Computational High-Throughput Screening of Polymeric Photocatalysts: Exploring the Effect of Composition, Sequence Isomerism and Conformational Degrees of Freedom

Degrees Of Freedom ◽

Chemical Space ◽

Low Cost ◽

Computational Screening ◽

Computational Workflow ◽

We discuss a low-cost computational workflow for the high-throughput screening of polymeric photocatalysts and demonstrate its utility by applying it to a number of challenging problems that would be difficult to tackle otherwise. Specifically we show how having access to a low-cost method allows one to screen a vast chemical space, as well as to probe the effects of conformational degrees of freedom and sequence isomerism. Finally, we discuss both the opportunities of computational screening in the search for polymer photocatalysts, as well as the biggest challenges.

Computational High-Throughput Screening of Polymeric Photocatalysts: Exploring the Effect of Composition, Sequence Isomerism and Conformational Degrees of Freedom

10.26434/chemrxiv.7314929.v1 ◽

2018 ◽

Author(s):

isabelle Heath-Apostolopoulos ◽

Liam Wilbraham ◽

Martijn Zwijnenburg

Keyword(s):

High Throughput ◽

Degrees Of Freedom ◽

Chemical Space ◽

Low Cost ◽

Computational Screening ◽

Computational Workflow ◽

We discuss a low-cost computational workflow for the high-throughput screening of polymeric photocatalysts and demonstrate its utility by applying it to a number of challenging problems that would be difficult to tackle otherwise. Specifically we show how having access to a low-cost method allows one to screen a vast chemical space, as well as to probe the effects of conformational degrees of freedom and sequence isomerism. Finally, we discuss both the opportunities of computational screening in the search for polymer photocatalysts, as well as the biggest challenges.

Computational High-Throughput Screening of Polymeric Photocatalysts: Exploring the Effect of Composition, Sequence Isomerism and Conformational Degrees of Freedom

10.26434/chemrxiv.7314929 ◽

2018 ◽

Author(s):

isabelle Heath-Apostolopoulos ◽

Liam Wilbraham ◽

Martijn Zwijnenburg

Keyword(s):

High Throughput ◽

Degrees Of Freedom ◽

Chemical Space ◽

Low Cost ◽

Computational Screening ◽

Computational Workflow ◽

We discuss a low-cost computational workflow for the high-throughput screening of polymeric photocatalysts and demonstrate its utility by applying it to a number of challenging problems that would be difficult to tackle otherwise. Specifically we show how having access to a low-cost method allows one to screen a vast chemical space, as well as to probe the effects of conformational degrees of freedom and sequence isomerism. Finally, we discuss both the opportunities of computational screening in the search for polymer photocatalysts, as well as the biggest challenges.

Computational High-Throughput Screening of Polymeric Photocatalysts: Exploring the Effect of Composition, Sequence Isomerism and Conformational Degrees of Freedom

10.26434/chemrxiv.7314929.v2 ◽

2018 ◽

Author(s):

isabelle Heath-Apostolopoulos ◽

Liam Wilbraham ◽

Martijn Zwijnenburg

Keyword(s):

High Throughput ◽

Degrees Of Freedom ◽

Chemical Space ◽

Low Cost ◽

Computational Screening ◽

Computational Workflow ◽

We discuss a low-cost computational workflow for the high-throughput screening of polymeric photocatalysts and demonstrate its utility by applying it to a number of challenging problems that would be difficult to tackle otherwise. Specifically we show how having access to a low-cost method allows one to screen a vast chemical space, as well as to probe the effects of conformational degrees of freedom and sequence isomerism. Finally, we discuss both the opportunities of computational screening in the search for polymer photocatalysts, as well as the biggest challenges.

Computational high-throughput screening of polymeric photocatalysts: exploring the effect of composition, sequence isomerism and conformational degrees of freedom

Faraday Discussions ◽

10.1039/c8fd00171e ◽

2019 ◽

Vol 215 ◽

pp. 98-110 ◽

Cited By ~ 2

Author(s):

Isabelle Heath-Apostolopoulos ◽

Liam Wilbraham ◽

Martijn A. Zwijnenburg

Keyword(s):

High Throughput ◽

Degrees Of Freedom ◽

Low Cost ◽

Computational Workflow ◽

We discuss a low-cost computational workflow for the high throughput screening of polymeric photocatalysts.

