scholarly journals AutoGraph: Autonomous Graph Based Clustering of Small-Molecule Conformations

2020 ◽  
Author(s):  
Kiyoto Tanemura ◽  
Susanta Das ◽  
Kenneth M. Merz Jr.
Keyword(s):  
Potential Energy ◽  
Potential Energy Surface ◽  
Clustering Algorithm ◽  
Energy Surface ◽  
Conformational Ensemble ◽  
Threshold Values ◽  
Optimal Method ◽  
Flexible Molecules ◽  
Hyperparameter Selection ◽  
Graph Based Clustering

<div> <div> <div> <p>While accurately modeling the conformational ensemble is required for predicting properties of flexible molecules, the optimal method of obtaining the conformational ensemble seems as varied as their applications. Ensemble structures have been modeled by generation, refinement, and clustering of conformations with a sufficient number of samples. We present a conformational clustering algorithm intended to automate the conformational clustering step through the Louvain algorithm, which requires minimal hyperparameters and importantly no predefined number of clusters or threshold values. The conformational graphs produced by this method for O-succinyl-L-homoserine, oxidized nicotinamide adenine dinucleotide, and 200 representative metabolites each preserved the geometric/energetic correlation expected for points on the potential energy surface. Clustering based on these graphs provide partitions informed by the potential energy surface. Automating conformational clustering in a workflow with AutoGraph may mitigate human biases introduced by guess-and-check over hyperparameter selection while allowing flexibility to the result by not imposing predefined criteria other than optimizing the model’s loss function. Associated codes are available at https://github.com/TanemuraKiyoto/AutoGraph . </p> </div> </div> </div>

scholarly journals AutoGraph: Autonomous Graph Based Clustering of Small-Molecule Conformations

2020 ◽  
Author(s):  
Kiyoto Tanemura ◽  
Susanta Das ◽  
Kenneth M. Merz Jr.
Keyword(s):  
Potential Energy ◽  
Potential Energy Surface ◽  
Clustering Algorithm ◽  
Energy Surface ◽  
Conformational Ensemble ◽  
Threshold Values ◽  
Optimal Method ◽  
Flexible Molecules ◽  
Hyperparameter Selection ◽  
Graph Based Clustering

<div> <div> <div> <p>While accurately modeling the conformational ensemble is required for predicting properties of flexible molecules, the optimal method of obtaining the conformational ensemble seems as varied as their applications. Ensemble structures have been modeled by generation, refinement, and clustering of conformations with a sufficient number of samples. We present a conformational clustering algorithm intended to automate the conformational clustering step through the Louvain algorithm, which requires minimal hyperparameters and importantly no predefined number of clusters or threshold values. The conformational graphs produced by this method for O-succinyl-L-homoserine, oxidized nicotinamide adenine dinucleotide, and 200 representative metabolites each preserved the geometric/energetic correlation expected for points on the potential energy surface. Clustering based on these graphs provide partitions informed by the potential energy surface. Automating conformational clustering in a workflow with AutoGraph may mitigate human biases introduced by guess-and-check over hyperparameter selection while allowing flexibility to the result by not imposing predefined criteria other than optimizing the model’s loss function. Associated codes are available at https://github.com/TanemuraKiyoto/AutoGraph . </p> </div> </div> </div>

Active Learning Accelerates Ab Initio Molecular Dynamics on Pericyclic Reactive Energy Surfaces

2020 ◽  
Author(s):  
Shi Jun Ang ◽  
Wujie Wang ◽  
Daniel Schwalbe-Koda ◽  
Simon Axelrod ◽  
Rafael Gomez-Bombarelli
Keyword(s):  
Molecular Dynamics ◽  
Potential Energy ◽  
Transition State ◽  
Potential Energy Surface ◽  
Ab Initio ◽  
Energy Surface ◽  
Computational Cost ◽  
Reactive Trajectories ◽  
Dynamics Simulations ◽  
Energy Surfaces

