scholarly journals Evaluation of Thermochemical Machine Learning for Potential Energy Curves and Geometry Optimization

2021 ◽  
Author(s):  
Dakota L. Folmsbee ◽  
David R. Koes ◽  
Geoffrey R. Hutchison
Keyword(s):  
Machine Learning ◽  
Potential Energy ◽  
Geometry Optimization ◽  
Potential Energy Curves

scholarly journals Evaluation of Thermochemical Machine Learning for Potential Energy Curves and Geometry Optimization

2020 ◽  
Author(s):  
Dakota Folmsbee ◽  
David R. Koes ◽  
Geoffrey Hutchison
Keyword(s):  
Machine Learning ◽  
Potential Energy ◽  
Density Functional ◽  
Single Point ◽  
Geometry Optimization ◽  
Potential Energy Curves ◽  
Semiempirical Methods ◽  
Neural Net ◽  
Quantitative Accuracy ◽  
The Moment

While many machine learning methods, particularly deep neural networks have been trained for density functional and quantum chemical energies and properties, the vast majority of these methods focus on single-point energies. In principle, such ML methods, once trained, offer thermochemical accuracy on par with density functional and wave function methods but at speeds comparable to traditional force fields or approximate semiempirical methods. So far, most efforts have focused on optimized equilibrium single-point energies and properties. In this work, we evaluate the accuracy of several leading ML methods across a range of bond potential energy curves and torsional potentials. Methods were trained on the existing ANI-1 training set, calculated using the ωB97X / 6-31G(d) single points at non-equilibrium geometries. We find that across a range of small molecules, several methods offer both qualitative accuracy (e.g., correct minima, both repulsive and attractive bond regions, anharmonic shape, and single minima) and quantitative accuracy in terms of the mean absolute percent error near the minima. At the moment, ANI-2x, FCHL, and our new grid-based convolutional neural net show good performance.

scholarly journals Evaluation of Thermochemical Machine Learning for Potential Energy Curves and Geometry Optimization

2020 ◽  
Author(s):  
Dakota Folmsbee ◽  
David R. Koes ◽  
Geoffrey Hutchison
Keyword(s):  
Machine Learning ◽  
Potential Energy ◽  
Density Functional ◽  
Single Point ◽  
Geometry Optimization ◽  
Potential Energy Curves ◽  
Semiempirical Methods ◽  
Neural Net ◽  
Quantitative Accuracy ◽  
The Moment

While many machine learning methods, particularly deep neural networks have been trained for density functional and quantum chemical energies and properties, the vast majority of these methods focus on single-point energies. In principle, such ML methods, once trained, offer thermochemical accuracy on par with density functional and wave function methods but at speeds comparable to traditional force fields or approximate semiempirical methods. So far, most efforts have focused on optimized equilibrium single-point energies and properties. In this work, we evaluate the accuracy of several leading ML methods across a range of bond potential energy curves and torsional potentials. Methods were trained on the existing ANI-1 training set, calculated using the ωB97X / 6-31G(d) single points at non-equilibrium geometries. We find that across a range of small molecules, several methods offer both qualitative accuracy (e.g., correct minima, both repulsive and attractive bond regions, anharmonic shape, and single minima) and quantitative accuracy in terms of the mean absolute percent error near the minima. At the moment, ANI-2x, FCHL, and our new grid-based convolutional neural net show good performance.

scholarly journals Evaluation of Thermochemical Machine Learning for Potential Energy Curves and Geometry Optimization

2021 ◽  
Author(s):  
Dakota Folmsbee ◽  
David R. Koes ◽  
Geoffrey Hutchison
Keyword(s):  
Machine Learning ◽  
Potential Energy ◽  
Density Functional ◽  
Single Point ◽  
Geometry Optimization ◽  
Potential Energy Curves ◽  
Semiempirical Methods ◽  
Neural Net ◽  
Quantitative Accuracy ◽  
The Moment

While many machine learning methods, particularly deep neural networks have been trained for density functional and quantum chemical energies and properties, the vast majority of these methods focus on single-point energies. In principle, such ML methods, once trained, offer thermochemical accuracy on par with density functional and wave function methods but at speeds comparable to traditional force fields or approximate semiempirical methods. So far, most efforts have focused on optimized equilibrium single-point energies and properties. In this work, we evaluate the accuracy of several leading ML methods across a range of bond potential energy curves and torsional potentials. Methods were trained on the existing ANI-1 training set, calculated using the ωB97X / 6-31G(d) single points at non-equilibrium geometries. We find that across a range of small molecules, several methods offer both qualitative accuracy (e.g., correct minima, both repulsive and attractive bond regions, anharmonic shape, and single minima) and quantitative accuracy in terms of the mean absolute percent error near the minima. At the moment, ANI-2x, FCHL, and our new grid-based convolutional neural net show good performance.

