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Investigating electron transfer behavior under external electric fields in molecular electronics is crucial for understanding the function of each component and for improving molecular design. Notably, the one-electron transfer is inevitable in molecular wires and switches, for which traditional density functional theory (DFT) and long-range corrected self-consistent-charge density functional tight binding (LC-DFTB) meet significant challenges. Inspired by previous studies on constrained configuration interaction schemes, we present restriction-based configuration interaction (RCI) LC-DFTB, a novel extension of LC-DFTB to deliver an accurate description of one-electron transfer under external electric fields. This approach retains the low cost of LC-DFTB while accurately capturing charge-resonance, localization versus delocalization, and field-induced response in large, structurally complex systems. We demonstrate its performance on a benzene assembly and a polyfluorene, showing that RCI-LC-DFTB efficiently describes the effects of molecular conformation and applied bias on electron localization and transfer. Our method thus provides a robust, scalable tool for the design of molecular electronic and organic photovoltaic materials.
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GPU-Enhanced DFTB Metadynamics for Efficiently Predicting Free Energies of Biochemical Systems
Metadynamics calculations of large chemical systems with ab initio methods are computationally prohibitive due to the extensive sampling required to simulate the large degrees of freedom in these systems. To address this computational bottleneck, we utilized a GPU-enhanced density functional tight binding (DFTB) approach on a massively parallelized cloud computing platform to efficiently calculate the thermodynamics and metadynamics of biochemical systems. To first validate our approach, we calculated the free-energy surfaces of alanine dipeptide and showed that our GPU-enhanced DFTB calculations qualitatively agree with computationally-intensive hybrid DFT benchmarks, whereas classical force fields give significant errors. Most importantly, we show that our GPU-accelerated DFTB calculations are significantly faster than previous approaches by up to two orders of magnitude. To further extend our GPU-enhanced DFTB approach, we also carried out a 10 ns metadynamics simulation of remdesivir, which is prohibitively out of reach for routine DFT-based metadynamics calculations. We find that the free-energy surfaces of remdesivir obtained from DFTB and classical force fields differ significantly, where the latter overestimates the internal energy contribution of high free-energy states. Taken together, our benchmark tests, analyses, and extensions to large biochemical systems highlight the use of GPU-enhanced DFTB simulations for efficiently predicting the free-energy surfaces/thermodynamics of large biochemical systems.
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- PAR ID:
- 10448893
- Date Published:
- Journal Name:
- Molecules
- Volume:
- 28
- Issue:
- 3
- ISSN:
- 1420-3049
- Page Range / eLocation ID:
- 1277
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/molecules28031277
