Skala is a neural XC functional trained on wavefunction data that beats state-of-the-art hybrids on main-group chemistry benchmarks at semi-local computational cost.
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Phenalene (C13H10) is detected for the first time outside TMC-1 CP, in the VeLLO MC27/L1521F, with a phenalene-to-benzonitrile ratio four times higher than in TMC-1 CP.
DFT-derived binding energy distributions for methanol and photolysis products on ASW ice, integrated into astrochemical models, demonstrate sensitivity of radical abundances to BE calculation methods.
Benchmarks of 15 MLIPs show parameter count and training set size correlate with accuracy, architecture drives speed and memory, and explicit Coulomb terms provide no benefit.
citing papers explorer
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Accurate and scalable exchange-correlation with deep learning
Skala is a neural XC functional trained on wavefunction data that beats state-of-the-art hybrids on main-group chemistry benchmarks at semi-local computational cost.
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Detection of the Polycyclic Aromatic Hydrocarbon Phenalene (C$_{13}$H$_{10}$) in the Very Low Luminosity Object (VeLLO) MC27/L1521F
Phenalene (C13H10) is detected for the first time outside TMC-1 CP, in the VeLLO MC27/L1521F, with a phenalene-to-benzonitrile ratio four times higher than in TMC-1 CP.
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Theoretical determination of the binding energies of methanol and related species onto amorphous solid water ice
DFT-derived binding energy distributions for methanol and photolysis products on ASW ice, integrated into astrochemical models, demonstrate sensitivity of radical abundances to BE calculation methods.
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Accuracy and Efficiency Benchmarks of Pretrained Machine Learning Potentials for Molecular Simulations
Benchmarks of 15 MLIPs show parameter count and training set size correlate with accuracy, architecture drives speed and memory, and explicit Coulomb terms provide no benefit.