Domain-Specific Knowledge Integration Through Curriculum Learning Method in Machine Learning for Spectral Analysis

We present a novel approach to enhance machine learning models for predicting UV spectra from SMILES string. By incorporating domain-specific knowledge through a curriculum learning method, we improve the predictive performance of these models. Our research demonstrates the potential of implementing domain-specific knowledge for machine learning to advance spectral analysis and its applications across various fields such as organic solar cells and photocatalysts.<br/>UV spectroscopy provides crucial insights into the electronic structures and properties of molecules. However, effective application of machine learning to spectral prediction faces challenges due to the complexities of UV spectra, such as peak overlap and instrumental variability. Our approach leverages the unique characteristics of UV spectra from organic molecules, which typically have relatively broad and smooth peaks compared to other spectroscopic techniques.<br/>We implement this domain-specific knowledge through a curriculum learning process which makes the model to progressively learn from simpler to more complex spectral patterns. Our method utilizes neural network architectures optimized for spectral prediction tasks, with input layers accepting molecular representations and output layers corresponding to spectral data points.<br/>The curriculum learning method employs a unique interpolation technique between selected data points at each resolution stage. The training process is divided into multiple stages of increasing spectral resolution, allowing the model to gradually adapt to more detailed spectral features.<br/>We evaluated our approach using a diverse dataset of experimental spectra and corresponding molecular structures. The dataset was appropriately split to ensure robust training, validation, and testing of the model.<br/>Our results demonstrate the effectiveness of the curriculum learning approach. We evaluated different curriculum learning strategies, comparing them to a baseline model trained directly on full-resolution spectra. The multi-stage curriculum learning approach consistently achieved lower test loss, indicating improved predictive accuracy.<br/>Our method highlights the transformative potential of integrating domain-specific knowledge into machine learning models for UV spectral prediction. The implementation of curriculum learning methods consistently enhanced model accuracy and physical realism, which represents significant advancements in spectral prediction capabilities.<br/>Future research should explore the extension of these domain-specific knowledge enhancements to other spectroscopic techniques and expand the scope of datasets to further validate the robustness and transferability of the proposed methods. Investigating the interpretability of the enhanced models could provide valuable insights into the underlying mechanisms of the structure-spectrum relationship.<br/>In conclusion, we showcase the potential of implementing domain-specific knowledge enhancement into machine learning for advancing spectral analysis and its applications. By integrating curriculum learning methods, we have developed more accurate and physically realistic models for UV spectral prediction. These advancements open to new avenues for scientific discovery and innovation, contributing to challenging problems in various fields such as organic solar cells and photocatalysts, and beyond. (3,390 characters)