Machine-learning atomic simulation for heterogeneous catalysis

Atomic Energy Models for Machine Learning: Atomic Restricted Boltzmann Machines

Learning Chemical Trends in Heterogeneous Catalysis

Despite tremendous advances in the understanding of catalysis in the past few decades, the discovery of improved materials for energy transformation processes has been guided by physical intuition and trial-and-error approaches. Even though computational simulations have provided a more rational approach, even the most systematic studies involving the most well-described systems require a large...

Simulation assisted machine learning

Predicting how a proposed cancer treatment will affect a given tumor can be cast as a machine learning problem, but the complexity of biological systems, the number of potentially relevant genomic and clinical features, and the lack of very large scale patient data repositories make this a unique challenge. “Pure data” approaches to this problem are underpowered to detect combinatorially comple...

Heterogeneous Catalysis for Environmental Remediation

The intensive human activities in chemical industry and environmental purification urge the development of advanced protocols for green production and waste management. In environmental science, developing highly efficient and environmentally-friendly catalytic materials and systems are very favourable approaches to green chemical synthesis and remediation of contaminated air, soil, and wastewa...

Heterogeneous Catalysis for Tandem Reactions

As long as the molecular complexity increases, the number of chemical transformations will increase in parallel, becoming a highly demanding synthetic process in terms of time and resources. Since the loss of material after each purification step will also dramatically reduce the overall efficiency of a synthetic process, the conversion of such transformation into a practical and efficient proc...