Reinforcement Learning Configuration Interaction

Goings, Joshua J.; Hu, Hang; Yang, Chao; Li, Xiaosong

doi:10.26434/chemrxiv.14342234

2021

DOI: 10.26434/chemrxiv.14342234

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Reinforcement Learning Configuration Interaction

Joshua J. Goings

Hang Hu²,

Chao Yang³

et al.

Abstract: A reinforcement learning algorithm is developed for the selected configuration interaction problem. We explore how reinforcement learning can obtain compact wave functions at near full configuration interaction accuracy.

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Active Learning Configuration Interaction for Excited-State Calculations of Polycyclic Aromatic Hydrocarbons

Jeong

Gaggioli

Gagliardi

2021

J. Chem. Theory Comput.

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We present the active learning configuration interaction (ALCI) method for multiconfigurational calculations based on large active spaces. ALCI leverages the use of an active learning procedure to find important electronic configurations among the full configurational space generated within an active space. We tested it for the calculation of singlet–singlet excited states of acenes and pyrene using different machine learning algorithms. The ALCI method yields excitation energies within 0.2–0.3 eV from those obtained by traditional complete active-space configuration interaction (CASCI) calculations (affordable for active spaces up to 16 electrons in 16 orbitals) by including only a small fraction of the CASCI configuration space in the calculations. For larger active spaces (we tested up to 26 electrons in 26 orbitals), not affordable with traditional CI methods, ALCI captures the trends of experimental excitation energies. Overall, ALCI provides satisfactory approximations to large active-space wave functions with up to 10 orders of magnitude fewer determinants for the systems presented here. These ALCI wave functions are promising and affordable starting points for the subsequent second-order perturbation theory or pair-density functional theory calculations.

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Active Learning Configuration Interaction for Excited-State Calculations of Polycyclic Aromatic Hydrocarbons

Jeong

Gaggioli

Gagliardi

2021

J. Chem. Theory Comput.

View full text Add to dashboard Cite

show abstract

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Reinforcement Learning Configuration Interaction

Abstract: A reinforcement learning algorithm is developed for the selected configuration interaction problem. We explore how reinforcement learning can obtain compact wave functions at near full configuration interaction accuracy.

Cited by 1 publication

References 26 publications

Active Learning Configuration Interaction for Excited-State Calculations of Polycyclic Aromatic Hydrocarbons

Active Learning Configuration Interaction for Excited-State Calculations of Polycyclic Aromatic Hydrocarbons

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