Open Materials 2024 dataset enables machine learning models to surpass previous standards, achieving F1 scores above 0.9 and 20 meV per atom accuracy in inorganic materials.
- 18 cites
Machine Learning in Materials Science
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NEP89 delivers high-accuracy, computationally efficient atomistic simulations across 89 elements, enabling large-scale studies previously limited by resource demands.
- Open access
- 28 cites
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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
Local pseudopotentials enhance the efficiency and accuracy of neural network-based quantum Monte Carlo for large, complex systems, including the Fe4S4 cluster.
TDiMS descriptor outperforms existing methods by accurately capturing nonlocal intramolecular interactions with 20% higher predictive accuracy in property prediction tasks.
- Open access
Agentic AI system RepurAgent outperforms baseline models by generating more novel, credible drug candidates across multiple stages of drug repurposing, including in leukemia, COVID-19, and rare diseases.
- Open access
Surface and bulk defect characteristics critically influence the photoelectrochemical performance of Ta(3)N(5) photoanodes, with surface properties varying significantly by precursor chemistry.
- Open access
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