex ML models, in general, offered more accurate predictions. However, in those studies, the linear models provided valuable information like which input variables (e.g., treatment conditions and material features) were more important (with higher influences) to the prediction (e.g., compressive strength). Note that linear regression assumes a linear relationship between input variables and the prediction. Most problems have different degrees of nonlinear characteristics and linear regression is not appropriate for highly nonlinear problems in which the relationship between the input and output variables cannot be approximated by a linear function. However, it is always a good idea to try linear regression (or other simple ML algorithms) first to see how difficult the problem is before applying other more complex ML algorithms.

Logistic regression

Unlike linear regression which is used for the prediction of continuous quantities, logistic regression is mostly used for the prediction of discrete class labels. Logistic regression predicts the probabilities for classification problems with two possible categories. A logistic function is used in logistic regression to force the output variable to be between 0 and 1. Thus, the output variable can be used to represent the probability for a sample being in a category:

(3)$$h = \; \displaystyle{{{\rm e}^{{\bf w}^{\rm T}{\bf x}}} \over {1 + {\rm e}^{{\bf w}^{\rm T}{\bf x}}}}$$

where w represents the weights and x represents the input variables of the model. Logistic regression is a generalized linear model since the predicted probabilities only depend on the weighted sum of the input variables. As only two categories are considered in logistic regression, 0.5 is used as the classification threshold. For logistic regression, the cross-entropy error is often used to estimate the error in the model:

(4)$$E = \; \displaystyle{1 \over N}\mathop \sum \limits_{n = 1}^N {\rm ln}\lpar {1 + {\rm e}^{-{\hat{y}}_n{\bf w}^{\rm T}{\bf x}_n}} \rpar $$

where N is the number of samples used to calculate the cross-entropy error, ${\bf x}_n$ represents the input variables of sample n, and $\hat{y}_n$ is the actual category of the sample (either 1 or −1). As mentioned above, logistic regression can only perform binary classification tasks. For multi-class classification tasks, Softmax regression, a generalization of logistic regression, is often applied, which extends logistic regression to multi-class classification.

Using logistic regression algorithms, Gu et al. presented pioneer work on applying ML to the design of composites for Mode I fracture (Table I).^{[Reference Gu, Chen and Buehler74]} Unlike the applications of using ML to predict the compressive strength of woods and concrete, in which experimental measurements were used as the training data,^{[Reference Tiryaki and Aydın71–Reference Young, Hall, Pilon, Gupta and Sant73]} Gu et al. performed finite element analysis to generate the training data for their composite design problems. Figure 2 shows the ML approach using logistic regression (i.e., linear model) for an 8 by 8 composite system in the authors’ work. The input variables represent the topology of composites, where two base materials (stiff and soft) were considered and denoted as 0 and 1. As can be seen in the figure, instead of using the actual performance (i.e., toughness and strength) of composites to train a regression model, the training samples were categorized into two classes, namely “good” design and “bad” design, and logistic regression was performed for the classification task. After the training process, the linear model not only could distinguish whether an unseen composite design was a good or bad design but also could estimate how good (or how bad) the composite design was by comparing the probabilities of being in each class (Fig. 2). This study demonstrated that ML could be applied to learn structure–property relationships of materials even when the performance of materials could not be accurately measured. This ML approach could be applied to other material modeling and design problems in which the performance of materials is difficult (or expensive) to measure in experiments or simulations. The authors showed that using ML to predict mechanical properties of composites is orders of magnitude faster than conventional finite element analysis. Moreover, although it was shown in the authors’ work that the prediction accuracy could be improved when using a more complex ML model such as CNN, the linear model provided high interpretability of the relationship between input and output variables. Thus, after the linear model is trained, the optimal designs (the designs with the highest toughness or strength) of composites could be generated directly by using the information of the weights in the linear model, without requiring any sampling, optimizing, or searching process. In the authors’ work, the linear model generated optimal designs of composites with the toughness and strength orders of magnitude higher than the training samples, and required a much less computational cost compared with exhaustive methods.

Figure 2.

Overall flow chart. The flow chart shows the ML approach using the linear model for an 8 by 8 system. The ML approach using the CNN model is similar to this flow chart but without the step of converting to 1-D arrays. Note that for a 16 by 16 system, the amount of input data, training data, and testing data are 1 million, 0.9 million, and 0.1 million, respectively. Reprinted with permission from Ref. Reference Gu, Chen and Buehler74. Copyright 2017 Elsevier.