Data-Driven Approaches Can Overcome Limitations in Multireference Diagnostics

10.26434/chemrxiv.12115944.v1 ◽

2020 ◽

Author(s):

Chenru Duan ◽

Fang Liu ◽

Aditya Nandy ◽

Heather Kulik

Keyword(s):

High Throughput ◽

Density Functional ◽

Correlation Energy ◽

Low Cost ◽

Small Data ◽

Data Sets ◽

Strongly Correlated ◽

Heavy Atoms ◽

Computational Screening

High-throughput computational screening typically employs methods (i.e., density functional theory or DFT) that can fail to describe challenging molecules, such as those with strongly correlated electronic structure. In such cases, multireference (MR) correlated wavefunction theory (WFT) would be the appropriate choice but remains more challenging to carry out and automate than single-reference (SR) WFT or DFT. Numerous diagnostics have been proposed for identifying when MR character is likely to have an effect on the predictive power of SR calculations, but conflicting conclusions about diagnostic performance have been reached on small data sets. We compute 15 MR diagnostics, ranging from affordable DFT-based to more costly MR-WFT-based diagnostics, on a set of 3,165 equilibrium and distorted small organic molecules containing up to six heavy atoms. Conflicting MR character assignments and low pairwise linear correlations among diagnostics are also observed over this set. We evaluate the ability of existing diagnostics to predict the percent recovery of the correlation energy, %<i>E</i><sub>corr</sub>. None of the DFT-based diagnostics are nearly as predictive of %<i>E</i><sub>corr</sub> as the best WFT-based diagnostics. To overcome the limitation of this cost–accuracy trade-off, we develop machine learning (ML, i.e., kernel ridge regression) models to predict WFT-based diagnostics from a combination of DFT-based diagnostics and a new, size-independent 3D geometric representation. The ML-predicted diagnostics correlate as well with MR effects as their computed (i.e., with WFT) values, significantly improving over the DFT-based diagnostics on which the models were trained. These ML models thus provide a promising approach to improve upon DFT-based diagnostic accuracy while remaining suitably low cost for high-throughput screening.

Data-Driven Approaches Can Overcome Limitations in Multireference Diagnostics

10.26434/chemrxiv.12115944 ◽

2020 ◽

Author(s):

Chenru Duan ◽

Fang Liu ◽

Aditya Nandy ◽

Heather Kulik

Keyword(s):

High Throughput ◽

Density Functional ◽

Correlation Energy ◽

Low Cost ◽

Small Data ◽

Data Sets ◽

Strongly Correlated ◽

Heavy Atoms ◽

Computational Screening

High-throughput computational screening typically employs methods (i.e., density functional theory or DFT) that can fail to describe challenging molecules, such as those with strongly correlated electronic structure. In such cases, multireference (MR) correlated wavefunction theory (WFT) would be the appropriate choice but remains more challenging to carry out and automate than single-reference (SR) WFT or DFT. Numerous diagnostics have been proposed for identifying when MR character is likely to have an effect on the predictive power of SR calculations, but conflicting conclusions about diagnostic performance have been reached on small data sets. We compute 15 MR diagnostics, ranging from affordable DFT-based to more costly MR-WFT-based diagnostics, on a set of 3,165 equilibrium and distorted small organic molecules containing up to six heavy atoms. Conflicting MR character assignments and low pairwise linear correlations among diagnostics are also observed over this set. We evaluate the ability of existing diagnostics to predict the percent recovery of the correlation energy, %<i>E</i><sub>corr</sub>. None of the DFT-based diagnostics are nearly as predictive of %<i>E</i><sub>corr</sub> as the best WFT-based diagnostics. To overcome the limitation of this cost–accuracy trade-off, we develop machine learning (ML, i.e., kernel ridge regression) models to predict WFT-based diagnostics from a combination of DFT-based diagnostics and a new, size-independent 3D geometric representation. The ML-predicted diagnostics correlate as well with MR effects as their computed (i.e., with WFT) values, significantly improving over the DFT-based diagnostics on which the models were trained. These ML models thus provide a promising approach to improve upon DFT-based diagnostic accuracy while remaining suitably low cost for high-throughput screening.

MALDIViz: A Comprehensive Informatics Tool for MALDI-MS Data Visualization and Analysis

SLAS DISCOVERY Advancing Life Sciences ◽

10.1177/2472555217727517 ◽

2017 ◽

Vol 22 (10) ◽

pp. 1246-1252 ◽

Cited By ~ 1

Author(s):

Kishore Kumar Jagadeesan ◽

Simon Ekström

Keyword(s):

Mass Spectrometry ◽

High Throughput ◽

Web Application ◽

Low Cost ◽

Maldi Ms ◽

Ionization Mass ◽

Label Free ◽

Label Free Detection ◽

R Shiny

Recently, mass spectrometry (MS) has emerged as an important tool for high-throughput screening (HTS) providing a direct and label-free detection method, complementing traditional fluorescent and colorimetric methodologies. Among the various MS techniques used for HTS, matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) provides many of the characteristics required for high-throughput analyses, such as low cost, speed, and automation. However, visualization and analysis of the large datasets generated by HTS MALDI-MS can pose significant challenges, especially for multiparametric experiments. The datasets can be generated fast, and the complexity of the experimental data (e.g., screening many different sorbent phases, the sorbent mass, and the load, wash, and elution conditions) makes manual data analysis difficult. To address these challenges, a comprehensive informatics tool called MALDIViz was developed. This tool is an R-Shiny-based web application, accessible independently of the operating system and without the need to install any program locally. It has been designed to facilitate easy analysis and visualization of MALDI-MS datasets, comparison of multiplex experiments, and export of the analysis results to high-quality images.

