Global warming has made the Arctic increasingly available for marine operations and created a demand for reliable operational sea ice forecasts to increase safety. Because ocean-ice numerical models are highly computationally intensive, relatively lightweight ML-based methods may be more efficient for sea ice forecasting. Many studies have exploited different deep learning models alongside classical approaches for predicting sea ice concentration in the Arctic. However, only a few focus on daily operational forecasts and consider the real-time availability of data needed for marine operations. In this article, we aim to close this gap and investigate the performance of the U-Net model trained in two regimes for predicting sea ice for up to the next 10 days. We show that this deep learning model can outperform simple baselines by a significant margin, and we can improve the model’s quality by using additional weather data and training on multiple regions to ensure its generalization abilities. As a practical outcome, we build a fast and flexible tool that produces operational sea ice forecasts in the Barents Sea, the Labrador Sea, and the Laptev Sea regions.

Density functional theory is the workhorse of present-day quantum chemistry thanks to its good balance between calculation accuracy and speed. In recent years, several neural network-based exchange–correlation functionals have been developed, with DM21, developed by Google DeepMind, being the most recognizable among them. In this study, we focus on evaluating the efficiency of DM21 functional on the task of optimizing molecular geometries and investigate how the non-smooth behavior of neural network-predicted exchange–correlation energy and potential affects the final geometry precision. We implement geometry optimization for the DM21 functional in PySCF and compare its performance with traditional functionals on various benchmarks. Our findings reveal that numerical noise coming from the neural network outputs contaminates numerical nuclear gradients required for geometry optimization. We also found that a numerical differentiation step in the range of 0.0001–0.001 Å is required to obtain sufficiently smooth nuclear gradients. Furthermore, we show that the non-smoothness of DM21 can be reproduced by adding random normally distributed noise to local energies of an analytical SCAN functional, allowing one to efficiently estimate the optimal numerical differentiation step for geometry optimization of a given molecule. Our findings show that DM21 does not outperform analytical functionals in the accuracy of optimized molecular geometries and is significantly slower, which limits its practical applicability to chemical calculations.