Efficient solution of Poisson’s equation in linear combination of atomic orbitals (LCAO) electronic structure calculations

1984 ◽  
Vol 81 (12) ◽  
pp. 5864-5872 ◽  
Author(s):  
Peter J. Feibelman
Keyword(s):  
Electronic Structure ◽  
Linear Combination ◽  
Efficient Solution ◽  
Electronic Structure Calculations ◽  
Poisson’S Equation ◽  
Poisson's Equation ◽  
Atomic Orbitals ◽  
Structure Calculations

Physics of Charged Mgn (n≤7) and Mixed MgnKy (x+y≤4) Clusters

1990 ◽  
Vol 206 ◽  
Author(s):  
S. N. Khanna ◽  
F. Reuse ◽  
V. de Coulon ◽  
J. Buttet
Keyword(s):  
Electronic Structure ◽  
Binding Energy ◽  
Density Functional ◽  
Electronic Structure Calculations ◽  
Atomic Orbitals ◽  
The Core ◽  
Non Local ◽  
The Stability ◽  
Mixed Clusters ◽  
Structure Calculations

ABSTRACTElectronic structure calculations on neutral and charged Mgn (n≤7) clusters and KnMgm mixed clusters have been carried out within a linear combination of atomic orbitals molecular orbital approach. The exchange correlaton effects have been treated via local spin density functional and non-local pseudopotentials have been used to replace the core. Our studies on charged Mgn clusters focus on the electron affinity and atomic binding in anionic clusters and the stability and observability of doubly ionized clusters. In KnMgm clusters, we have considered the evolution of the electronic spectrum and the variation of the binding energy with size and composition.

Message Passing Neural Networks for Partial Charge Assignment to Metal-Organic Frameworks

2020 ◽  
Author(s):  
Ali Raza ◽  
Arni Sturluson ◽  
Cory Simon ◽  
Xiaoli Fern
Keyword(s):  
Electronic Structure ◽  
Message Passing ◽  
Gas Adsorption ◽  
Metal Organic Frameworks ◽  
Computational Cost ◽  
Electronic Structure Calculations ◽  
High Fidelity ◽  
Partial Charges ◽  
Metal Organic ◽  
Structure Calculations

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>

Sign in / Sign up

Export Citation Format

Share Document