Neural networks

Linear models assume that the relationship between input and output variables is linear. Thus, the learning capacity of linear models is limited. Although it is possible to increase the learning capacity by adding a few nonlinear terms (e.g., polynomials) to the hypothesis, it is not practical as there is an infinite number of nonlinear functions that can be chosen from and in general little is known which nonlinear functions are more suitable for our problems. An alternative approach to achieve high learning capacity is to use linear regression with an activation function to make an artificial neuron (perceptron) and connect a number of neurons to make an artificial NN. The most basic architecture of NNs is multilayer perceptron, which is also denoted to as NN here. It consists of multiple hidden layers (the layers except for the input and output layers) and each layer comprises a number of neurons. Nonlinear functions such as rectified linear unit and sigmoid are used as activation functions to introduce nonlinearity. The hidden layers in a NN are called the fully-connected layers as all the neurons in adjacent layers are fully connected. The output of the first layer goes into the second layer as input, and so on. On the basis of the universal approximation theorem, a NN with one hidden layer containing a finite number of neurons can approximate any continuous functions. In addition to NN, CNN is another widely used NN architecture, which is commonly applied to analyzing images and videos. The main building block in the CNN architecture is the convolution layer. The convolution layer comprises a number of convolution filters. Each filter is convolved with the input from the previous layer and generates feature maps, which are fed into the next layer as input. Compared with the fully-connected layers, this convolution operator can significantly reduce the number of weights needed in an ML model and makes the computation more efficient. The purpose of doing convolution is to extract relevant features from data. This feature learning ability is essential for image-related problems. In practice, CNN models usually outperform NN models on image classification tasks and many others.

NN and CNN have been applied to a few composite studies mostly for regression tasks. Qi et al. applied a NN model to predict the unconfined compressive strength of cemented paste backfill (Table I).^{[Reference Qi, Fourie and Chen75]} In the authors’ work, the training data were collected from experiments. The input variables include the tailing type, cement-tailings ratio, solids content, and curing time while the output variable is the unconfined compressive strength of specimens. The authors applied particle swarm optimization to tune the architecture (the numbers of hidden layers and neurons) of their NN model and showed that their NN model can accurately predict and calculate the unconfined compressive strength of cemented paste backfill. For composite modeling and design problems, the topology of a composite can naturally be represented as an image (2-D or 3-D). The unit cells or base materials of a composite are converted to pixels (with different values) of the corresponding image. Accordingly, all the advantages of CNN found in image-related tasks are valid when applying to composite problems. Yang et al. applied CNN models to predict the effective stiffness of 3-D composites (Table I).^{[Reference Yang, Yabansu, Al-Bahrani, Liao, Choudhary, Kalidindi and Agrawal70]} In the authors’ work, the training data were generated by 3-D Gaussian filters with different covariance matrixes. The performance of the training data was calculated by finite element analysis. The input variables include the topology of composites made up of two base materials (stiff and soft) and the output variable is the effective stiffness. Different architectures of CNN models were explored to tune hyperparameters (the numbers of layers and filters). The authors showed that their CNN models can produce highly accurate predictions of the effective stiffness of composites based on a given topology.

Recently, CNN has been employed to design hierarchical composites. Gu et al. applied CNN models to accelerate the design of bioinspired hierarchical composites (Table I).^{[Reference Gu, Chen, Richmond and Buehler66]} In the authors’ work, three unit cells made up of stiff and soft materials were used to create hierarchical composites. Those unit cells exhibited distinctive anisotropy responses because of different geometrical configurations of stiff and soft materials in the unit cells. The input variables represent the topology of hierarchical composites, where the three unit cells are denoted as 1, 2, and 3. Compared with using complete microstructural data with the geometrical configuration of all local elements (stiff and soft), using macrostructural data with the geometrical configuration of unit cells drastically reduces the data space. Thus, a less complex ML architecture and fewer training samples are needed for this learning task. The output variable is the toughness of hierarchical composites calculated using finite element analysis. The idea of using unit cells to represent the topology of composites is similar to the homogenization process commonly applied in estimating effective material properties of composites. However, the conventional homogenization process requires to extract effective (homogenized) parameters of unit cells based on a rigorous mathematical theory,^{[Reference Michel, Moulinec and Suquet76]} which is not required in the authors’ ML approach. Moreover, the fracture of composites occurs on the microstructural scale (local elements) and cannot be captured in a homogenized FEM model. Consequently, in homogenized finite element analysis, the localization process is required to convert the averaged strains of unit cells to the strains of local elements. In contrast, Gu et al. showed that the correlations between the geometrical configuration of unit cells (macrostructural data) and the corresponding toughness (microstructural property) can be learned by ML directly based on training data, without the need for conventional homogenization and localization processes. For this hierarchical composite problem, CNN was implemented to perform a regression task since linear regression was incapable of capturing the highly nonlinear correlations between the input and output variables. However, unlike linear models, CNN models (and most ML models) cannot generate optimal designs directly based on the values of weights. To overcome this limitation, Gu et al. further augmented CNN models to discover high-performing hierarchical composite designs with a self-learning algorithm. The concept of the self-learning algorithm is similar to that of the genetic algorithm. The sampling process consisted of many sampling loops. In each sampling loop, a large number of new composite designs (candidates) were fed into their CNN model and the model predicted the performance of those designs with a negligible computational cost. From there, high-performing candidates were identified and used to generate new candidates for the next sampling loop. The mechanical properties of the ML designs were then calculated using finite element analysis. As can be seen in Fig. 3, simulation results show that the ML designs are much tougher and stronger than the training samples. Most importantly, the simulation results were validated through additive manufacturing and experimental testing in the authors’ work. Note that knowing the optimal designs gives us an opportunity to probe design strategies and physical insights (Fig. 3) of how to create tougher and stronger hierarchical composites.

