Investigation of the torsional barrier of EDOT using molecular mechanics and DFT methods

Expanded porphyrins provide a versatile route to molecular switching devices due to their ability to shift between several π-conjugation topologies encoding distinct properties. Taking into account its size and huge conformational flexibility, DFT remains the workhorse for modeling such extended macrocycles. Nevertheless, the stability of Hückel and Möbius conformers depends on a complex interplay of different factors, such as hydrogen bonding, p···p stacking, steric effects, ring strain and electron delocalization. As a consequence, the selection of an exchange-correlation functional for describing the energy profile of topological switches is very difficult. For these reasons, we have examined the performance of a variety of wavefunction methods and density functionals for describing the thermochemistry and kinetics of topology interconversions across a wide range of macrocycles. Especially for hexa- and heptaphyrins, the Möbius structures have a pronouncedly stronger degree of static correlation than the Hückel and figure-eight structures, and as a result the relative energies of singly-twisted structures are a challenging test for electronic structure methods. Comparison of limited orbital space full CI calculations with CCSD(T) calculations within the same active spaces shows that post-CCSD(T) correlation contributions to relative energies are very minor. At the same time, relative energies are weakly sensitive to further basis set expansion, as proven by the minor energy differences between MP2/cc-pVDZ and explicitly correlated MP2-F12/cc-pVDZ-F12 calculations. Hence, our CCSD(T) reference values are reasonably well-converged in both 1-particle and n-particle spaces. While conventional MP2 and MP3 yield very poor results, SCS-MP2 and particularly SOS-MP2 and SCS-MP3 agree to better than 1 kcal mol-1 with the CCSD(T) relative energies. Regarding DFT methods, only M06-2X provides relative errors close to chemical accuracy with a RMSD of 1.2 kcal mol-1. While the original DSD-PBEP86 double hybrid performs fairly poorly for these extended p-systems, the errors drop down to 2 kcal mol-1 for the revised revDSD-PBEP86-NL, again showing that same-spin MP2-like correlation has a detrimental impact on performance like the SOS-MP2 results.

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On-the-Fly Determination of Active Region Centers in Adaptive-Partitioning QM/MM

10.26434/chemrxiv.12376919 ◽

2020 ◽

Author(s):

Zenghui Yang

Keyword(s):

Quantum Mechanics ◽

Active Region ◽

Molecular Mechanics ◽

Active Regions ◽

Available Energy ◽

Adaptive Partitioning ◽

Different Levels

Quantum mechanics/molecular mechanics (QM/MM) methods partition the system into active and environmental regions and treat them with different levels of theory, achieving accuracy and efficiency at the same time. Adaptive-partitioning (AP) QM/MM methods allow on-the-fly changes to the QM/MM partitioning of the system. Many of the available energy-based AP-QM/MM methods partition the system according to distances to pre-chosen centers of active regions. For such AP-QM/MM methods, I develop an adaptive-center (AC) method that allows on-the-fly determination of the centers of active regions according to general geometrical or potential-related criteria, extending the range of application of energy-based AP-QM/MM methods to systems where active regions may occur or vanish during the simulation.

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A Conserved Asparagine in a Ubiquitin Conjugating Enzyme Positions the Substrate for Nucleophilic Attack

10.26434/chemrxiv.7019579.v1 ◽

2018 ◽

Cited By ~ 1

Author(s):

Walker M. Jones ◽

Aaron G. Davis ◽

R. Hunter Wilson ◽

Katherine L. Elliott ◽

Isaiah Sumner

Keyword(s):

Molecular Dynamics ◽

Quantum Mechanics ◽

Molecular Mechanics ◽

Reaction Rates ◽

Nucleophilic Attack ◽

Classical Molecular Dynamics ◽

Ubiquitin Conjugating Enzyme ◽

Conjugating Enzyme

We present classical molecular dynamics (MD), Born-Oppenheimer molecular dynamics (BOMD), and hybrid quantum mechanics/molecular mechanics (QM/MM) data. MD was performed using the GPU accelerated pmemd module of the AMBER14MD package. BOMD was performed using CP2K version 2.6. The reaction rates in BOMD were accelerated using the Metadynamics method. QM/MM was performed using ONIOM in the Gaussian09 suite of programs. Relevant input files for BOMD and QM/MM are available.

