scholarly journals John Norman Murrell. 2 March 1932 — 25 January 2016

2016 ◽  
Vol 63 ◽  
pp. 467-486 ◽  
Author(s):  
Sir David C. Clary ◽  
Anthony J. Stace ◽  
Jonathan Tennyson
Keyword(s):  
Graduate Students ◽  
Potential Energy ◽  
Chemical Bonding ◽  
Potential Energy Surfaces ◽  
Organic Molecules ◽  
Intermolecular Forces ◽  
Theoretical Chemistry ◽  
Research And Teaching ◽  
Energy Surfaces ◽  
The University

John Murrell was a theoretical chemist who made important contributions to the understanding of the spectra of organic molecules, to the theory of intermolecular forces and to the construction of potential energy surfaces. He established the University of Sussex as a major centre for research and teaching in theoretical chemistry. He was also a successful writer of textbooks for undergraduate and graduate students on chemical bonding and related topics.

scholarly journals Transferable Neural Network Potential Energy Surfaces for Closed-Shell Organic Molecules: Extension to Ions

2021 ◽  
Author(s):  
Leif Jacobson ◽  
James Stevenson ◽  
Farhad Ramezanghorbani ◽  
Delaram Ghoreishi ◽  
Karl Leswing ◽  
...  
Keyword(s):  
Neural Network ◽  
Potential Energy ◽  
Potential Energy Surfaces ◽  
Organic Molecules ◽  
Closed Shell ◽  
Energy Surfaces

Resolving the Quadruple Bonding Conundrum in C2 Using Insights Derived from Excited State Potential Energy Surfaces: A Molecular Orbital Perspective

2019 ◽  
Author(s):  
Ishita Bhattacharjee ◽  
Debashree Ghosh ◽  
Ankan Paul
Keyword(s):  
Excited State ◽  
Potential Energy ◽  
Molecular Orbital ◽  
High Spin ◽  
Potential Energy Surfaces ◽  
Valence Bond ◽  
Deep Minimum ◽  
State Potential ◽  
Energy Surfaces ◽  
Mo Theory

The question of quadruple bonding in C<sub>2</sub> has emerged as a hot button issue, with opinions sharply divided between the practitioners of Valence Bond (VB) and Molecular Orbital (MO) theory. Here, we have systematically studied the Potential Energy Curves (PECs) of low lying high spin sigma states of C<sub>2</sub>, N<sub>2</sub> and Be<sub>2</sub> and HC≡CH using several MO based techniques such as CASSCF, RASSCF and MRCI. The analyses of the PECs for the<sup> 2S+1</sup>Σ<sub>g/u</sub> (with 2S+1=1,3,5,7,9) states of C<sub>2</sub> and comparisons with those of relevant dimers and the respective wavefunctions were conducted. We contend that unlike in the case of N<sub>2</sub> and HC≡CH, the presence of a deep minimum in the <sup>7</sup>Σ state of C<sub>2</sub> and CN<sup>+</sup> suggest a latent quadruple bonding nature in these two dimers. Hence, we have struck a reconciliatory note between the MO and VB approaches. The evidence provided by us can be experimentally verified, thus providing the window so that the narrative can move beyond theoretical conjectures.

Extending the Applicability of the ANI Deep Learning Molecular Potential to Sulfur and Halogens

2020 ◽  
Author(s):  
Christian Devereux ◽  
Justin Smith ◽  
Kate Davis ◽  
Kipton Barros ◽  
Roman Zubatyuk ◽  
...  
Keyword(s):  
Drug Development ◽  
Autonomous Vehicles ◽  
Potential Energy Surfaces ◽  
Organic Molecules ◽  
Chemical Elements ◽  
Molecular Potential ◽  
Wide Range ◽  
Energy Surfaces ◽  
New Applications ◽  
Physical Phenomena

<p>Machine learning (ML) methods have become powerful, predictive tools in a wide range of applications, such as facial recognition and autonomous vehicles. In the sciences, computational chemists and physicists have been using ML for the prediction of physical phenomena, such as atomistic potential energy surfaces and reaction pathways. Transferable ML potentials, such as ANI-1x, have been developed with the goal of accurately simulating organic molecules containing the chemical elements H, C, N, and O. Here we provide an extension of the ANI-1x model. The new model, dubbed ANI-2x, is trained to three additional chemical elements: S, F, and Cl. Additionally, ANI-2x underwent torsional refinement training to better predict molecular torsion profiles. These new features open a wide range of new applications within organic chemistry and drug development. These seven elements (H, C, N, O, F, Cl, S) make up ~90% of drug like molecules. To show that these additions do not sacrifice accuracy, we have tested this model across a range of organic molecules and applications, including the COMP6 benchmark, dihedral rotations, conformer scoring, and non-bonded interactions. ANI-2x is shown to accurately predict molecular energies compared to DFT with a ~10<sup>6</sup> factor speedup and a negligible slowdown compared to ANI-1x. The resulting model is a valuable tool for drug development that can potentially replace both quantum calculations and classical force fields for myriad applications.</p>

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