Minimally Empirical Double Hybrid Functionals Trained Against the GMTKN55 Database: revDSD-PBEP86-D4, revDOD-PBE-D4, and DOD-SCAN-D4

2019 ◽  
Author(s):  
Golokesh Santra ◽  
Nitai Sylvetsky ◽  
Gershom Martin
Keyword(s):  
Substantial Improvement ◽  
Viable Alternative ◽  
Mean Absolute Deviation ◽  
Dispersion Correction ◽  
Training Set ◽  
Weighted Mean ◽  
Absolute Deviation ◽  
Hybrid Functionals ◽  
Scaling Algorithms

We present a family of minimally empirical double-hybrid DFT functionals parametrized against the very large and diverse GMTKN55 benchmark. The very recently proposed wB97M(2) empirical double hybrid (with 16 empirical parameters) has the lowest WTMAD2 (weighted mean absolute deviation over GMTKN55) ever reported at 2.19 kcal/mol. However, our xrevDSD-PBEP86-D4 functional reaches a statistically equivalent WTMAD2=2.22 kcal/mol, using just a handful of empirical parameters, and the xrevDOD-PBEP86-D4 functional reaches 2.25 kcal/mol with just opposite-spin MP2 correlation, making it amenable to reduced-scaling algorithms. In general, the D4 empirical dispersion correction is clearly superior to D3BJ. If one eschews dispersion corrections of any kind, noDispSD-SCAN offers a viable alternative. Parametrization over the entire GMTKN55 dataset yields substantial improvement over the small training set previously employed in the DSD papers.

Minimally Empirical Double Hybrid Functionals Trained Against the GMTKN55 Database: revDSD-PBEP86-D4, revDOD-PBE-D4, and DOD-SCAN-D4

2019 ◽  
Author(s):  
Golokesh Santra ◽  
Nitai Sylvetsky ◽  
Gershom Martin
Keyword(s):  
Substantial Improvement ◽  
Viable Alternative ◽  
Mean Absolute Deviation ◽  
Dispersion Correction ◽  
Training Set ◽  
Weighted Mean ◽  
Absolute Deviation ◽  
Hybrid Functionals ◽  
Scaling Algorithms

We present a family of minimally empirical double-hybrid DFT functionals parametrized against the very large and diverse GMTKN55 benchmark. The very recently proposed wB97M(2) empirical double hybrid (with 16 empirical parameters) has the lowest WTMAD2 (weighted mean absolute deviation over GMTKN55) ever reported at 2.19 kcal/mol. However, our xrevDSD-PBEP86-D4 functional reaches a statistically equivalent WTMAD2=2.22 kcal/mol, using just a handful of empirical parameters, and the xrevDOD-PBEP86-D4 functional reaches 2.25 kcal/mol with just opposite-spin MP2 correlation, making it amenable to reduced-scaling algorithms. In general, the D4 empirical dispersion correction is clearly superior to D3BJ. If one eschews dispersion corrections of any kind, noDispSD-SCAN offers a viable alternative. Parametrization over the entire GMTKN55 dataset yields substantial improvement over the small training set previously employed in the DSD papers.

Minimally Empirical Double Hybrid Functionals Trained Against the GMTKN55 Database: revDSD-PBEP86-D4, revDOD-PBE-D4, and DOD-SCAN-D4

2019 ◽  
Author(s):  
Golokesh Santra ◽  
Nitai Sylvetsky ◽  
Gershom Martin
Keyword(s):  
Substantial Improvement ◽  
Viable Alternative ◽  
Mean Absolute Deviation ◽  
Dispersion Correction ◽  
Training Set ◽  
Weighted Mean ◽  
Absolute Deviation ◽  
Hybrid Functionals ◽  
Scaling Algorithms

We present a family of minimally empirical double-hybrid DFT functionals parametrized against the very large and diverse GMTKN55 benchmark. The very recently proposed wB97M(2) empirical double hybrid (with 16 empirical parameters) has the lowest WTMAD2 (weighted mean absolute deviation over GMTKN55) ever reported at 2.19 kcal/mol. However, our xrevDSD-PBEP86-D4 functional reaches a statistically equivalent WTMAD2=2.22 kcal/mol, using just a handful of empirical parameters, and the xrevDOD-PBEP86-D4 functional reaches 2.25 kcal/mol with just opposite-spin MP2 correlation, making it amenable to reduced-scaling algorithms. In general, the D4 empirical dispersion correction is clearly superior to D3BJ. If one eschews dispersion corrections of any kind, noDispSD-SCAN offers a viable alternative. Parametrization over the entire GMTKN55 dataset yields substantial improvement over the small training set previously employed in the DSD papers.

scholarly journals Minimally Empirical Double Hybrid Functionals Trained Against the GMTKN55 Database: revDSD-PBEP86-D4, revDOD-PBE-D4, and DOD-SCAN-D4

