Machine Learning in Materials Science
Moving in bioRxiv, Nature Computational Science, Nature Communications, Environmental pollution, Nature and Nature Methods.
- Moved, week to 23 Oct 2026
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Latest in Machine Learning in Materials Science
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AI/ML workflow predicts small-molecule activity against drug-susceptible and drug-resistant Mycobacterium tuberculosis with high accuracy, achieving AUROC scores above 0.83.
- Open access
Active and geometric deep learning combined in a closed-loop workflow accurately predicts reaction outcomes and regioselectivity in drug discovery, achieving perfect pinpointing on challenging substrates.
- Open access
- 1 cites
Large language models (LLMs) up to 70 billion parameters consistently fail to learn coordinate information in coordinate-category datasets, despite excelling at category patterns.
- Open access
- 13 cites
Spurious model comparisons are prevalent in biomedical AI, with nearly two-thirds of studies using invalid tests that inflate false-positive rates.
- Open access
- 1 cites
TDiMS descriptor outperforms existing methods by accurately capturing nonlocal intramolecular interactions with 20% higher predictive accuracy in property prediction tasks.
- Open access
Local pseudopotentials enhance the efficiency and accuracy of neural network-based quantum Monte Carlo for large, complex systems, including the Fe4S4 cluster.
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Topics moving, week to 23 Oct 2026
- Single-cell and spatial transcriptomics6
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- Alzheimer's disease research and treatments3
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