Accelerated Discovery of High-Refractive-Index Polyimides via First-Principles Molecular Modeling, Virtual High-Throughput Screening, and Data Mining

2019 ◽  
Vol 123 (23) ◽  
pp. 14610-14618 ◽  
Author(s):  
Mohammad Atif Faiz Afzal ◽  
Mojtaba Haghighatlari ◽  
Sai Prasad Ganesh ◽  
Chong Cheng ◽  
Johannes Hachmann
Keyword(s):  
Data Mining ◽  
Refractive Index ◽  
Molecular Modeling ◽  
High Throughput ◽  
First Principles ◽  
High Throughput Screening ◽  
High Refractive Index

Accelerated Discovery of High-Refractive-Index Polyimides via First-Principles Molecular Modeling, Virtual High-Throughput Screening, and Data Mining

2019 ◽  
Author(s):  
Mohammad Atif Faiz Afzal ◽  
Mojtaba Haghighatlari ◽  
Sai Prasad Ganesh ◽  
Chong Cheng ◽  
Johannes Hachmann
Keyword(s):  
Data Mining ◽  
Refractive Index ◽  
High Throughput ◽  
First Principles ◽  
High Throughput Screening ◽  
Large Scale ◽  
Computational Study ◽  
High Refractive Index ◽  
Structural Features ◽  
Learning Program

<div>We present a high-throughput computational study to identify novel polyimides (PIs) with exceptional refractive index (RI) values for use as optic or optoelectronic materials. Our study utilizes an RI prediction protocol based on a combination of first-principles and data modeling developed in previous work, which we employ on a large-scale PI candidate library generated with the ChemLG code. We deploy the virtual screening software ChemHTPS to automate the assessment of this extensive pool of PI structures in order to determine the performance potential of each candidate. This rapid and efficient approach yields a number of highly promising leads compounds. Using the data mining and machine learning program package ChemML, we analyze the top candidates with respect to prevalent structural features and feature combinations that distinguish them from less promising ones. In particular, we explore the utility of various strategies that introduce highly polarizable moieties into the PI backbone to increase its RI yield. The derived insights provide a foundation for rational and targeted design that goes beyond traditional trial-and-error searches.</div>

Accelerated Discovery of High-Refractive-Index Polyimides via First-Principles Molecular Modeling, Virtual High-Throughput Screening, and Data Mining

2019 ◽  
Author(s):  
Mohammad Atif Faiz Afzal ◽  
Mojtaba Haghighatlari ◽  
Sai Prasad Ganesh ◽  
Chong Cheng ◽  
Johannes Hachmann
Keyword(s):  
Data Mining ◽  
Refractive Index ◽  
High Throughput ◽  
First Principles ◽  
High Throughput Screening ◽  
Large Scale ◽  
Computational Study ◽  
High Refractive Index ◽  
Structural Features ◽  
Learning Program

<div>We present a high-throughput computational study to identify novel polyimides (PIs) with exceptional refractive index (RI) values for use as optic or optoelectronic materials. Our study utilizes an RI prediction protocol based on a combination of first-principles and data modeling developed in previous work, which we employ on a large-scale PI candidate library generated with the ChemLG code. We deploy the virtual screening software ChemHTPS to automate the assessment of this extensive pool of PI structures in order to determine the performance potential of each candidate. This rapid and efficient approach yields a number of highly promising leads compounds. Using the data mining and machine learning program package ChemML, we analyze the top candidates with respect to prevalent structural features and feature combinations that distinguish them from less promising ones. In particular, we explore the utility of various strategies that introduce highly polarizable moieties into the PI backbone to increase its RI yield. The derived insights provide a foundation for rational and targeted design that goes beyond traditional trial-and-error searches.</div>

scholarly journals A deep neural network model for packing density predictions and its application in the study of 1.5 million organic molecules

2019 ◽  
Vol 10 (36) ◽  
pp. 8374-8383 ◽  
Author(s):  
Mohammad Atif Faiz Afzal ◽  
Aditya Sonpal ◽  
Mojtaba Haghighatlari ◽  
Andrew J. Schultz ◽  
Johannes Hachmann
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Refractive Index ◽  
High Throughput ◽  
Neural Network Model ◽  
High Throughput Screening ◽  
Deep Neural Network ◽  
Organic Molecules ◽  
High Refractive Index ◽  
Computational Pipeline

Computational pipeline for the accelerated discovery of organic materials with high refractive index via high-throughput screening and machine learning.

scholarly journals Searching for materials with high refractive index and wide band gap: A first-principles high-throughput study

2019 ◽  
Vol 3 (4) ◽  
Author(s):  
Francesco Naccarato ◽  
Francesco Ricci ◽  
Jin Suntivich ◽  
Geoffroy Hautier ◽  
Ludger Wirtz ◽  
...  
Keyword(s):  
Refractive Index ◽  
Band Gap ◽  
High Throughput ◽  
First Principles ◽  
Wide Band ◽  
High Refractive Index ◽  
Wide Band Gap ◽  
High Throughput Study

Efficient Modulation of Catalytic Performance of Electrocatalytic Nitrogen Reduction with Transition Metal Anchored N/O-codoped Graphene by Coordination Engineering

2021 ◽  
Author(s):  
Xiaolin Wang ◽  
Li-Ming Yang
Keyword(s):  
Nitrogen Fixation ◽  
Transition Metal ◽  
High Throughput ◽  
First Principles ◽  
High Throughput Screening ◽  
Catalytic Performance ◽  
Nitrogen Reduction ◽  
Highly Efficient ◽  
First Time

We for the first time report the discovery of a series of highly efficient electrocatalysts, i.e., transition metal anchored N/O-codoped graphene, for nitrogen fixation via high-throughput screening combined with first-principles...

Deriving Knowledge through Data Mining High-Throughput Screening Data

2004 ◽  
Vol 47 (25) ◽  
pp. 6373-6383 ◽  
Author(s):  
David J. Diller ◽  
Doug W. Hobbs
Keyword(s):  
Data Mining ◽  
High Throughput ◽  
High Throughput Screening

scholarly journals Correction: Computational and experimental investigation of TmAgTe2 and XYZ2 compounds, a new group of thermoelectric materials identified by first-principles high-throughput screening

2016 ◽  
Vol 4 (19) ◽  
pp. 4331-4331 ◽  
Author(s):  
Hong Zhu ◽  
Geoffroy Hautier ◽  
Umut Aydemir ◽  
Zachary M. Gibbs ◽  
Guodong Li ◽  
...  
Keyword(s):  
Experimental Investigation ◽  
High Throughput ◽  
Thermoelectric Materials ◽  
First Principles ◽  
High Throughput Screening

Correction for ‘Computational and experimental investigation of TmAgTe2 and XYZ2 compounds, a new group of thermoelectric materials identified by first-principles high-throughput screening’ by Hong Zhu et al., J. Mater. Chem. C, 2015, 3, 10554–10565.

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