<div>Modeling dynamical effects in chemical reactions, such as post-transition state bifurcation, requires <i>ab initio</i> molecular dynamics simulations due to the breakdown of simpler static models like transition state theory. However, these simulations tend to be restricted to lower-accuracy electronic structure methods and scarce sampling because of their high computational cost. Here, we report the use of statistical learning to accelerate reactive molecular dynamics simulations by combining high-throughput ab initio calculations, graph-convolution interatomic potentials and active learning. This pipeline was demonstrated on an ambimodal trispericyclic reaction involving 8,8-dicyanoheptafulvene and 6,6-dimethylfulvene. With a dataset size of approximately</div><div>31,000 M062X/def2-SVP quantum mechanical calculations, the computational cost of exploring the reactive potential energy surface was reduced by an order of magnitude. Thousands of virtually costless picosecond-long reactive trajectories suggest that post-transition state bifurcation plays a minor role for the reaction in vacuum. Furthermore, a transfer-learning strategy effectively upgraded the potential energy surface to higher</div><div>levels of theory ((SMD-)M06-2X/def2-TZVPD in vacuum and three other solvents, as well as the more accurate DLPNO-DSD-PBEP86 D3BJ/def2-TZVPD) using about 10% additional calculations for each surface. Since the larger basis set and the dynamic correlation capture intramolecular non-covalent interactions more accurately, they uncover longer lifetimes for the charge-separated intermediate on the more accurate potential energy surfaces. The character of the intermediate switches from entropic to thermodynamic upon including implicit solvation effects, with lifetimes increasing with solvent polarity. Analysis of 2,000 reactive trajectories on the chloroform PES shows a qualitative agreement with the experimentally-reported periselectivity for this reaction. This overall approach is broadly applicable and opens a door to the study of dynamical effects in larger, previously-intractable reactive systems.</div>

Rotational de-excitations of C3H+ (1Σ+) by collision with He: new ab initio potential energy surface and scattering calculations

2020 ◽  
Vol 494 (4) ◽  
pp. 5675-5681 ◽  
Author(s):  
Sanchit Chhabra ◽  
T J Dhilip Kumar
Keyword(s):  
Potential Energy ◽  
Potential Energy Surface ◽  
Ab Initio ◽  
Cross Sections ◽  
Energy Surface ◽  
Molecular Ions ◽  
Basis Set ◽  
Rate Coefficients ◽  
Close Coupling ◽  
Collisional Rate Coefficients

ABSTRACT Molecular ions play an important role in the astrochemistry of interstellar and circumstellar media. C3H+ has been identified in the interstellar medium recently. A new potential energy surface of the C3H+–He van der Waals complex is computed using the ab initio explicitly correlated coupled cluster with the single, double and perturbative triple excitation [CCSD(T)-F12] method and the augmented correlation consistent polarized valence triple zeta (aug-cc-pVTZ) basis set. The potential presents a well of 174.6 cm−1 in linear geometry towards the H end. Calculations of pure rotational excitation cross-sections of C3H+ by He are carried out using the exact quantum mechanical close-coupling approach. Cross-sections for transitions among the rotational levels of C3H+ are computed for energies up to 600 cm−1. The cross-sections are used to obtain the collisional rate coefficients for temperatures T ≤ 100 K. Along with laboratory experiments, the results obtained in this work may be very useful for astrophysical applications to understand hydrocarbon chemistry.

scholarly journals Probing the location of the unpaired electron in spin–orbit changing collisions of NO with Ar

2020 ◽  
Vol 22 (39) ◽  
pp. 22289-22301
Author(s):  
Cornelia G. Heid ◽  
Imogen P. Bentham ◽  
Victoria Walpole ◽  
Razvan Gheorghe ◽  
Pablo G. Jambrina ◽  
...  
Keyword(s):  
Potential Energy ◽  
Potential Energy Surface ◽  
Unpaired Electron ◽  
Energy Surface ◽  
Spin Orbit ◽  
Selective Sampling

The ability to orient NO molecules prior to collision with Ar atoms allows selective sampling of different potential energy surface regions and elucidation of the associated collision pathways.

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