Mechanism of Nitrite Reduction at T2Cu Centers:  Electronic Structure Calculations of Catalysis by Copper Nitrite Reductase and by Synthetic Model Compounds

2007 ◽  
Vol 111 (19) ◽  
pp. 5511-5517 ◽  
Author(s):  
Mahesh Sundararajan ◽  
Ian H. Hillier ◽  
Neil A. Burton
1990 ◽  
Vol 209 ◽  
Author(s):  
Steven M. Risser ◽  
Kim F. Ferris

The extent of the delocalized π electron network is of prime importance in determining hyperpolarizabilities of conjugated molecules. Thus, for conjugated polymers, disruptions to this continuous order such as structural and/or conformational defects may have a large influence on the hyperpolarizabilities. We have performed semiempirical electronic structure calculations for a series of model compounds such as polyenes and polyphosphazenes to determine the effects of structural and conformational defects on the hyperpolarizabilities. Briefly, we find that structural and conjugational defects enhance the hyperpolarizabilities of polyenes, but have little influence on polyphosphazenes due to their limited π delocalization.


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

2021 ◽  
Vol 155 (3) ◽  
pp. 034110
Author(s):  
Prakash Verma ◽  
Lee Huntington ◽  
Marc P. Coons ◽  
Yukio Kawashima ◽  
Takeshi Yamazaki ◽  
...  

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