Researchers have developed a novel method for optimizing neural network wave functions, enabling the efficient prediction of ground and excited-state potential energy surfaces (PESs). This approach leverages weight sharing and dynamical ordering of electronic states to achieve up to a two-orders-of-magnitude cost reduction compared to traditional methods. The technique was validated on four complex excited-state PESs, including ethylene, the carbon dimer, the methylenimmonium cation, and a rubredoxin active site model, demonstrating the potential of transferable deep-learning QMC for studying electronic excitations in molecules. AI
IMPACT This method could significantly speed up simulations of molecular interactions and reactions, aiding research in chemistry and materials science.
RANK_REASON Academic paper detailing a new computational method. [lever_c_demoted from research: ic=1 ai=1.0]
- arXiv
- Carbon Dimers as the Dominant Feeding Species in Epitaxial Growth and Morphological Phase Transition of Graphene on Different Cu Substrates
- deep-learning QMC
- ethylene
- methylenimmonium cation
- QMC
- rubredoxin
- Zeno Schätzle
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