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.

scholarly journals New insight into the potential energy landscape and relaxation pathways of photoexcited aniline from CASSCF and XMCQDPT2 electronic structure calculations

2014 ◽  
Vol 16 (7) ◽  
pp. 3122-3133 ◽  
Author(s):  
Matthieu Sala ◽  
Oliver M. Kirkby ◽  
Stéphane Guérin ◽  
Helen H. Fielding
Keyword(s):  
Electronic Structure ◽  
Potential Energy ◽  
Excited States ◽  
Energy Landscape ◽  
Electronic Structure Calculations ◽  
Relaxation Dynamics ◽  
Potential Energy Landscape ◽  
Structure Calculations ◽  
Insight Into

New insight into the nonadiabatic relaxation dynamics of aniline following excitation to its first three singlet excited states, 11ππ*, 11π3s/πσ* and 21ππ*.

Active Learning of Many-Body Configuration Space: Application to the Cs+–water MB-nrg Potential Energy Function as a Case Study

2020 ◽  
Author(s):  
Yaoguang Zhai ◽  
Alessandro Caruso ◽  
Sicun Gao ◽  
Francesco Paesani
Keyword(s):  
Electronic Structure ◽  
Active Learning ◽  
Potential Energy ◽  
Molecular Simulations ◽  
Electronic Structure Calculations ◽  
Many Body ◽  
High Level ◽  
Structure Calculations ◽  
Training Sets

<div> <div> <div> <p>The efficient selection of representative configurations that are used in high-level electronic structure calculations needed for the development of many-body molecular models poses a challenge to current data-driven approaches to molecular simulations. Here, we introduce an active learning (AL) framework for generating training sets corresponding to individual many-body contributions to the energy of a N-body system, which are required for the development of MB-nrg potential energy functions (PEFs). Our AL framework is based on uncertainty and error estimation, and uses Gaussian process regression (GPR) to identify the most relevant configurations that are needed for an accurate representation of the energy landscape of the molecular system under exam. Taking the Cs<sup>+</sup>–water system as a case study, we demonstrate that the application of our AL framework results in significantly smaller training sets than previously used in the development of the original MB-nrg PEF, without loss of accuracy. Considering the computational cost associated with high-level electronic structure calculations for training set configurations, our AL framework is particularly well-suited to the development of many-body PEFs, with chemical and spectroscopic accuracy, for molecular simulations from the gas to condensed phase. </p> </div> </div> </div>

scholarly journals Dimension reduction in conformational analysis: a two-rotor mathematical model of amino acid diamide conformational potential energy surface

2017 ◽  
Vol 95 (8) ◽  
pp. 830-836 ◽  
Author(s):  
John Justine S. Villar ◽  
Adrian Roy L. Valdez ◽  
David H. Setiadi ◽  
Béla Fiser ◽  
Béla Viskolcz ◽  
...  
Keyword(s):  
Electronic Structure ◽  
Amino Acid ◽  
Potential Energy ◽  
Potential Energy Surface ◽  
Energy Surface ◽  
Electronic Structure Calculations ◽  
Small Error ◽  
Computational Time ◽  
Order Of Magnitude ◽  
Structure Calculations

The conformational potential energy surface (PES) of a molecule provides insights into the relative stability of the possible foldamers. However, the time and space complexity of electronic structure calculations, commonly used to generate PES, increases exponentially with an increasing number of atoms. The use of mathematical functions to model the topology of conformational PES is an alternative to more computer-intensive quantum chemical calculations, but the choice and complexity of functions used are crucial in achieving more accurate results. This paper presents a method to illustrate the topology of amino acid diamide PESs through a linear combination of a Fourier series and a mixture of Gaussian functions. Results yield a significantly small error, with an average RMSE of 4.9946 kJ mol−1 for all fits, which suggest that these functions may be used to represent the topology of the PESs, with around twofold order of magnitude decrease in computational time, with respect to DFT electronic structure calculations. This study ultimately aims to provide a foundation for a framework on building polypeptide PES from individual amino acid PESs.

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