scholarly journals Projected Commutator DIIS Method for Accelerating Hybrid Functional Electronic Structure Calculations

2017 ◽  
Vol 13 (11) ◽  
pp. 5458-5467 ◽  
Author(s):  
Wei Hu ◽  
Lin Lin ◽  
Chao Yang
2012 ◽  
Vol 1394 ◽  
Author(s):  
Adisak Boonchun ◽  
Walter R. L. Lambrecht ◽  
Jiraroj T-Thienprasert ◽  
Sukit Limpijumnong

ABSTRACTElectronic structure calculations using the Heyd-Scuseria-Ernzerhof (HSE) hybrid functional are presented for NO-pair complexes with and without hydrogen to test the hypothesis that such defect complexes could lead to shallower levels than for isolated NO and hence p-type doping. The H is found to bind strongly to one of the N in the pair and removes thecorresponding defect level from the gap but the second N’s polaronic defect level in the gap remains deep.


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 ◽  
...  

2016 ◽  
Vol 18 (1) ◽  
pp. 403-413 ◽  
Author(s):  
Bin-Bin Xie ◽  
Shu-Hua Xia ◽  
Xue-Ping Chang ◽  
Ganglong Cui

Sequential vs. concerted S1 relaxation pathways.


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