The field of molecular structure prediction is rapidly evolving, with a focus on developing innovative methods to improve the accuracy and efficiency of prediction models. Recent research has emphasized the importance of integrating multiple views and modalities, such as 2D and 3D molecular structures, to enhance cross-view consistency and model expressiveness. The use of geometric-aware co-attention mechanisms and periodic representation learning has shown significant promise in improving predictive performance and interpretability.

Notable papers in this area include those that propose novel frameworks for pre-training graph neural networks on molecular structures, achieving state-of-the-art results in predictive performance and interpretability. Another paper presents a geometric-aware co-attention model for predicting perovskite solar cell power conversion efficiency, which outperforms existing baselines and demonstrates the importance of integrating geometric and textual information. Additionally, a Python package for machine learning on molecular crystals has been introduced, providing a flexible toolkit for data-driven modeling and crystal structure prediction. Lastly, a graph neural network framework that explicitly integrates multiscale representations and periodic feature encoding has been proposed, showing improved predictive accuracy in crystal structure property prediction.