Hydrogen energy-based electrochemical energy conversion technologies offer the promise
of enabling a transition of the global energy landscape from fossil fuels to renewable energy …

Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …

The unprecedented ability of computations to probe atomic-level details of catalytic systems
holds immense promise for the fundamentals-based bottom-up design of novel …

This “white paper” is a concise perspective of the potential of machine learning in the
process systems engineering (PSE) domain, based on a session during FIPSE 5, held in …

Electrochemical carbon dioxide reduction is a potential pathway for sustainable production
of fuels and chemicals. However, the detailed catalytic mechanism in cells using high …

In silico catalyst design is a grand challenge of chemistry. Traditional computational
approaches have been limited by the need to compute properties for an intractably large …

Chemical kinetic modeling in heterogeneous catalysis is advancing in its ability to provide
qualitatively or even quantitatively accurate prediction of real-world behavior because of …

Ethylene oxidation by Ag catalysts has been extensively investigated over the past few
decades, but many key fundamental issues about this important catalytic system are still …

Conspectus Microkinetic modeling based on density functional theory (DFT) derived
energetics is important for addressing fundamental questions in catalysis. The quantitative …

The computational design of catalytic materials is a high dimensional structure optimization
problem that is limited by the bottleneck of expensive quantum computation tools. Current …