scholarly journals MODELING OF ALKYL SALICYLATE COMPOUNDS AS UV ABSORBER BASED ON ELECTRONIC TRANSITION BY USING SEMIEMPIRICAL QUANTUM MECHANICS ZINDO/s CALCULATION

2010 ◽  
Vol 2 (1) ◽  
pp. 64-72 ◽  
Author(s):  
Iqmal Tahir ◽  
Karna Wijaya ◽  
Titik Subarni
Keyword(s):  
Electronic Transition ◽  
Single Point ◽  
Geometry Optimization ◽  
Energy Transition ◽  
Uv Protection ◽  
Semiempirical Methods ◽  
Excited Configuration ◽  
Uv C ◽  
Protection Activity ◽  
Alkyl Salicylate

Modeling of several alkyl salicylates based on electronic transition by using semiempriical mechanical quantum ZINDO/s calculation has been done. Object of these research were assumed only alkyl salicylates of C4 (butyl) until C8 (octyl) homologue with 4-7 example structures of each homologue. All of the computation have been performed using quantum chemistry - package software Hyperchem 6.0. The research covered about drawing each of the structure, geometry optimization using semiempirical AM1 algorithm and followed with single point calculation using semiempirical ZINDO/s technique. ZINDO/s calculations used a defined criteria that is singly excited - Configuration Interaction (CI), gap of HOMO-LUMO energy transition was 2 and degeneracy level was 3. Analysis of the theoretical spectra was focused in the UV-B (290-320 nm) and UV-C (200-290 nm) area. The result showed that modeling of the compound can be used for predicting the type of UV protection activity depending with the electronic transition in the UV area. Modification of the alkyl homologue relatively did not change the value of wavelength absorbtion to indicate the UV protection activity. Alkyl salicylate compounds were predicted as UV-C sunscreen or relatively the compounds have protection effect for UV-C.   Keywords: alkyl salicylate, sunscreen, semiempirical methods

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.

scholarly journals Theoretical Investigation of the Size Effect on the Oxygen Adsorption Energy of Coinage Metal Nanoparticles

2021 ◽  
Author(s):  
Amir H. Hakimioun ◽  
Elisabeth M. Dietze ◽  
Bart D. Vandegehuchte ◽  
Daniel Curulla-Ferre ◽  
Lennart Joos ◽  
...  
Keyword(s):  
Particle Size ◽  
Size Effect ◽  
Adsorption Energy ◽  
Single Point ◽  
Finite Size ◽  
Geometry Optimization ◽  
Oxygen Adsorption ◽  
Finite Size Effect ◽  
Full Geometry Optimization ◽  
Coinage Metal

AbstractThis study evaluates the finite size effect on the oxygen adsorption energy of coinage metal (Cu, Ag and Au) cuboctahedral nanoparticles in the size range of 13 to 1415 atoms (0.7–3.5 nm in diameter). Trends in particle size effects are well described with single point calculations, in which the metal atoms are frozen in their bulk position and the oxygen atom is added in a location determined from periodic surface calculations. This is shown explicitly for Cu nanoparticles, for which full geometry optimization only leads to a constant offset between relaxed and unrelaxed adsorption energies that is independent of particle size. With increasing cluster size, the adsorption energy converges systematically to the limit of the (211) extended surface. The 55-atomic cluster is an outlier for all of the coinage metals and all three materials show similar behavior with respect to particle size. Graphic Abstract

Modeling of a Rotaxane-based Molecular Device

2002 ◽  
Vol 741 ◽  
Author(s):  
Xiange Zheng ◽  
Karl Sohlberg
Keyword(s):  
Single Point ◽  
Computational Cost ◽  
Geometry Optimization ◽  
Potential Energy Function ◽  
Computational Procedure ◽  
Structural Features ◽  
Full Geometry Optimization ◽  
Point Energy ◽  
Semi Empirical ◽  
Relationship Of

ABSTRACTA computational procedure is presented for investigating photo-induced switchable rotaxanes and demonstrated for a known system. This procedure starts with the generation of more than 104 chemically reasonable rotaxane conformations based on an empirical intramolecular potential energy function. Single-point energy calculations at the semi-empirical (AM1) level are carried out for each structure in the singlet (ground), triplet, and anionic doublet states. The structural features are assigned and then correlated with energy for each state. What emerges is a profile of the structure-energy relationship that captures the salient features of the system that endow it with device-like character. Full geometry optimization of a subset of co-conformations (∼1%) demonstrates that the procedure based on single-point calculations is sufficient to obtain a profile of the relationship of structural features to energy that is consistent with experiments, at greatly reduced computational cost.

Conceptual Synthesis of Compliance at a Single Point

2006 ◽  
Author(s):  
Charles Kim ◽  
Yong-Mo Moon ◽  
Sridhar Kota
Keyword(s):  
Building Block ◽  
Compliant Mechanism ◽  
Single Point ◽  
Geometry Optimization ◽  
Building Blocks ◽  
Kinematic Behavior ◽  
The Mathematical Model ◽  
Insight Into ◽  
Block Approach