2019 ◽  
Author(s):  
Golokesh Santra ◽  
Nitai Sylvetsky ◽  
Gershom Martin
Keyword(s):  
Substantial Improvement ◽  
Viable Alternative ◽  
Mean Absolute Deviation ◽  
Dispersion Correction ◽  
Training Set ◽  
Weighted Mean ◽  
Absolute Deviation ◽  
Hybrid Functionals ◽  
Scaling Algorithms

We present a family of minimally empirical double-hybrid DFT functionals parametrized against the very large and diverse GMTKN55 benchmark. The very recently proposed wB97M(2) empirical double hybrid (with 16 empirical parameters) has the lowest WTMAD2 (weighted mean absolute deviation over GMTKN55) ever reported at 2.19 kcal/mol. However, our xrevDSD-PBEP86-D4 functional reaches a statistically equivalent WTMAD2=2.22 kcal/mol, using just a handful of empirical parameters, and the xrevDOD-PBEP86-D4 functional reaches 2.25 kcal/mol with just opposite-spin MP2 correlation, making it amenable to reduced-scaling algorithms. In general, the D4 empirical dispersion correction is clearly superior to D3BJ. If one eschews dispersion corrections of any kind, noDispSD-SCAN offers a viable alternative. Parametrization over the entire GMTKN55 dataset yields substantial improvement over the small training set previously employed in the DSD papers.

scholarly journals Minimally Empirical Double Hybrid Functionals Trained Against the GMTKN55 Database: revDSD-PBEP86-D4, revDOD-PBE-D4, and DOD-SCAN-D4

2019 ◽  
Author(s):  
Golokesh Santra ◽  
Nitai Sylvetsky ◽  
Gershom Martin
Keyword(s):  
Substantial Improvement ◽  
Viable Alternative ◽  
Mean Absolute Deviation ◽  
Dispersion Correction ◽  
Training Set ◽  
Weighted Mean ◽  
Absolute Deviation ◽  
Hybrid Functionals ◽  
Scaling Algorithms

We present a family of minimally empirical double-hybrid DFT functionals parametrized against the very large and diverse GMTKN55 benchmark. The very recently proposed wB97M(2) empirical double hybrid (with 16 empirical parameters) has the lowest WTMAD2 (weighted mean absolute deviation over GMTKN55) ever reported at 2.19 kcal/mol. However, our xrevDSD-PBEP86-D4 functional reaches a statistically equivalent WTMAD2=2.22 kcal/mol, using just a handful of empirical parameters, and the xrevDOD-PBEP86-D4 functional reaches 2.25 kcal/mol with just opposite-spin MP2 correlation, making it amenable to reduced-scaling algorithms. In general, the D4 empirical dispersion correction is clearly superior to D3BJ. If one eschews dispersion corrections of any kind, noDispSD-SCAN offers a viable alternative. Parametrization over the entire GMTKN55 dataset yields substantial improvement over the small training set previously employed in the DSD papers.

B3LYP-SVM METHOD FOR THE ESTIMATION OF MOLECULAR ENTHALPIES OF FORMATION

2007 ◽  
Vol 06 (03) ◽  
pp. 495-509 ◽  
Author(s):  
GUI-KAI YAN ◽  
JUN-JIE LI ◽  
BING-RUI LI ◽  
JIA HU ◽  
WEN-PING GUO
Keyword(s):  
Support Vector Machine ◽  
Correlation Coefficient ◽  
Enthalpies Of Formation ◽  
Support Vector ◽  
Maximum Deviation ◽  
Mean Absolute Deviation ◽  
Training Set ◽  
Absolute Deviation ◽  
Test Set

Support vector machine (SVM) is used to predict the enthalpies of formation at 298 K [Formula: see text] for 261 molecules based on B3LYP/6-311g (3df,2p) results. With data randomly separated into two parts: 195 for training set and 66 for test set, the resulting mean absolute deviation (MAD) and maximum deviation (MD) for training set are 1.51 kcal/mol and 9.23 kcal/mol (correlation coefficient R = 0.9995), and for test set they become to 1.78 kcal/mol and 7.31 kcal/mol (R = 0.9990). The result is improved according to G2 method.

scholarly journals Using Benford’s law to assess the quality of COVID-19 register data in Brazil

2020 ◽  
Author(s):  
Lucas Silva ◽  
Dalson Figueiredo Filho
Keyword(s):  
Goodness Of Fit ◽  
Epidemiological Surveillance ◽  
Evidence Based ◽  
Mean Absolute Deviation ◽  
Chi Square ◽  
Absolute Deviation ◽  
Distortion Factor ◽  
Benford Law ◽  
Official Data

Abstract We employ Newcomb–Benford law (NBL) to evaluate the reliability of COVID-19 figures in Brazil. Using official data from February 25 to September 15, we apply a first digit test for a national aggregate dataset of total cases and cumulative deaths. We find strong evidence that Brazilian reports do not conform to the NBL theoretical expectations. These results are robust to different goodness of fit (chi-square, mean absolute deviation and distortion factor) and data sources (John Hopkins University and Our World in Data). Despite the growing appreciation for evidence-based-policymaking, which requires valid and reliable data, we show that the Brazilian epidemiological surveillance system fails to provide trustful data under the NBL assumption on the COVID-19 epidemic.

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