scholarly journals Charge-Transfer Knowledge Graph among Amino Acids Derived from High-Throughput Electronic Structure Calculations for Protein Database

ACS Omega ◽  
2018 ◽  
Vol 3 (4) ◽  
pp. 4094-4104 ◽  
Author(s):  
Hongwei Wang ◽  
Fang Liu ◽  
Tiange Dong ◽  
Likai Du ◽  
Dongju Zhang ◽  
...  
1996 ◽  
Vol 446 ◽  
Author(s):  
Martina E. Bachlechner ◽  
Ingvar Ebbsjö ◽  
Rajiv K. Kalia ◽  
Priya Vashishta

AbstractStructural correlations at the Si(111)/Si3N4(0001) interface are studied using the molecular dynamics (MD) method. In the bulk, Si is described by the Stillinger-Weber potential and Si3N4 by an interaction potential which contains two-body (steric, Coulomb, electronic polarizabilities) and three-body (bond bending and stretching) terms. At the interface, the charge transfer from silicon to nitrogen is taken from LCAO electronic structure calculations. Using these Si, Si3N4 and interface interactions in MD simulations, the interface structure (atomic positions, bond lengths, and bond angles) is determined. Results for fracture in silicon are also presented.


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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