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Machine Learning

Machine-Learning Model Instantly Predicts Polymer Properties

Hundreds of millions of tons of polymer materials are produced globally for use in a vast and ever-growing application space with new material demands such as green chemistry polymers, consumer packaging, adhesives, automotive components, fabrics and solar cells. But discovering suitable polymer materials for use in these applications lies in accurately predicting the properties that a candidate material will have. Obtaining a quantitative understanding of the relationship between chemical structure and observable properties is particularly challenging for polymers, due to their complex 3D chemical assembly that can consist of extremely long chains of thousands of atoms. Recently, a team…

Technology

LLNL And Amazon Web Services To Cooperate On Standardized Software Stack For HPC

Lawrence Livermore National Laboratory (LLNL) and Amazon Web Services (AWS) have signed a memorandum of understanding (MOU) to define the role of leadership-class high performance computing (HPC) in a future where cloud HPC is ubiquitous. Under the MOU, LLNL and AWS will explore software and hardware solutions spanning cloud and on-premises HPC environments, with the goal of establishing a common stack of open-source software components that can run equally well at both large HPC centers and on cloud resources. “The cloud HPC market is growing, and clouds are becoming a viable way to run HPC jobs,” said computer scientist Todd Gamblin, who is…

Machine Learning

Machine Learning Model Finds COVID-19 Risks For Cancer Patients

If a cancer patient tests positive for COVID-19, are they more likely to become hospitalized from the disease? That depends on certain risk factors, according to a new study by researchers at Lawrence Livermore National Laboratory (LLNL) and the University of California, San Francisco (UCSF), who looked to identify cancer-related risks for poor outcomes from COVID-19. Analyzing one of the largest databases of patients with cancer and COVID-19, the team found previously unreported links between a rare type of cancer — as well as two cancer treatment-related drugs — and an increased risk of hospitalization from COVID-19. The findings appear in the journal Cancer Medicine. “There is…

Study Finds New Biomarkers That Could Assist In Identifying Deep-Space Flight Risks

An international team of scientists has found new biomarkers that can be used for diagnostic purposes and potentially as predictive tools of the risks associated with deep-space flight. In their study, the team, including three researchers from Lawrence Livermore National Laboratory (LLNL), examined approximately two-decade-old blood samples from space shuttle astronauts before and after flight. Their findings have been published online in the journal Frontiers in Genetics. “We knew that nucleic acid within exosomes can be intact for 15-20 years, but we weren’t sure how space travel would affect them and whether we would find intact exosomes containing nucleic acids in the two-decade-old…

Technology

Lawrence Livermore’s Falgout Wins SIAM Supercomputing Career Prize

The Society for Industrial and Applied Mathematics (SIAM) announced Lawrence Livermore National Laboratory computational mathematician Rob Falgout as the recipient of the 2022 SIAM Activity Group on Supercomputing Career Prize. Established in 2009, the career prize recognizes “broad and distinguished contributions to the field of algorithms research and development for parallel scientific and engineering computing,” according to the organization. The prize will be awarded to Falgout during the 2022 SIAM Conference on Parallel Processing for Scientific Computing, held from Feb. 23–26. “It’s highly gratifying to be recognized for my work in multigrid algorithms research and parallel computing, and beyond flattering to be in…

Artificial Intelligence

LLNL Establishes AI Innovation Incubator To Advance Artificial Intelligence For Applied Science

Lawrence Livermore National Laboratory (LLNL) has established the AI Innovation Incubator (AI3), a collaborative hub aimed at uniting experts in artificial intelligence (AI) from LLNL, industry and academia to advance AI for large-scale scientific and commercial applications. LLNL has entered into a new memoranda of understanding with Google, IBM and NVIDIA, with plans to use the incubator to facilitate discussions and form future collaborations around hardware, software, tools and utilities to accelerate AI for applied science. In addition, several existing projects will fall under the AI3 umbrella, including continued work with Hewlett Packard Enterprise (HPE) and Advanced Micro Devices Inc. (AMD) to demonstrate the power of AI and high performance computing…

Machine Learning

Registrations Ongoing For Virtual Forum On Industrial Machine Learning Applications

Registration is open through July 29 for the first-ever Machine Learning for Industry Forum (ML4I), a three-day virtual event starting Aug. 10. The forum aims to foster and illustrate the adoption of machine learning methods for practical industrial outcomes, with a strong emphasis on manufacturing. Over the course of the event, attendees will engage in dialog about applications, tools and techniques and special topics centered around machine learning’s impact and potential in industry. Topics will include process control and optimization, computer vision and robotics, workflows, hardware and software, dataset augmentation and curation and physics-constrained learning. The forum also will highlight…

Machine Learning Aids In Materials Design

Scientists developed a machine learning algorithm to predict 3D molecular crystal density from 2D chemical structures. A long-held goal by chemists across many industries, including energy, pharmaceuticals, energetics, food additives and organic semiconductors, is to imagine the chemical structure of a new molecule and be able to predict how it will function for a desired application. In practice, this vision is difficult, often requiring extensive laboratory work to synthesize, isolate, purify and characterize newly designed molecules to obtain the desired information. Recently, a team of Lawrence Livermore National Laboratory (LLNL) materials and computer scientists have brought this vision to fruition for energetic…

Satellites May Have Underestimated Warming In The Lower Atmosphere

New research by Lawrence Livermore National Laboratory (LLNL) climate scientists and collaborators shows that satellite measurements of the temperature of the troposphere (the lowest region of the atmosphere) may have underestimated global warming over the last 40 years. The research appears in the Journal of Climate The team studied four different properties of tropical climate change. Each property is a ratio between trends in two “complementary” variables. Complementary variables — like tropical temperature and moisture — are expected to show correlated behavior. This correlated behavior is governed by basic, well-understood physical processes. The first three properties considered by the team…

The Data-Driven Future Of Extreme Physics

By applying modern machine learning and data science methods to “extreme” plasma physics, researchers can gain insight into our universe and find clues about creating a limitless amount of energy. In a recent perspective published in Nature, Lawrence Livermore National Laboratory (LLNL) scientists and international collaborators outline key challenges and future directions in using machine learning (ML) and other data-driven techniques to better understand these extreme conditions that potentially pave the pathway to nuclear fusion as an industrial power source, as well as helping to improve our understanding of the universe. Extreme plasma is described as the physics of matter…

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