ChemSpaX: Exploration of Chemical Space by Automated Functionalization of Molecular Scaffold

10.26434/chemrxiv.14617320 ◽

2021 ◽

Author(s):

Adarsh Kalikadien ◽

Evgeny A. Pidko ◽

Vivek Sinha

Keyword(s):

Force Field ◽

High Throughput ◽

Chemical Space ◽

Space Exploration ◽

Molecular Structures ◽

Data Driven ◽

Pincer Complexes ◽

Cobalt Porphyrin ◽

Input Structure

<div>Local chemical space exploration of an experimentally synthesized material can be done by making slight structural</div><div>variations of the synthesized material. This generation of many molecular structures with reasonable quality,</div><div>that resemble an existing (chemical) purposeful material, is needed for high-throughput screening purposes in</div><div>material design. Large databases of geometry and chemical properties of transition metal complexes are not</div><div>readily available, although these complexes are widely used in homogeneous catalysis. A Python-based workflow,</div><div>ChemSpaX, that is aimed at automating local chemical space exploration for any type of molecule, is introduced.</div><div>The overall computational workflow of ChemSpaX is explained in more detail. ChemSpaX uses 3D information,</div><div>to place functional groups on an input structure. For example, the input structure can be a catalyst for which one</div><div>wants to use high-throughput screening to investigate if the catalytic activity can be improved. The newly placed</div><div>substituents are optimized using a computationally cheap force-field optimization method. After placement of</div><div>new substituents, higher level optimizations using xTB or DFT instead of force-field optimization are also possible</div><div>in the current workflow. In representative applications of ChemSpaX, it is shown that the structures generated by</div><div>ChemSpaX have a reasonable quality for usage in high-throughput screening applications. Representative applications</div><div>of ChemSpaX are shown by investigating various adducts on functionalized Mn-based pincer complexes,</div><div>hydrogenation of Ru-based pincer complexes, functionalization of cobalt porphyrin complexes and functionalization</div><div>of a bipyridyl functionalized cobalt-porphyrin trapped in a M2L4 type cage complex. Descriptors such as</div><div>the Gibbs free energy of reaction and HOMO-LUMO gap, that can be used in data-driven design and discovery</div><div>of catalysts, were selected and studied in more detail for the selected use cases. The relatively fast GFN2-xTB</div><div>method was used to calculate these descriptors and a comparison was done against DFT calculated descriptors.</div><div>ChemSpaX is open-source and aims to bolster the efforts of the scientific community towards data-driven material</div><div>discovery.</div>

Computational Method for Simulating Thermoset Polymer Curing and Prediction of Thermophysical Properties

10.26434/chemrxiv.12453989.v1 ◽

2020 ◽

Author(s):

Jeffrey Sanders ◽

Carla E. Estridge ◽

Matthew B Jackson ◽

Thomas JL Mustard ◽

Samuel J. Tucker ◽

...

Keyword(s):

High Throughput ◽

Thermophysical Properties ◽

Simulation Analysis ◽

Low Cost ◽

Polymer Network ◽

Thermomechanical Properties ◽

Computational Method ◽

Cross Linking ◽

Automated Simulation

Thermoset polymers are an area of intense research due to their low cost, ease of processing, environmental resistance, and unique physical properties. The favorable properties of this class of polymers have many applications in aerospace, automotive, marine, and sports equipment industries. Molecular simulations of thermosets are frequently used to model formation of the polymer network, and to predict the thermomechanical properties. These simulations usually require custom algorithms that are not easily accessible to non-experts and not suited for high throughput screening. To address these issues, we have developed a robust cross-linking algorithm that can incorporate different types of chemistries and leverage GPU-enabled molecular dynamics simulations. Automated simulation analysis tools for cross-linking simulations are also presented. Using four well known epoxy/amine formulations as a foundational case study and benzoxazine as an example of how additional chemistries can be modeled, we demonstrate the power of the algorithm to accurately predict curing and thermophysical properties. These tools are able to streamline the thermoset simulation process, opening up avenues to in-silico high throughput screening for advanced material development.