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mathematical sciences, Computational Sciences & Mathematics

PNNL’s Computing Research portfolio—spanning from basic to applied—includes data and computational engineering, high-performance computing, applied mathematics, semantic and human language technologies, machine learning, data and computing architectures, systems integration, and software and application development. At PNNL, scientists, engineers, programmers, and researchers work together to apply advanced theories, methods, algorithms, models, evaluation tools and testbeds, and computational-based solutions to address complex scientific challenges affecting energy, biological sciences, the environment, and national security.


Harnessing New Computing Tools to Solve Big Physics Problems

Pacific Northwest National Laboratory (PNNL) has been tapped to lead a $10 million, four-year effort to uncover hidden physics using new developments in deep learning, a computing technique that harnesses the power of machine learning and big data.

Gao Self Assembling

Programmable Self-Assembled Nanostructures

A research team including postdoctoral scientist Peiyuan Gao has developed a highly tunable version of self-assembled structures by adjusting external stimuli to manipulate the shape and size of a polymer cluster called a micelle, a structure formed in nature.

Jiajia Li

PNNL Computer Scientist Jiajia Li Receives the "Best Student Paper" Award

Jiajia Li developed a way to speed computing and minimize storage requirements in a real-world multicore-parallel CPU platform. Li's work was recognized at Supercomputing 2018, the largest annual international conference for high-performance computing.

Kenneth Roche

2019 INCITE award fosters search for new elements, quantum interactions

Kenneth Roche is part of a team awarded a 2019 INCITE award. Roche, a member of PNNL’s High-Performance Computing Group and a lead in the DOE Exascale Computing Project, will work on a team of eight scientists led by Aurel Bulgac of the University of Washington.


Automating Expertise

A lack of labeled data is a bottleneck for deep learning that impacts many domains. To remedy this, PNNL scientists developed ChemNet, which learns expert knowledge from large unlabeled databases and outperforms current deep learning methods. ChemNet also has potential applications in other research areas with similar data challenges. This month, the team will showcase ChemNet during the 24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining in London.

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Computing Research

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