This repository contains an artificial dataset constructed for the study of uncertainty characterization and quantification in chemical ML applications. The data are designed to be noise-free and represent group-additivity calculations of enthalpy of formation, rather than calculated or measured enthalpy of formation directly. Where did the targets come from: These data files contain SMILES and targets for a simple group additivity calculation of enthalpy of formation at 298 K. The group additivity coefficients were fitted to the molecules of the qm9 computational chemistry database. Fragments were only considered that appeared in at least 100 molecules. These coefficients were rounded to 3 decimals. Groups only consider a bond radius of 1...
We present a set of group contribution models for predicting heat of formation of organic compounds....
An automated computational thermochemistry protocol based on explicitly correlated coupled-cluster t...
Group contribution (GC) methods to predict thermochemical properties are eminently important to proc...
This repository contains an artificial dataset constructed for the study of uncertainty characteriza...
The thermodynamic properties of a substance are key to predicting its behavior in physical and chemi...
We developed a database of 2869 experimental values of enthalpy of formation and 1403 values for ent...
In order to test new procedures for the calculation of basic molecular properties, a properly valida...
Experimental formation enthalpies for inorganic compounds, collected from years of calorimetric expe...
We present the full database of the article "Supervised Machine Learning Model to Predict Standard V...
Machine learning provides promising new methods for accurate yet rapid prediction of molecular prope...
Accurate thermochemistry estimation of polycyclic molecules is crucial for kinetic modeling of chemi...
A methodology for predicting the standard enthalpy of formation of gas-phase molecules with high spe...
Thermodynamic data are key in the understanding and design of chemical processes. Next to the experi...
This study extends a previous publication on group additivity values (GAVs) for the elements C, H, a...
A prerequisite for the generation of detailed fundamental kinetic models is the availability of accu...
We present a set of group contribution models for predicting heat of formation of organic compounds....
An automated computational thermochemistry protocol based on explicitly correlated coupled-cluster t...
Group contribution (GC) methods to predict thermochemical properties are eminently important to proc...
This repository contains an artificial dataset constructed for the study of uncertainty characteriza...
The thermodynamic properties of a substance are key to predicting its behavior in physical and chemi...
We developed a database of 2869 experimental values of enthalpy of formation and 1403 values for ent...
In order to test new procedures for the calculation of basic molecular properties, a properly valida...
Experimental formation enthalpies for inorganic compounds, collected from years of calorimetric expe...
We present the full database of the article "Supervised Machine Learning Model to Predict Standard V...
Machine learning provides promising new methods for accurate yet rapid prediction of molecular prope...
Accurate thermochemistry estimation of polycyclic molecules is crucial for kinetic modeling of chemi...
A methodology for predicting the standard enthalpy of formation of gas-phase molecules with high spe...
Thermodynamic data are key in the understanding and design of chemical processes. Next to the experi...
This study extends a previous publication on group additivity values (GAVs) for the elements C, H, a...
A prerequisite for the generation of detailed fundamental kinetic models is the availability of accu...
We present a set of group contribution models for predicting heat of formation of organic compounds....
An automated computational thermochemistry protocol based on explicitly correlated coupled-cluster t...
Group contribution (GC) methods to predict thermochemical properties are eminently important to proc...