The formation of amide bonds is estimated to be the most common bond forming reaction that occurs in biological systems and pharmaceutical production. Many synthetic polymers such as nylon and Kevlar contain amide bonds which are crucial to their structural strength and stability…
Iridium oxides have been considered as a benchmark catalyst exhibiting high activity, albeit a modest stability, for the oxygen evolution reaction (OER) in acidic media…
The ability of machine learning models to learn the underlying physics is critical for the transferability of models to different thermodynamic states (e.g., temperatures, phases, interfaces, etc.)…
The formation of amide bonds is estimated to be the most common bond forming reaction that occurs in biological systems and pharmaceutical production. Many synthetic polymers such as nylon and Kevlar contain amide bonds which are crucial to their structural strength and stability…
Iridium oxides have been considered as a benchmark catalyst exhibiting high activity, albeit a modest stability, for the oxygen evolution reaction (OER) in acidic media…
The formation of amide bonds is estimated to be the most common bond forming reaction that occurs in biological systems and pharmaceutical production. Many synthetic polymers such as nylon and Kevlar contain amide bonds which are crucial to their structural strength and stability…
Iridium oxides have been considered as a benchmark catalyst exhibiting high activity, albeit a modest stability, for the oxygen evolution reaction (OER) in acidic media…
The formation of amide bonds is estimated to be the most common bond forming reaction that occurs in biological systems and pharmaceutical production. Many synthetic polymers such as nylon and Kevlar contain amide bonds which are crucial to their structural strength and stability…
Iridium oxides have been considered as a benchmark catalyst exhibiting high activity, albeit a modest stability, for the oxygen evolution reaction (OER) in acidic media…
The ability of machine learning models to learn the underlying physics is critical for the transferability of models to different thermodynamic states (e.g., temperatures, phases, interfaces, etc.)…
The formation of amide bonds is estimated to be the most common bond forming reaction that occurs in biological systems and pharmaceutical production. Many synthetic polymers such as nylon and Kevlar contain amide bonds which are crucial to their structural strength and stability…
Iridium oxides have been considered as a benchmark catalyst exhibiting high activity, albeit a modest stability, for the oxygen evolution reaction (OER) in acidic media…
The ability of machine learning models to learn the underlying physics is critical for the transferability of models to different thermodynamic states (e.g., temperatures, phases, interfaces, etc.)…
The formation of amide bonds is estimated to be the most common bond forming reaction that occurs in biological systems and pharmaceutical production. Many synthetic polymers such as nylon and Kevlar contain amide bonds which are crucial to their structural strength and stability…
Iridium oxides have been considered as a benchmark catalyst exhibiting high activity, albeit a modest stability, for the oxygen evolution reaction (OER) in acidic media…
The ability of machine learning models to learn the underlying physics is critical for the transferability of models to different thermodynamic states (e.g., temperatures, phases, interfaces, etc.)…
Training machine learning models efficiently is critical to develop computationally affordable and accurate models to push the frontiers of computational chemistry…
Bimetallic solid catalysts catalyze selective oxidative alcohol-alcohol and alcohol-amine coupling; in particular, those with group 10 and 11 transition metals (M; e.g., silver or palladium) as minor components in gold or copper hosts. A series of bimetallic M-Au and M-Cu catalysts (i.e…
Nanometer-sized metal nanoparticles (NPs) play an important role in catalysis, but common computational models are often limited to single-facet surfaces due to computational cost. More realistic computational models are required to make meaningful comparisons with experimental studies (e.g…
As machine learning interatomic potentials (MLIP) enable the calculations of systems larger than typical quantum chemical investigations, new methods are required to properly utilize MLIPs in applications…