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Accurate and Transferable Multitask Prediction of Chemical Properties with an Atoms-in-Molecule Neural Network

10.26434/chemrxiv.7151435.v2 ◽

2018 ◽

Author(s):

Roman Zubatyuk ◽

Justin S. Smith ◽

Jerzy Leszczynski ◽

Olexandr Isayev

Keyword(s):

Neural Network ◽

Molecular System ◽

Computational Cost ◽

Chemical Properties ◽

The State ◽

Molecular Properties ◽

Training Data ◽

Dft Methods ◽

Benchmark Datasets ◽

Quantum Phenomena

Atomic and molecular properties could be evaluated from the fundamental Schrodinger’s equation and therefore represent different modalities of the same quantum phenomena. Here we present AIMNet, a modular and chemically inspired deep neural network potential. We used AIMNet with multitarget training to learn multiple modalities of the state of the atom in a molecular system. The resulting model shows on several benchmark datasets the state-of-the-art accuracy, comparable to the results of orders of magnitude more expensive DFT methods. It can simultaneously predict several atomic and molecular properties without an increase in computational cost. With AIMNet we show a new dimension of transferability: the ability to learn new targets utilizing multimodal information from previous training. The model can learn implicit solvation energy (like SMD) utilizing only a fraction of original training data, and archive MAD error of 1.1 kcal/mol compared to experimental solvation free energies in MNSol database.

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A Systematic DFT Study of Two Elementary Steps Involving Hydrogen Peroxide and the Hydroxyl Radical in the Fenton Reaction

10.26434/chemrxiv.6160142 ◽

2018 ◽

Author(s):

Danilo Carmona ◽

David Contreras ◽

Oscar A. Douglas-Gallardo ◽

Stefan Vogt-Geisse ◽

Pablo Jaque ◽

...

Keyword(s):

Hydrogen Peroxide ◽

Hydroxyl Radical ◽

Ab Initio ◽

Fenton Reaction ◽

Basis Set ◽

Basis Sets ◽

Dft Methods ◽

Elementary Steps ◽

Oxygen Oxygen ◽

Complete Basis Set

The Fenton reaction plays a central role in many chemical and biological processes and has various applications as e.g. water remediation. The reaction consists of the iron-catalyzed homolytic cleavage of the oxygen-oxygen bond in the hydrogen peroxide molecule and the reduction of the hydroxyl radical. Here, we study these two elementary steps with high-level ab-initio calculations at the complete basis set limit and address the performance of different DFT methods following a specific classification based on the Jacob´s ladder in combination with various Pople's basis sets. Ab-initio calculations at the complete basis set limit are in agreement to experimental reference data and identified a significant contribution of the electron correlation energy to the bond dissociation energy (BDE) of the oxygen-oxygen bond in hydrogen peroxide and the electron affinity (EA) of the hydroxyl radical. The studied DFT methods were able to reproduce the ab-initio reference values, although no functional was particularly better for both reactions. The inclusion of HF exchange in the DFT functionals lead in most cases to larger deviations, which might be related to the poor description of the two reactions by the HF method. Considering the computational cost, DFT methods provide better BDE and EA values than HF and post--HF methods with an almost MP2 or CCSD level of accuracy. However, no systematic general prediction of the error based on the employed functional could be established and no systematic improvement with increasing the size in the Pople's basis set was found, although for BDE values certain systematic basis set dependence was observed. Moreover, the quality of the hydrogen peroxide, hydroxyl radical and hydroxyl anion structures obtained from these functionals was compared to experimental reference data. In general, bond lengths were well reproduced and the error in the angles were between one and two degrees with some systematic trend with the basis sets. From our results we conclude that DFT methods present a computationally less expensive alternative to describe the two elementary steps of the Fenton reaction. However, choice of approximated functionals and basis sets must be carefully done and the provided benchmark allows a systematic validation of the electronic structure method to be employed

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

10.26434/chemrxiv.9763094 ◽

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.

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