Achieving the 2016 Paris agreement goal of limiting global warming below 2° C and
securing a sustainable energy future require materials innovations in renewable energy …

Chemical engineers rely on models for design, research, and daily decision-making, often
with potentially large financial and safety implications. Previous efforts a few decades ago to …

In chemical kinetics research, kinetic models containing hundreds of species and tens of
thousands of elementary reactions are commonly used to understand and predict the …

We present a group contribution method (SoluteGC) and a machine learning model
(SoluteML) to predict the Abraham solute parameters, as well as a machine learning model …

The Reaction Mechanism Generator (RMG) database for chemical property prediction is
presented. The RMG database consists of curated datasets and estimators for accurately …

Machine learning provides promising new methods for accurate yet rapid prediction of
molecular properties, including thermochemistry, which is an integral component of many …

Because collecting precise and accurate chemistry data is often challenging, chemistry data
sets usually only span a small region of chemical space, which limits the performance and …

Accurate quantitative chemical kinetic models are useful in many applications, ranging from
design of chemical processes to building a consensus for international treaties (eg the …

In spite of increasing importance of cyclic hydrocarbons in various chemical systems, studies
on the fundamental properties of these compounds, such as enthalpy of formation, are still …

With the rise in production of natural gas, there is increased interest in homogeneous partial
oxidation (POX) to convert methane to syngas (CO+ H2), ethene (C2H4) and acetylene …