Marek Černý

Electing a clown turns the government into a circus.

Leveraging Persistent Homology Features for Accurate Defect Formation Energy Predictions via Graph Neural Networks In machine-learning-assisted high-throughput defect studies, a defect-aware latent representation of the supercell structure is crucial for the accurate prediction of defect properties. The performance of current graph neural network (GNN) models is limited due to the fact that defect properties depend strongly on the local atomic configurations near the defect sites and due to the oversmoothing problem of GNN. Herein, we demonstrate that persistent homology features, which encode the topological information on the local chemical environment around each atomic site, can characterize the structural information on defects. Using the dataset containing a wide spectrum of O-based perovskites with all available vacancies as an example, we show that incorporating the persistent homology features, along with proper choices of graph pooling operations, significantly increases the prediction accuracy, with the MAE reduced by 55%. Those features can be easily integrated into the state-of-the-art GNN models, including the graph Transformer network and the equivariant neural network, and universally improve their performance. Besides, our model also overcomes the convergence issue with respect to the supercell size that was present in previous GNN models. Furthermore, using the datasets of defective BaTiO3 with multiple substitutions and multiple vacancies as examples, our GNN model can also predict the defect–defect interactions accurately. These results suggest that persistent homology features can effectively improve the performance of machine learning models and assist the accelerated discovery of functional defects for technological applications.

Fang et al. use persistent homology within graph neural networks to capture local defect structures in perovskites, cutting errors by 55%. Their approach resolves supercell scaling issues, boosting defect predictions and accelerating materials discovery. pubs.acs.org/doi/full/10....

Amazing video on how the right insight is so important!

youtube.com/watch?v=Q10_...

New Achilles Heel Exposed in Antibiotic Resistance Carbapenems are last-resort antibiotics for treating bacterial infections. A study shows how zinc plays a role in drug-resistant bacteria.

Good resistance news: An 'Achilles heel' discovered in antibiotic resistant bacteria. Scientists in Canada found the critical vulnerability: bacteria need zinc. Deprive them of the nutrient & their stability is disrupted making them susceptible to antibiotics again www.genengnews.com/topics/infec...

Impressive! 🔥🔥

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Deep-Learning Based Docking Methods: Fair Comparisons to Conventional Docking Workflows The diffusion learning method, DiffDock, for docking small-molecule ligands into protein binding sites was recently introduced. Results included comparisons to more conventional docking approaches, wi...

Been a while since I read a paper like this:
• "What [DiffDock] appears to be doing cannot be considered" docking
• "Results are ... contaminated with near neighbors to test cases"
• "Results for DiffDock were artifactual"
• "Results for other methods were incorrectly done"
arxiv.org/abs/2412.02889