Deep-learning QMC method accelerates excited-state potential energy surface calculations

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]

Read on arXiv cs.LG →

• 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

• paper
• infra

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →