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A systematic construction of configuration interaction wavefunctions in the complete CI space
Andrew W Prentice1, Jeremy P Coe1, Martin J Paterson1
1Institute of Chemical Sciences, School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh EH14 4AS, United Kingdom.
We developed systematic-Monte Carlo Configuration Interaction (MCCI) for quantum chemistry. This method systematically explores configuration space, showing comparable accuracy to standard MCCI for various molecules.
Area of Science:
- Quantum Chemistry
- Computational Chemistry
- Electronic Structure Theory
Background:
- Configuration Interaction (CI) methods are crucial for accurate electronic structure calculations.
- Monte Carlo Configuration Interaction (MCCI) offers a computationally efficient alternative to full CI.
- Exploring and systematically constructing CI wavefunctions remains an active area of research.
Purpose of the Study:
- To introduce and evaluate a systematic variant of the MCCI method.
- To compare the performance of systematic-MCCI against standard MCCI and pruned-FCI approaches.
- To assess the scalability and accuracy of systematic-MCCI for various molecular systems.
Main Methods:
- Development of systematic-MCCI by batch-wise consideration of the interacting space.
- Comparison with MCCI and pruned-Full Configuration Interaction (FCI) methods.
- Application to neon atom, water dissociation, carbon monoxide excited state, and chromium dimer.
Main Results:
- Systematic-MCCI, MCCI, and pruned-MCCI show comparable accuracy for neon's ground state, recovering ~99% of correlation energy.
- MCCI performs comparably to systematic approaches for water dissociation potential energy surface.
- Systematic-MCCI demonstrates good scalability despite longer run times and avoids local minima encountered by MCCI in the chromium dimer case.
Conclusions:
- Systematic-MCCI provides a robust and accurate approach for constructing CI wavefunctions.
- The method shows comparable performance to existing MCCI techniques while offering systematic exploration.
- Scalability is good, and the approach can avoid pitfalls like local minima in complex systems.
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