Unraveling the Mysteries of Consecutive Atmospheric River Events

April 18, 2024

In California's 2022-2023 winter season, nine atmospheric rivers caused severe flooding, landslides, and power outages, the longest continuous weather condition of this type in 70 years. Scientists at Berkeley Lab used machine learning to study these weather systems and found that more intense atmospheric rivers are more likely to occur successively within a short period.

EcoFABs Could Lead to Better Bioenergy Crops

February 29, 2024

Fabricated ecosystems created at Berkeley Lab will expedite microbiome research, and help underrepresented students in the classroom.

AI-based Approach Speeds Diagnosis of I/O Performance Bottlenecks in HPC

March 13, 2024

Data management researchers in Berkeley Lab's Scientific Data Division are using artificial intelligence methods to automatically diagnose I/O performance bottlenecks in HPC applications at the job level.

How Scientists Are Accelerating Chemistry Discoveries With Automation

April 8, 2024

A new automated workflow developed by scientists at Berkeley Lab has the potential to allow researchers to analyze the products of their reaction experiments in real time, a key capability needed for future automated chemical processes.

Machine Learning Yields More Efficient Hydrogen Combustion Reactivity Modeling

February 15, 2024

Researchers from Berkeley Lab, UC Berkeley, and Penn State University have shown that active learning-based machine learning atomic force prediction offers a more efficient and computationally affordable alternative to ab initio molecular dynamics in hydrogen combustion modeling.

New Tomographic Reconstruction Algorithm Developed at Berkeley Lab Sets World Record

January 17, 2024

A team of researchers from Berkeley Lab’s CAMERA developed a new tomographic reconstruction algorithm, TomoCAM, that leverages advanced mathematical techniques and GPU-based computing. Their method set a new world record by surpassing the speed of existing state-of-the-art iterative tomographic reconstruction algorithms.

Perlmutter Provides Peek into Interior of Ice Giant Planets

January 22, 2024

Researchers at UC Berkeley used the Perlmutter supercomputer at NERSC to make progress toward a better understanding of chemistry inside ice giant planets, a step forward for planetary science that may also have applications on Earth.

Machine Learning Opens New Doors in Traumatic Brain Injury Research

February 12, 2024

In a paper published in Nature Scientific Reports, researchers from Berkeley Lab, UCSF, the Medical College of Wisconsin, and UC Berkeley – in conjunction with the TRACK-TBI collaboration – describe how an innovative machine learning approach can enhance the prognosis and understanding of traumatic brain injury and other complex medical conditions.

‘Set it and forget it’: automated lab uses AI and robotics to improve proteins

January 11, 2024

A self-driving lab system spent half a year engineering enzymes to work at higher temperatures.

Berkeley Lab Researchers Publish Pioneering Book on Autonomous Experimentation

January 2, 2024

Just as auto-complete was revolutionary for text composition, autonomous experimentation will change how experiments are performed. Berkeley Lab’s Marcus Noack and Daniela Ushizima published the first-ever book dedicated to this topic.

The Future is Bright: CAMERA Mathematics for Accelerating Scientific Discovery

Berkeley Lab’s CAMERA will expand its work on developing new mathematics, algorithms, software, autonomous workflows, and real-time edge mathematics for the DOE’s next-generation X-ray light sources and neutron scattering facilities.

Berkeley Lab Machine Learning Experts Share Exciting Scientific Developments at NeurIPS 2023

November 30, 2023

In less than two weeks, several Berkeley Lab machine learning experts head to New Orleans for the 37th Conference on Information Processing Systems (NeurIPS 2023), held this year from December 10-16, 2023.

Making Rad Maps With Robot Dogs

October 11, 2023

Combining multiple sensors with smart robots is the latest evolution in mapping nuclear radiation.

The M.O. of ML: Can AI Foundation Models Drive Accelerated Scientific Discovery?

November 10, 2023

Can AI foundation models drive accelerated scientific discovery? Berkeley Lab scientists answer that question and reveal the a promising modus operandi of scientific machine learning.

ExaLearn Expands Horizons with Surrogate Modeling

June 20, 2023

Advancing surrogate modeling is one of the four main focus areas of ExaLearn, an ECP co-design center bringing machine learning to the forefront of the ECP effort. 

Using Deep Learning to Better Assess Lithium Metal Battery Performance

July 14, 2023

A team of researchers from CAMERA, Berkeley Lab, UC Irvine, and Argonne have developed batteryNET, a deep learning algorithm that enhances the assessment of lithium agglomeration in solid-state lithium metal batteries.

Epidemiological Modeling in the Exascale Era

September 8, 2023

With funding from DOE’s BRaVE initiative, the Berkeley Lab-led EMERGE project will expand the capabilities of ExaEpi, an exascale-ready epidemiological agent-based model to target six diseases.

