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Centre for Computational Science, Department of Chemistry, University College London, WC1H 0AJ, United Kingdom, Advanced Research Computing Centre, University College London, WC1H 0AJ, United Kingdom
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TNASS achieves superior accuracy in multi-scale modeling by automating active space selection, outperforming traditional methods in both ground state energy and dipole moment predictions.
The small scaling exponents of LLMs reveal a troubling energy unsustainability that persists even when accounting for biases in loss function calculations.
Achieving a quantum-classical separation in measurement complexity could revolutionize how we approach chaotic systems in machine learning.
