Lab Partnering Service Discovery
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Bert de Jong leads the Computational Chemistry, Materials, and Climate Group, which advances scientific computing by developing and enhancing applications in key disciplines, as well as developing tools and libraries for addressing general problems in computational science.
de Jong is the director of the LBNL Quantum Algorithms Team QAT4Chem, the team director of the Accelerated Research for Quantum Computing (ARQC) Team AIDE-QC, both funded by DOE ASCR, focused on developing software stacks, algorithms, and computer science and applied mathematics solutions for chemical sciences and other fields on near-term quantum computing devices. He is also a co-PI on the ARQC team FAR-QC (led out of Sandia). He is also part of LBNL’s quantum testbed, developing superconducting qubits. He is the LBNL lead for the Basic Energy Sciences Quantum Information Sciences project (led out of PNNL), where he is focusing on new approaches for encoding wave functions and embedding quantum systems. In addition, he is a co-PI on an LBNL led HEP funded quantum information science projects.
de Jong is a co-PI within the DOE ASCR Exascale Computing Project (ECP) as the LBNL lead for the NWChemEx effort, contributing to the development of an exascale computational chemistry code. He is the LBNL lead for the Basic Energy Sciences SPEC Computational Chemistry Center (led out of PNNL), where he is working on reduced scaling MCSCF and beyond GW approaches for molecules.
He leads an LBNL funded effort on machine learning for chemical sciences, focused on developing deep learning networks (GANs and autoencoders) for the prediction of structure-function relationships and its inverse, with a demonstrating in mass spectrometry. As part of this effort, his team developed the ML4Chem Python package.
Areas of expertise: software and algorithms for near-term quantum computing devices, machine learning, supercomputing, computational chemistry.
Lawrence Berkeley National Laboratory (Berkeley Lab), a U. S. Department of Energy Office of Science national lab managed by the University of California, delivers science solutions to the world â solutions derived from hundreds of patented and patent pending technologies plus scores of copyrighted software tools and published, peer-reviewed manuscripts.
Berkeley Lab has more than one hundred cutting-edge research projects using AI to find new scientific solutions to national problems. Through this effort, computer scientists, mathematicians, and domain scientists are collaborating to turn burgeoning datasets into scientific insights. Visit Berkeley Labâs Machine Learning for Science site for more information.
Berkeley Labâs advanced materials expertise is applied to innovation in batteries and other energy storage technologies, semiconductors, and photovoltaics. Additional energy-related areas of expertise include grid modernization and security, bio-based fuels and chemicals and building energy and demand response. Several National User Facilities are available for collaborative engagement: the Advanced Light Source, Molecular Foundry, National Energy Research Scientific Computing Center (NERSC), Energy Sciences Network, and the Joint Genome Institute. Other specialized facilities include FLEXLAB for building energy research and the Advanced Biofuels Process Demonstration Unit.
Ernest Orlando Lawrence, the lab's founder, believed team science yielded the greatest discoveries. That belief is reflected today in interdisciplinary teams and collaborative projects connecting Berkeley Lab, industry, and other research organizations. Berkeley Lab's Intellectual Property Office, connects industry partners with lab innovations and unique facilities to enable lab-to-market transition.
- Basic science: seeks to understand how nature works. This research includes experimental and theoretical work in materials science, physics, chemistry, biology, high-energy physics, and mathematics and computer science, including high performance computing.
- Applied science and engineering helps to find practical solutions to society’s problems. These programs focus primarily on energy resources, environmental management and national security.
Andy Mounce has research experience in condensed matter physics, semiconductor qubits, nitrogen vacancy magnetometry, and defects in wide band gap semiconductors. His expertise includes utilizing quantum information science techniques for understanding basic properties of quantum materials and quantum information relevant materials, such as superconductors, strongly correlated electronic materials, magnetic materials, and topological phases in materials. These techniques include cryogenic amplification, optically detected magnetic resonance, nitrogen vacancy detected magnetometry, photoluminescence, and bulk spin-resonance. Additionally, he is using machine learning in image analysis techniques, such as compressive sensing and neural networks, to both optimize experimental implementations and analysis.
Ojas Parekh is a Principal Member of Technical Staff in the Center for Computing Research at Sandia National Laboratories. Parekh is a theoretical computer scientist with a background in discrete optimization. More broadly, he has worked on combinatorial scientific computing, geospatial graph analysis, and neural-inspired computation, and he is currently leading several interdisciplinary and multi-institutional efforts that seek to understand novel ways in which quantum computation may offer resource advantages over classical computation, especially for optimization, simulation, and machine learning. Parekh received his PhD in Algorithms, Combinatorics, and Optimization from Carnegie Mellon University in 2002 and has been at Sandia since 2010.
Energy research represents a major focus for BNL over the next decade. We are using a multifaceted approach driven by the unique state-of-the art laboratory facilities and the inter-disciplinary expertise of our scientific staff to solve fundamental questions regarding U.S. energy independence and to translate discoveries into deployable technologies. The laboratory has identified several energy focus areas – including biofuels, complex materials, catalysis, and solar energy.
BNL's one-of-kind user facilities include the National Synchrotron Light Source II NSLS-II, which produces extremely bright beams of x-ray, ultraviolet, and infrared light for scientists exploring materials—including superconductors, catalysts, geological samples, and proteins—to accelerate advances in energy, environmental science, and medicine. Scientists at our Center for Functional Nanomaterials create materials and explore their unique structure and properties at the nanoscale, with a focus on more efficient solar and energy storage materials. And at BNL's Northeast Solar Energy Research Center, where researchers from labs, academia, and industry study test new solar technologies, working to make solar "power plants" more efficient and economical
In addition to fundamental research, the laboratory actively collaborates with industry and other academic institutions to bring the benefits of scientific discoveries to the marketplace. Brookhaven's Office of Strategic Partnerships integrates Brookhaven Lab's industry engagement, technology licensing, and economic development functions to expand the impact of collaborative research and technology commercialization. Strategic Partnerships supports the Laboratory's science mission through identifying, pursuing and managing partnerships with a broad set of private-sector companies, federal agencies, and non-federal entities. For information on licensing and industry.
Jonathan Carter is the Associate Laboratory Director for Computing Sciences at Lawrence Berkeley National Laboratory (Berkeley Lab). The Computing Sciences Area at Berkeley Lab encompasses the National Energy Research Scientific Computing Division (NERSC), the Scientific Networking Division (home to the Energy Sciences Network, ESnet) and the Computational Research Division.
Dr. Carter's research interests are in the evaluation of system architectures and algorithms for high-performance computing, and in computational chemistry and physics simulations. Recently he has been engaged in a project to look at computer architectures beyond the end of Moore's Law and has focused on techniques to perform simulations for computational chemistry using newly developed quantum computing test-beds. He brings a unique perspective to his work, formed from using computing resources as a domain scientist, from performing performance analyses of computer architectures, and from his experience in moving large-scale computational systems from idea to reality.
Carter joined Computing Sciences as part of the National Energy Research Scientific Computing (NERSC) Division at the end of 1996, working with a broad range of scientists to optimize applications, transition projects from shared-memory vector systems to massively parallel systems, and providing in-depth consulting for materials scientists and chemists using NERSC. He became group leader of the consulting group at the end of 2005. During his time at NERSC, he led or played a lead role in teams that procured and deployed three of the fastest computing systems in the world.
Areas of expertise: quantum computing, beyond Moore's Law computer architectures, high-performance computing (HPC) / supercomputing, and computational chemistry.