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Orbital Transformers learn molecular wavefunctions for faster TDDFT simulations

By PulseAugur Editorial · [1 sources] ·

Researchers have developed OrbEvo, an equivariant graph transformer model designed to predict molecular wavefunctions in time-dependent density functional theory (TDDFT). This new approach aims to accelerate the simulation of molecular dynamics under external excitations, which is crucial for predicting properties like optical absorption and electron dynamics. OrbEvo utilizes wavefunction pooling or density matrix aggregation to learn the time evolution operator, showing accurate results on datasets derived from QM9 and MD17. AI

IMPACT OrbEvo could significantly speed up simulations in quantum chemistry, enabling more complex and accurate predictions of molecular behavior.

RANK_REASON This is a research paper detailing a new model for scientific simulation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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

Orbital Transformers learn molecular wavefunctions for faster TDDFT simulations

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This is a research paper detailing a new model for scientific simulation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Xuan Zhang, Haiyang Yu, Chengdong Wang, Jacob Helwig, Shuiwang Ji, Xiaofeng Qian ·

    Orbital Transformers for Predicting Wavefunctions in Time-Dependent Density Functional Theory

    arXiv:2603.03511v2 Announce Type: replace Abstract: We aim to learn wavefunctions simulated by time-dependent density functional theory (TDDFT), which can be efficiently represented as linear combination coefficients of atomic orbitals. In real-time TDDFT, the electronic wavefunc…

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