Multiple-H-bonded-zwitterionic tetramer structure for L-(+)-2-Chlorophenylglycine, as investigated by UV, IR and Raman spectroscopy and electronic structure calculations

2021 ◽  
pp. 131218
Author(s):  
Madhuri D. Prabhu ◽  
Jayashree Tonannavar ◽  
J. Tonannavar
Keyword(s):  
Raman Spectroscopy ◽  
Electronic Structure ◽  
Electronic Structure Calculations ◽  
Ir And Raman Spectroscopy ◽  
Ir And Raman ◽  
Structure Calculations ◽  
Tetramer Structure

Impact of Fluorination and Chlorination on the Electronic Structure, Topology and in-Plane Ring Normal Modes of Pyridine

2021 ◽  
Author(s):  
Enrico Benassi ◽  
Tamara Vaganova ◽  
Evgenij V Malykhin ◽  
Haiyan Fan
Keyword(s):  
Raman Spectroscopy ◽  
Electronic Structure ◽  
Quantum Chemical Calculations ◽  
Quantum Chemical ◽  
Normal Modes ◽  
Ir And Raman Spectroscopy ◽  
Structure Topology ◽  
Ft Ir ◽  
Ir And Raman

Seven partially and fully fluorinated/chlorinated pyridines were investigated by means of FT-IR and Raman spectroscopy combined with quantum chemical calculations with the main aim to detect how the nature and...

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>

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