How can you accurately validate machine learning models for bandgap prediction?

Machine learning (ML) models can accelerate the discovery of novel materials with desirable properties, such as bandgap, which is a key factor for many electronic and photonic applications. However, how can you ensure that your ML models are accurate and reliable when predicting bandgap values from various input features, such as chemical composition, crystal structure, or density functional theory (DFT) calculations? In this article, you will learn about some common methods and challenges for validating ML models for bandgap prediction, and how to improve your model performance and interpretability.

1 Data quality and quantity

One of the first steps for validating ML models for bandgap prediction is to check the quality and quantity of your data. You need to have enough data points to cover the diversity and complexity of the materials space, and to avoid overfitting or underfitting your model. You also need to ensure that your data is consistent, reliable, and free of errors or outliers. You can use various data cleaning, preprocessing, and augmentation techniques to improve your data quality and quantity, such as filtering, scaling, imputing, or generating synthetic data.

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2 Model selection and optimization

Another important step for validating ML models for bandgap prediction is to choose and optimize the most suitable model for your problem. You need to consider the trade-off between model complexity and interpretability, as well as the computational cost and efficiency of your model. You can use different ML algorithms, such as linear regression, decision trees, support vector machines, neural networks, or ensemble methods, depending on your data characteristics and objectives. You also need to tune the hyperparameters of your model, such as learning rate, regularization, or number of layers, to achieve the best performance and avoid overfitting or underfitting.

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Anubhav S.

Building AI | Angel Investor | Author | 40 Under 40 Data Science | Top 10 Data Scientists (India) 2020
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One should also consider advanced methods such as Bayesian optimisation and genetic algorithms for hyper-parameter tuning, leveraging automated machine learning (AutoML) platforms, and considering transfer learning with pre-trained neural network architectures. Employing diverse model architectures and algorithms, coupled with ensemble techniques such as bagging and boosting, enhances predictive performance. Employing robust validation strategies like stratified sampling and time series cross-validation ensures the model's reliability. Additionally, integrating model explanation tools like SHAP values and expert domain knowledge can provide valuable insights, ensuring the models' accuracy and interpretability in real-world applications.

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3 Model evaluation and comparison

A crucial step for validating ML models for bandgap prediction is to evaluate and compare your model performance and accuracy with various metrics and methods. You need to split your data into training, validation, and test sets, and use cross-validation or bootstrapping to reduce the variance and bias of your estimates. You also need to select the appropriate metrics for your problem, such as mean absolute error (MAE), root mean squared error (RMSE), coefficient of determination (R2), or mean absolute percentage error (MAPE), and compare them with the baseline or benchmark models. You can also use visual tools, such as scatter plots, residual plots, or error distributions, to analyze your model predictions and errors.

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Anubhav S.

Building AI | Angel Investor | Author | 40 Under 40 Data Science | Top 10 Data Scientists (India) 2020
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If bandgap prediction is formulated as a classification problem, AUC and ROC curves are essential metrics for evaluating model performance, especially in binary or multiclass classification scenarios. One could also consider Cohen's Kappa that measures the agreement between predicted and actual classifications, considering the possibility of the agreement occurring by chance. It is particularly useful when dealing with imbalanced class distributions. Another method could be Quantile Loss that assesses the model's accuracy in predicting specific quantiles of the target variable distribution. This is especially useful if you are interested in predicting bandgap values within certain percentiles.

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4 Model uncertainty and robustness

A final step for validating ML models for bandgap prediction is to assess and quantify the uncertainty and robustness of your model predictions and errors. You need to account for the sources of uncertainty and error in your data, model, and evaluation, such as noise, outliers, missing values, or model assumptions. You can use various methods to estimate and propagate the uncertainty and error of your model predictions, such as Bayesian inference, Monte Carlo simulation, or bootstrapping. You also need to test the robustness and generalizability of your model predictions under different scenarios and conditions, such as new data, noise, or perturbations.

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Anubhav S.

Building AI | Angel Investor | Author | 40 Under 40 Data Science | Top 10 Data Scientists (India) 2020
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Try and validate the model across different domains or datasets related to bandgap prediction. This approach assesses how well the model generalises across different materials or experimental conditions, providing insights into its applicability in diverse scenarios. One could also conduct simulation studies to assess how the model performs under controlled conditions. Another approach that can be tried is that instead of predicting a single value, develop probabilistic models that provide a distribution of possible predictions along with confidence intervals. This approach gives a clearer understanding of the uncertainty associated with each prediction.

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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How can you accurately validate machine learning models for bandgap prediction?

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1 Data quality and quantity

2 Model selection and optimization

3 Model evaluation and comparison

4 Model uncertainty and robustness

5 Here’s what else to consider

Materials Science

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