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What are the most effective machine learning models for bandgap prediction?

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Bandgap is a key property of materials that determines their electronic and optical behavior. It is the energy difference between the highest occupied and the lowest unoccupied molecular orbitals. Predicting bandgap accurately and efficiently is essential for designing new materials with desired functionalities, such as solar cells, LEDs, or sensors. However, bandgap is often challenging to calculate from first principles or measure experimentally. Machine learning (ML) models offer an alternative approach that can leverage existing data and learn complex patterns to estimate bandgap values. In this article, you will learn about some of the most effective ML models for bandgap prediction and how they work.

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Ivano Aglietto

Want to enhance your products with graphene? | Founder, CEO GrapheneUP | grapheneup.com

1 Data representation

When predicting bandgap, one of the main factors that affects the performance of ML models is how materials are represented as input features. Representations such as composition-based descriptors, atomic coordinates, crystal graphs, or density functional theory (DFT) calculations can all be used, with each having its own benefits and drawbacks. Composition-based descriptors are easy to obtain and can handle a large number of materials, but they ignore the spatial arrangement and bonding of the atoms. Atomic coordinates can capture structural information, but they require a consistent reference frame and are sensitive to noise and distortions. Crystal graphs incorporate both chemical and topological information, but they require a reliable way to define the bonds and assign weights to the edges. Lastly, DFT calculations provide detailed information on the electronic structure and properties of materials but are expensive and time-consuming to perform.

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Dr. Isil Berkun

LinkedIn Top Voice | Founder of DigiFab AI | Keynote Speaker | Google WTM Ambassador | Intel Alum
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Choosing the right data representation in materials science is akin to selecting the ideal tool for a job. Composition-based descriptors are your reliable Swiss army knife: versatile and straightforward. Atomic coordinates, on the other hand, are like precision calipers: exacting but demanding of context. Crystal graphs integrate features as a multifunctional toolset would, though they necessitate a deep understanding of bonds and weights. DFT calculations? They're the high-powered microscope: offering unparalleled insight but demanding in resources. Hence, when predicting bandgap, discerning the right representation tool is pivotal. After all, the efficacy of our ML models depends largely on our foundational choices.

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Ivano Aglietto

Want to enhance your products with graphene? | Founder, CEO GrapheneUP | grapheneup.com
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The text offers a comprehensive yet nuanced overview of material representation techniques in machine learning models for bandgap prediction, serving as a critical resource for academics, engineers, and entrepreneurs. It establishes a hierarchy of complexity among descriptors, ranging from easily obtainable composition-based descriptors to computationally demanding density functional theory (DFT) calculations. This hierarchy aids in informed decision-making, particularly in projects with varying scopes and resources. Furthermore, the balanced discussion of each method's strengths and limitations presents an invaluable guide for optimizing performance. The technical depth and rigor appeal to a broad audience.

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2 Model selection

When selecting a model architecture and algorithm for ML models to use in bandgap prediction, there are several factors to consider. Linear models, such as linear regression or lasso regression, are simple and fast to train and interpret, but they may not be able capture non-linear behavior. Kernel methods, like support vector regression or Gaussian process regression, can handle non-linear patterns but require significant computational resources and tuning of the kernel parameters. Neural networks, such as feed-forward neural networks or convolutional neural networks, can learn from large and diverse datasets but may also be prone to overfitting. Lastly, tree-based methods, such as decision trees or gradient boosting machines, can handle non-linear patterns and feature interactions but may also be sensitive to noise and outliers.

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Ivano Aglietto

Want to enhance your products with graphene? | Founder, CEO GrapheneUP | grapheneup.com
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It's a critical discourse that appeals to a diverse set of stakeholders—from academics and engineers to entrepreneurs—given its direct implication on the efficiency and reliability of material property prediction. Linear models, for instance, offer the benefit of simplicity and speed but might falter when faced with complex, non-linear behaviors. Kernel methods ramp up the complexity to capture non-linearity but come with a computational and tuning overhead. Neural networks bring the promise of handling vast and diverse data but run the risk of overfitting, thereby narrowing their generalizability. Tree-based methods balance non-linearity and feature interactions but may falter when noise and outliers are present.

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3 Model evaluation

The performance of ML models for bandgap prediction is affected by the way they are evaluated and compared. Various metrics and methods can measure accuracy, precision, and robustness, such as mean absolute error (MAE), root mean squared error (RMSE), coefficient of determination (R2), or cross-validation. MAE averages the absolute differences between predicted and actual bandgap values, while RMSE penalizes larger errors more than smaller ones. R2 measures how well the model fits the data, relative to a baseline model that predicts the mean value. Cross-validation tests the model on unseen data to avoid overfitting or underfitting, but it may be computationally intensive.

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4 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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What are the most effective machine learning models for bandgap prediction?

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1 Data representation

2 Model selection

3 Model evaluation

4 Here’s what else to consider

Materials Science

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