Figure 3.

ML-generated designs. (a) Strength and toughness ratios of designs computed from training data and ML output designs. Strength ratio is the strength normalized by the highest training data strength value. Toughness ratio is the toughness normalized by the highest training data toughness value. The ML output designs are shown from training loops of 1000 and 1,000,000. Envelopes show that ML material properties exceed those of training data. (b) Effects of learning time on ML models for minimum, mean, and maximum toughness ratio start to converge as training loops increase. (c) Microstructures from partitions A (lowest toughness designs in training data) and B (highest toughness designs from ML) in part (a) of the figure with the corresponding colors for unit cell blocks (blue = U1, orange = U2, yellow = U3). Also shown in the right-most columns for the designs A and B are the strain distributions, which show lower strain concentration at the crack tip for the ML-generated designs^{[Reference Gu, Chen, Richmond and Buehler66]}.

Recently, Hanakata et al. applied ML to accelerate the design of stretchable graphene kirigami, which is a patterning graphene sheet with cuts (Table I).^{[Reference Hanakata, Cubuk, Campbell and Park77]} In the authors’ work, NN and CNN models were applied to predict the yield stress and yield strain of graphene sheets based on the cutting pattern described by two unit cells. The input variables represent the topology of graphene sheets, where the two unit cells (with a cut and with no cut) are denoted as 0 and 1. The training data generated by MD and ML models was applied to perform a regression task to achieve accuracy close to the MD simulations. After the training process was completed, an iterative screening process was used to search for high-performing (high-yield strain) candidates. In each screening iteration, their CNN model was applied to search for high-performing candidates and the performance of those candidates was calculated using MD. The new simulation results were then added to the training data for the next iteration of training and screening processes. The authors showed that when more and more samples were used to train the CNN model, the yield strains of the high-performing candidates in each iteration were also improved.

Gaussian process

Although deep neural networks (e.g., NN and CNN) with many hidden layers and neurons, theoretically, can capture any complex patterns in data, they require a large amount of training data in order to learn the hidden patterns without overfitting. For some problems, simple ML algorithms cannot capture the complex patterns in data and generating a large amount of training data for deep learning algorithms is unfeasible due to the time and cost of conducting experiments or simulations. A GP, which is a non-parametric approach, serves as an alternative method for highly nonlinear problems. A GP is a collection of random variables and assumes that all input and output variables have joint Gaussian distributions. Instead of setting a hypothesis for an ML model and finding optimal values for the weights in the model, a GP produces a distribution of all possible functions that are consistent with the observed (training) data. Therefore, the number of parameters in a GP is unbounded and grows with the amount of training data. A GP is defined as:

(5)$$y = f\,({\bf x})\sim {\rm {\cal G}{\cal P}}(m({\bf x}),\; k({\bf x},{\bf {x}^{\prime}}))$$

where $m({\bf x})$ is the mean function and $k({\bf x},{\bf {x}^{\prime}})$ is the covariance function. When a GP model is applied to predict the output value for an unseen data point, it generates a Gaussian distribution with a covariance matrix produced by a kernel function (e.g., squared exponential) which measures the similarity between points. Therefore, a GP model not only can make a prediction for an unseen data point based on the training data but also can naturally quantify the uncertainty of the prediction. In general, the uncertainty of the prediction for an unseen data point increases when the point is away from the training data points. Although a GP is a powerful and elegant method with numerous applications in ML and statistics, the implementation requires a computational cost which grows as O(n ³), where n is the number of training data points. Thus, conventional GP models have been mostly applied to problems with a small amount of training data. There are a number of sparse GP techniques proposed to reduce the computational cost for large data sets.^{[Reference Quiñonero-Candela and Rasmussen78,Reference Snelson and Ghahramani79]}