In this paper, we investigate a methodology for the conceptual synthesis of compliance at a single point based on a building block approach. The methodology lays the foundation for more general compliant mechanism synthesis problems involving multiple points of interest (i.e. inputs and outputs). In the building block synthesis, the problem specifications are decomposed into related sub-problems if a single building block cannot perform the desired task. The sub-problems are tested against the library of building blocks until a suitable building block is determined. The synthesized design is composed of an assembly of the building blocks to provide the desired functionality. The building block approach is intuitive and provides key insight into how individual building blocks contribute to the overall function. We investigate the basic kinematic behavior of individual building blocks and relate this to the behavior of a design composed of building blocks. This serves to not only generate viable solutions but also to augment the understanding of the designer. Once a feasible concept is thus generated, known methods for size and geometry optimization may be employed to fine tune performance. The key enabler of the building block synthesis is the method of capturing kinematic behavior using Compliance Ellipsoids. The mathematical model of the compliance ellipsoids facilitates the characterization of the building blocks, transformation of problem specifications, decomposition into sub-problems, and the ability to search for alternate solutions. The compliance ellipsoids also give insight into how individual building blocks contribute to the overall kinematic function. The effectiveness and generality of the methodology are demonstrated through a synthesis example. Using only a limited set of building blocks, the methodology is capable of addressing generic kinematic problem specifications.

Modeling of a Rotaxane-based Molecular Device

2002 ◽  
Vol 761 ◽  
Author(s):  
Xiange Zheng ◽  
Karl Sohlberg
Keyword(s):  
Single Point ◽  
Computational Cost ◽  
Geometry Optimization ◽  
Potential Energy Function ◽  
Computational Procedure ◽  
Structural Features ◽  
Full Geometry Optimization ◽  
Point Energy ◽  
Semi Empirical ◽  
Relationship Of

ABSTRACTA computational procedure is presented for investigating photo-induced switchable rotaxanes and demonstrated for a known system. This procedure starts with the generation of more than 104 chemically reasonable rotaxane conformations based on an empirical intramolecular potential energy function. Single-point energy calculations at the semi-empirical (AM1) level are carried out for each structure in the singlet (ground), triplet, and anionic doublet states. The structural features are assigned and then correlated with energy for each state. What emerges is a profile of the structure-energy relationship that captures the salient features of the system that endow it with device-like character. Full geometry optimization of a subset of co-conformations (∼1%) demonstrates that the procedure based on single-point calculations is sufficient to obtain a profile of the relationship of structural features to energy that is consistent with experiments, at greatly reduced computational cost.

scholarly journals Calculation of the Proton Affinities of Primary, Secondary, and Tertiary Amines Using Semiempirical and ab initio Methods

2000 ◽  
Vol 55 (8) ◽  
pp. 687-694 ◽  
Author(s):  
Gerhard Raabe ◽  
Yuekui Wang ◽  
Jörg Fleischhauer
Keyword(s):  
Ab Initio ◽  
Preliminary Investigation ◽  
Geometry Optimization ◽  
Dft Method ◽  
Semiempirical Methods ◽  
Tertiary Amines ◽  
Proton Affinities ◽  
Bonding Parameters ◽  
Experimental Values ◽  
The One

The proton affinities of some primary, secondary, and tertiary amines have been calculated with different semiempirical and nonempirical quantum chemical methods. We were particularly interested in the question which of the most popular semiempirical methods yield good overall correlations between calculated and experimental values and, therefore, allow a reliable prediction of hitherto unknown proton affinities. We found that some of the most frequently used semiempirical methods result in good correlations only within the groups of primary, secondary and tertiary amines, while the overall correlation is even worse than the one obtained with the noniterative EHT method. Among the more recent methods which allow geometry optimizations (MINDO/3, MNDO, AMI, PM3, MSINDO) the best results have been calculated with the MSINDO method. Testing for the influence of geometry optimization we surprisingly found that two of these methods (MINDO/3, AMI) perform even better when geometry optimizations are omitted and standard bonding parameters are used instead. Superior results, however, have been obtained with the CNDO/2- and the INDO method. Finally, the best correlations between semiempirically calculated and experimental proton affinities have been achieved with the spectroscopic parametrizations of these methods, CNDO/2S and INDO/2S, respectively. The correlations resulting in these cases are close to those reached at the ZPE+MP2/6-31 l++G**//HF/6-311++G** level of ab initio theory and with a comparable DFT method. A preliminary investigation revealed that an improvement in the semiempirical calculation of proton affinities might be obtained if different Uμμ parameters are used for the nitrogen atoms of primary, secondary, and tertiary amines.

scholarly journals A Model Study on the Possible Effects of an External Electrical Field on Enzymes Having Dinuclear Iron Cluster [2Fe-2S]

2012 ◽  
Vol 2012 ◽  
pp. 1-9
Author(s):  
Lemi Türker
Keyword(s):  
Electric Field ◽  
External Electric Field ◽  
Chemical Reactivity ◽  
Single Point ◽  
Geometry Optimization ◽  
Chemical Properties ◽  
Iron Cluster ◽  
Model Study ◽  
Amide Form ◽  
Dinuclear Iron

Hydrogenases which catalyze the H2↔ 2H++ 2e−reaction are metalloenzymes that can be divided into two classes, the NiFe and Fe enzymes, on the basis of their metal content. Iron-sulfur clusters [2Fe-2S] and [4Fe-4S] are common in ironhydrogenases. In the present model study, [2Fe-2S] cluster has been considered to visualize the effect of external electric field on various quantum chemical properties of it. In the model, all the cysteinyl residues are in the amide form. The PM3 type semiempirical calculations have been performed for the geometry optimization of the model structure in the absence and presence of the external field. Then, single point DFT calculations (B3LYP/6-31+G(d)) have been carried out. Depending on the direction of the field, the chemical reactivity of the model enzyme varies which suggests that an external electric field could, under proper conditions, improve the enzymatic hydrogen production.

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