At a conference featuring the most advanced computing hardware and software, ML in its various guises was on full display and highlighted by Nathan Baker’s featured invited presentation.
PNNL and Argonne researchers developed and tested a chemical process that successfully captures radioactive byproducts from used nuclear fuel so they could be sent to advanced reactors for destruction while also producing electrical power.
In the third year of the DISCOVR Consortium project, the consortium team has identified an algal strain that progressed successfully through multiple evaluation phases.
In today’s digital age, the rabbit hole of connected information can be not only a time sink, but downright overwhelming. Even for high-performance computers.
Nitrogen oxides, also known as NOx, form when fossil fuels burn at high temperatures. When emitted from industrial sources such as coal power plants, these pollutants react with other compounds to produce harmful smog.
Eric Hoppe, senior scientist, was selected a 2019 American Chemical Society (ACS) fellow. Eric is being recognized for his contributions to analytical chemistry measurements and three decades of volunteer service to the ACS community.
Researchers at PNNL have developed a model that predicts outcomes from the algae hydrothermal liquefaction process in a way that mirrors commercial reality much more closely than previous analyses.
Researchers at PNNL have introduced an alternative method using a molecular-based pump that could potentially use a quarter less energy than the age-old mechanical pump.
Researchers apply numerical simulations to understand more about a sturdy material and how its basic structure responds to and resists radiation. The outcomes could help guide development of the resilient materials of the future.
Researchers at PNNL and their collaborators have made a significant improvement to a catalyst that is more rugged and can reduce tailpipe pollution at lower temperatures than existing methods.
Scientists created a fast-track tutorial that equips a neural network to tackle drug discovery and other applications where there's a shortage of precisely labeled chemical data.