scholarly journals Implementation of Quantum Machine Learning for Electronic Structure Calculations of Periodic Systems on Quantum Computing Devices

2021 ◽  
Author(s):  
Shree Hari Sureshbabu ◽  
Manas Sajjan ◽  
Sangchul Oh ◽  
Sabre Kais
Keyword(s):  
Machine Learning ◽  
Electronic Structure ◽  
Quantum Computing ◽  
Electronic Structure Calculations ◽  
Periodic Systems ◽  
Quantum Machine Learning ◽  
Structure Calculations

A finite temperature linear tetrahedron method for electronic structure calculations of periodic systems

2004 ◽  
Vol 121 (6) ◽  
pp. 2466 ◽  
Author(s):  
Oleg V. Yazyev ◽  
Edward N. Brothers ◽  
Konstantin N. Kudin ◽  
Gustavo E. Scuseria
Keyword(s):  
Electronic Structure ◽  
Finite Temperature ◽  
Electronic Structure Calculations ◽  
Periodic Systems ◽  
Structure Calculations

scholarly journals Addressing uncertainty in atomistic machine learning

2017 ◽  
Vol 19 (18) ◽  
pp. 10978-10985 ◽  
Author(s):  
Andrew A. Peterson ◽  
Rune Christensen ◽  
Alireza Khorshidi
Keyword(s):  
Machine Learning ◽  
Electronic Structure ◽  
Potential Energy ◽  
Electronic Structure Calculations ◽  
Structure Calculations

Machine-learning regression can precisely emulate the potential energy and forces of more expensive electronic-structure calculations, but to make useful predictions an assessment must be made of the prediction's credibility.

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