57Fe Mössbauer Isomer Shifts of Heme Protein Model Systems:  Electronic Structure Calculations

2002 ◽  
Vol 124 (26) ◽  
pp. 7829-7839 ◽  
Author(s):  
Yong Zhang ◽  
Junhong Mao ◽  
Eric Oldfield
1993 ◽  
Vol 328 ◽  
Author(s):  
KIM F. Ferris ◽  
W. D. Samuels ◽  
Y. Morita ◽  
G. J. Exarhos

ABSTRACTThe optical response of polyphosphazenes can be directly related to the π (out-of-plane) and π′ (in-plane) bonding interactions intrinsic to the electronic structure of these Materials. Altering this structure either by hydrogen bonding or absórbate effects, affects both the linear and nonlinear optical susceptibilities. In this paper, we have performed electronic structure calculations on the cyclic Molecules, P3N3 (NHCH3)6, P3N3(SCH3)6, P3N3 (OCH3)6 and P4N4 (NHCH3)8 as model systems for the polymer. Charge distribution arguments are discussed to explain the influence of a polarizing electric field on the π bonding systems, and are used to suggest methods to enhance their nonlinearities.


2006 ◽  
Vol 34 (1) ◽  
pp. 1-9 ◽  
Author(s):  
S.-U. Weber ◽  
M. Grodzicki ◽  
C. A. Geiger ◽  
W. Lottermoser ◽  
G. Tippelt ◽  
...  

2020 ◽  
Author(s):  
Ali Raza ◽  
Arni Sturluson ◽  
Cory Simon ◽  
Xiaoli Fern

Virtual screenings can accelerate and reduce the cost of discovering metal-organic frameworks (MOFs) for their applications in gas storage, separation, and sensing. In molecular simulations of gas adsorption/diffusion in MOFs, the adsorbate-MOF electrostatic interaction is typically modeled by placing partial point charges on the atoms of the MOF. For the virtual screening of large libraries of MOFs, it is critical to develop computationally inexpensive methods to assign atomic partial charges to MOFs that accurately reproduce the electrostatic potential in their pores. Herein, we design and train a message passing neural network (MPNN) to predict the atomic partial charges on MOFs under a charge neutral constraint. A set of ca. 2,250 MOFs labeled with high-fidelity partial charges, derived from periodic electronic structure calculations, serves as training examples. In an end-to-end manner, from charge-labeled crystal graphs representing MOFs, our MPNN machine-learns features of the local bonding environments of the atoms and learns to predict partial atomic charges from these features. Our trained MPNN assigns high-fidelity partial point charges to MOFs with orders of magnitude lower computational cost than electronic structure calculations. To enhance the accuracy of virtual screenings of large libraries of MOFs for their adsorption-based applications, we make our trained MPNN model and MPNN-charge-assigned computation-ready, experimental MOF structures publicly available.<br>


2021 ◽  
Vol 154 (11) ◽  
pp. 114105
Author(s):  
Max Rossmannek ◽  
Panagiotis Kl. Barkoutsos ◽  
Pauline J. Ollitrault ◽  
Ivano Tavernelli

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