A Non-Planar Iodinated Pyrrole Study

2016 ◽  
Vol 1819 ◽  
Author(s):  
E.A. Valenzuela-Hermosillo ◽  
J.H. Pacheco-Sánchez
Keyword(s):  
Potential Energy ◽  
Dft Calculations ◽  
Iodine Atom ◽  
Geometry Optimization ◽  
Potential Energy Curves ◽  
Conductive Material ◽  
Pyramidal Structure ◽  
Pyramidal Configuration ◽  
Structure Configuration

ABSTRACTNon-planar iodinated pyrrole structures were found through DFT calculations of geometry optimization, when doping one pyrrole molecule with iodine atoms. This take us to a new mono-iodinated pyrrole structure in which one pyrrole molecule is attacked with one iodine atom in a pyramidal configuration. Then, the pyrrole molecule was attacked with two and until four optimized linear iodine atoms in a pyramidal structure configuration. The corresponding potential energy curves were also constructed in order to know what kind of adsorption (physisorption or chemisorption) is obtained, considering physisorption as lower than ten kcal/mol, and chemisorption greater than twenty kcal/mol according to the literature. Finally, it is known that halogenated pyrrole is a highly conductive material required in several fields.

A THEORETICAL STUDY OF BISTABILITY OF POLYDIACETYLENE: TCDU(poly(5,7-dodecadiyne-1,12-diyl-bis-phenylurethane)

2001 ◽  
Vol 15 (28n30) ◽  
pp. 3821-3824
Author(s):  
HIDEKI KATAGIRI ◽  
YUKIHIRO SHIMOI ◽  
SHUJI ABE
Keyword(s):  
Potential Energy ◽  
First Principles ◽  
Theoretical Study ◽  
Geometry Optimization ◽  
Optimization Procedure ◽  
Stable Structure ◽  
Type Structure ◽  
Potential Energy Curves ◽  
First Principles Calculation ◽  
Bond Lengths

We performed a first-principles calculation of typical polydiacetylene (PDA), TCDU (poly(5,7-dodecadiyne-1,12-diyl-bis-phenylurethane)). Potential energy curves (PEC's) as a function of two bond lengths of the backbone chain are presented. The present PEC's show that TCDU has only an acetylene-type stable structure and a butatriene-type structure is unstable, consistent with our previous calculations with a geometry optimization procedure. This result is in contrast to the case of a hypothetical hydrogen-substituted PDA where a butatriene-type structure is obtained as a meta-stable structure.

BAND NN: A Deep Learning Framework For Energy Prediction and Geometry Optimization of Organic Small Molecules

2019 ◽  
Author(s):  
Siddhartha Laghuvarapu ◽  
Yashaswi Pathak ◽  
U. Deva Priyakumar
Keyword(s):  
Machine Learning ◽  
Density Functional ◽  
Computational Cost ◽  
Geometry Optimization ◽  
Dft Methods ◽  
Energy Prediction ◽  
Machine Learning Model ◽  
Equilibrium Structures ◽  
High Level ◽  
Non Equilibrium

Recent advances in artificial intelligence along with development of large datasets of energies calculated using quantum mechanical (QM)/density functional theory (DFT) methods have enabled prediction of accurate molecular energies at reasonably low computational cost. However, machine learning models that have been reported so far requires the atomic positions obtained from geometry optimizations using high level QM/DFT methods as input in order to predict the energies, and do not allow for geometry optimization. In this paper, a transferable and molecule-size independent machine learning model (BAND NN) based on a chemically intuitive representation inspired by molecular mechanics force fields is presented. The model predicts the atomization energies of equilibrium and non-equilibrium structures as sum of energy contributions from bonds (B), angles (A), nonbonds (N) and dihedrals (D) at remarkable accuracy. The robustness of the proposed model is further validated by calculations that span over the conformational, configurational and reaction space. The transferability of this model on systems larger than the ones in the dataset is demonstrated by performing calculations on select large molecules. Importantly, employing the BAND NN model, it is possible to perform geometry optimizations starting from non-equilibrium structures along with predicting their energies.

Potential energy curves of low-lying states of HNO

1979 ◽  
Vol 66 (3) ◽  
pp. 523-526 ◽  
Author(s):  
Okio Nomura ◽  
Suehiro Iwata
Keyword(s):  
Potential Energy ◽  
Potential Energy Curves

scholarly journals Exploration of interlacing and avoided crossings in a manifold of potential energy curves by a unitary group adapted state specific multi-reference perturbation theory (UGA-SSMRPT)

2021 ◽  
Vol 155 (1) ◽  
pp. 014101
Author(s):  
Dibyajyoti Chakravarti ◽  
Koustav Hazra ◽  
Riya Kayal ◽  
Sudip Sasmal ◽  
Debashis Mukherjee
Keyword(s):  
Perturbation Theory ◽  
Potential Energy ◽  
Unitary Group ◽  
Potential Energy Curves ◽  
Avoided Crossings
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