PubChemQC Project: A Large-Scale First-Principles Electronic Structure Database for Data-Driven Chemistry

2017 ◽  
Vol 57 (6) ◽  
pp. 1300-1308 ◽  
Author(s):  
Maho Nakata ◽  
Tomomi Shimazaki
2000 ◽  
Vol 64 (2) ◽  
pp. 311-317 ◽  
Author(s):  
M. C. Warren ◽  
M. T. Dove ◽  
S. A. T. Redfern

AbstractAt high temperature, MgAl2O4 spinel is stabilized by disorder of Mg and Al between octahedral and tetrahedral sites. This behaviour has been measured up to 1700 K in recent neutron experiments, but the extrapolation of subsequently fitted thermodynamic models is not reliable. First principles simulation of the electronic structure of such minerals can in principle accurately predict disorder, but would require unfeasibly large computing resources. We have instead parameterized on-site and short-ranged cluster potentials using a small number of electronic structure simulations at zero temperature. These potentials were then used in large-scale statistical simulations at finite temperatures to predict disordering thermodynamics beyond the range of experimental measurements. Within the temperature range of the experiment, good agreement is obtained for the degree of order. The entropy and free energy are calculated and compared to those from macroscopic models.


2014 ◽  
Vol 52 (12) ◽  
pp. 1025-1029
Author(s):  
Min-Wook Oh ◽  
Tae-Gu Kang ◽  
Byungki Ryu ◽  
Ji Eun Lee ◽  
Sung-Jae Joo ◽  
...  

2019 ◽  
Author(s):  
Mohammad Atif Faiz Afzal ◽  
Mojtaba Haghighatlari ◽  
Sai Prasad Ganesh ◽  
Chong Cheng ◽  
Johannes Hachmann

<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>


2012 ◽  
Vol 54 ◽  
pp. 287-292 ◽  
Author(s):  
Xiao-Jun Chen ◽  
Meng-Xue Zeng ◽  
Ren-Nian Wang ◽  
Zhou-Sheng Mo ◽  
Bi-Yu Tang ◽  
...  

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