Approximate factorization of molecular potential surfaces. I. Basic approach

1997 ◽  
Vol 106 (14) ◽  
pp. 5874-5893 ◽  
Author(s):  
D. Madsen ◽  
R. Pearman ◽  
M. Gruebele
Keyword(s):  
Approximate Factorization ◽  
Potential Surfaces ◽  
Basic Approach ◽  
Molecular Potential

Approximate Factorization of Molecular Potential Surfaces II. Internal Rotors

2000 ◽  
Vol 214 (11) ◽  
Author(s):  
R. Pearman ◽  
M. Gruebele
Keyword(s):  
Potential Energy ◽  
Energy Surface ◽  
Vibrational Energy ◽  
Approximate Factorization ◽  
Potential Surfaces ◽  
Energy Redistribution ◽  
Large Molecules ◽  
Molecular Potential ◽  
Scaling Models ◽  
Random Matrix Models

Scaling models for the polyatomic potential energy surface occupy a ground intermediate between ab initio surfaces and random matrix models. They are useful for the study of vibrational energy redistribution (IVR) in large molecules, and have been shown to reproduce many features of more accurate spectroscopically fitted or

An application of the functional Boys-Bernardi counterpoise method to molecular potential surfaces

1973 ◽  
Vol 29 (2) ◽  
pp. 167-172 ◽  
Author(s):  
Allan Johansson ◽  
Peter Kollman ◽  
Steve Rothenberg
Keyword(s):  
Potential Surfaces ◽  
Molecular Potential ◽  
Counterpoise Method

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>

Sign in / Sign up

Export Citation Format

Share Document