Sustainable energy generation calls for a shift away from centralized, high-temperature,
energy-intensive processes to decentralized, low-temperature conversions that can be …

Big data and artificial intelligence has revolutionized science in almost every field–from
economics to physics. In the area of materials science and computational heterogeneous …

The development of machine learning models for electrocatalysts requires a broad set of
training data to enable their use across a wide variety of materials. One class of materials …

Catalyst discovery and optimization is key to solving many societal and energy challenges
including solar fuel synthesis, long-term energy storage, and renewable fertilizer production …

The discovery and development of catalysts and catalytic processes are essential
components to maintaining an ecological balance in the future. Recent revolutions made in …

Designing materials for catalysis is challenging because the performance is governed by an
intricate interplay of various multiscale phenomena, such as the chemical reactions on …

Machine learning (ML) integrated density functional theory (DFT) calculations have recently
been used to accelerate the design and discovery of heterogeneous catalysts such as single …

The presence of a dataset that covers a parametric space of materials and process
conditions in a process-consistent manner is essential for the realization of catalyst …

Designing catalysts is a challenging matter as catalysts are involved with various factors that
impact synthesis, catalysts, reactor and reaction. In order to overcome these difficulties …

Artificial intelligence (AI) can accelerate catalyst design by identifying key physicochemical
descriptive parameters correlated with the underlying processes triggering, favoring, or …