Tapia et al. applied a GP model to predict the porosity in metallic parts produced by selective laser melting, which is a laser-based additive manufacturing process (Table I).^{[Reference Tapia, Elwany and Sang80]} In the authors’ work, the input variables are the laser power and laser scanning speed, which are two of the most influential processing parameters. The output variable is the porosity of specimens (17-4 PH stainless steel). A GP can be computationally very efficient when the amount of training data is small. The authors used the GP model to predict the porosity of specimens over the entire design space. Thus, the resulting porosity at any combination of the laser power and laser scanning speed was revealed. Recently, Hansoge et al. applied GP models to predict mechanical properties of assembled hairy nanoparticles (NPs) (Table I).^{[Reference Hansoge, Huang, Sinko, Xia, Chen and Keten81]} Assembled hairy NPs is a type of polymer nanocomposite with relatively regular spacing between particles. In the authors’ work, the input variables are the polymer chain length, grafting density, polymer–NP interaction strength, and NP-edge length. The output variable is the modulus or toughness of assembled hairy NPs. The training data was generated using coarse-grained molecular dynamics (CG-MD) and GP models were applied to perform regression tasks based on the CG-MD simulation results. The authors used the GP models (metamodel) to predict the modulus and toughness of assembled hairy NPs over the entire design space. Figure 4 shows the modulus and toughness obtained from the metamodel by sampling 1 million assembled hairy NPs with different combinations of input variables. As can be seen in the figure, the predicted modulus and toughness obtained from the metamodel and those obtained from the CG-MD simulations are very close.

Figure 4.

(a) Young's modulus versus toughness obtained from the metamodel. Pareto frontier obtained by sampling 1 million input parameters over the entire design space. (b) One hundred initial CG-MD designs (blue dots) are used to build the metamodel. Using the metamodel, a Pareto frontier (red curve) is obtained. Seven random points from the Pareto curve are chosen (purple squares) and tested by running CG-MD simulations (green diamonds). Comparison of (c) Young's modulus and (d) toughness obtained from the metamodel and CG-MD simulations. The error bars represent a 95% confidence interval. Reprinted with permission from Ref. Reference Hansoge, Huang, Sinko, Xia, Chen and Keten81. Copyright 2018 American Chemical Society.

Summary and future perspectives

This prospective paper presents recent progress in the applications of ML to composite materials. We give a brief overview of some basic ML algorithms and review recent studies using ML models to predict mechanical properties of composites. In those studies, ML models were applied to approximate physics-based modeling tools such as MD and FEM with a computational cost orders of magnitude less. Given the computing speed advantage of ML approaches compared with physics-based modeling tools, ML models were applied to explore the design space of composite design problems and to generate optimal designs of composites. By knowing the optimal designs obtained from ML for different composite design problems, it is possible to probe design strategies for achieving high-performing composites. Although ML models are often being considered as “black box” models, the discovered design strategies can provide new physical insights into how to make tougher and stronger composites.

Future implementations for further breakthroughs in ML applications on composite materials include developing more efficient topology representations for composites and more efficient inverse design techniques. Although the topology of a composite can naturally be represented as an image by converting the unit cells or base materials of the composite to pixels, this representation is not as efficient for large composite systems (e.g., aircraft wings). For example, considering each base element in an FEM model as an input variable for ML naturally causes computational problems when the model is large. Thus, developing new topology representations (with fewer variables) for large composite systems is imperative to apply ML to design large composite systems. In this prospective paper, we review recent studies using ML to solve inverse design problems. Gu et al. applied a linear model to generate optimal designs of composites by using the information of the weights in the model.^{[Reference Gu, Chen and Buehler74]} However, the performance of the ML designs was limited to the learning ability of the linear model. In their later work, Gu et al. augmented CNN models with a self-learning algorithm to discover high-performing hierarchical composite designs.^{[Reference Gu, Chen, Richmond and Buehler66]} Hanakata et al. applied ML with an iterative screening process to search for high-performing graphene kirigami.^{[Reference Hanakata, Cubuk, Campbell and Park77]} Recently, Peurifoy et al. showed that NN models could be used to solve an inverse design problem for NPs by using backpropagation to train the inputs of the NN models.^{[Reference Peurifoy, Shen, Jing, Yang, Cano-Renteria, DeLacy, Joannopoulos, Tegmark and Soljačić82]} Although the gradient in this inverse design approach is analytical, not numerical, it is still a gradient-based optimization approach. Thus, this approach can still get stuck in local minima or critical points. Note there is no optimization approach that can guarantee finding global minima of a non-convex optimization problem without exploring the entire design space. Future work on understanding the performance (theoretically or numerically) of different inverse design approaches for the applications of ML on composites is required for the accelerated discovery of new materials. Though true inverse design is currently a challenge in the field, it is believed that ML will play a promising role in this engineering problem in the future.