Berkeley Lab Plays Major Role in Developing the DOE Vision for AI

June 12, 2023

Berkeley Lab’s Computing Sciences Area leadership and staff were instrumental in developing the U.S. Department of Energy's vision for AI, detailed in a new report released today.

Harnessing Machine Learning to Make Complex Systems More Energy Efficient

May 12, 2023

In computer simulations, Berkeley Lab’s Stephen Whitelam used neural networks to train nanosystems, which are tiny machines about the size of molecules, to work with greater energy efficiency. 

Berkeley Lab Computing Sciences Takes Novel Approach to Science Workforce Development and Diversity

May 31, 2023

The Sustainable Research Pathways (SRP) program has been connecting students from underrepresented communities and institutions to scientists and staff at Berkeley Lab for nearly a decade to facilitate research collaborations and bolster workforce development and diversity, equity, and inclusion.

NERSC Supports First All-GPU Full-Scale Physics Simulation

June 6, 2023

Using supercomputers at NERSC, researchers have completed a simulation of a detector of neutrino interactions that’s designed to run exclusively on graphics processing units (GPUs)—the first simulation of its kind and an example of using GPUs’ highly parallel structure to process large amounts of physical data.

New Math Methods and Perlmutter HPC Combine to Deliver Record-Breaking ML Algorithm

March 13, 2023

Using the Perlmutter supercomputer at NERSC, researchers at Berkeley Lab have devised a new mathematical method for analyzing extremely large datasets—and, in the process, demonstrated proof of principle on a record-breaking dataset of more than five million points. 

Meet the Autonomous Lab of the Future

April 17, 2023

Robots operate instruments and artificial intelligence makes decisions to find useful new materials at the A-Lab 

Berkeley Lab Scientists Create Machine Learning Pipeline for Interpreting Large Tomography Datasets

January 25, 2023

A group of Berkeley Lab scientists has developed and tested several machine learning techniques organized in a learning pipeline to improve the interpretation of increasingly large cryo-ET datasets.

Berkeley Lab’s Ushizima Honored with PMWC Pioneer Award

January 25, 2023

Berkeley Lab’s Daniela Ushizima was recognized with PMWC Pioneer Award for constructing “a new and reliable technique for diagnosing Alzheimer’s disease and measuring the efficacy of experimental treatments.”

Improving Training for Scientific Machine Learning

March 3, 2023

In the world of scientific machine learning (SciML), scientists are beginning to embrace the use of neural networks as a way to accelerate simulations. At the heart of deep learning algorithms, neural networks provide a mechanism to encode complex dependency structures, using many connected node layers to transform data into learned features to be used for a wide range of scientific tasks.

Machine Learning Tackles Long COVID

January 5, 2023

A new machine learning tool developed by a Berkeley Lab-led team analyzes electronic health records to help understand how and why some individuals develop long COVID symptoms. The tool may even enable more effective treatments by helping clinicians develop tailore therapies to different patient groups.

HYPPO: Leveraging Prediction Uncertainty to Optimize Deep Learning Models for Science

March 1, 2022

A team of computational researchers has developed a new software tool for conducting hyperparameter optimization (HPO) of deep neural networks while taking into account the prediction uncertainty that arises from using stochastic optimizers for training the models. Dubbed “HYPPO,” this open-source package is designed to optimize the architectures of deep learning models specifically for scientific applications.

Advancing New Battery Design with Deep Learning

April 4, 2022

A team of researchers from Lawrence Berkeley National Laboratory (Berkeley Lab) and the UC Irvine recently moved this effort forward with the development of deep-learning algorithms to automate the quality control and assessment of new battery designs.

Computational Analysis Enables Breakthrough in Biomolecular Dynamics

April 20, 2022

A new study with data analyses from Berkeley Lab computational researchers helps broaden the physical understanding of biomolecular assembly by tracking motion at unprecedented resolution and defining a general procedure for using in situ visualization and machine learning to explore such dynamics.

New Encoder-Decoder Overcomes Limitations in Scientific Machine Learning

May 2, 2022

A team of Berkeley Lab computational researchers led by Talita Perciano from the Scientific Data Division's Machine Learning and Analytics Group introduced a new Python-based encoder-decoder framework that overcomes both the segmentation and widespread adaptability problems.

Neuroscience Simulations Shed Light on Origins of Human Brain Recordings

July 11, 2022

A Berkeley Lab-led team is establishing a new understanding of which neurons, precisely, are generating signals recorded at the brain's surface. Their a seven-year search included developing new ECoG devices, performing brain surgery on rats to record their neurological signals, developing new machine-learning algorithms to process the data, and running a full-scale biophysically accurate simulation at NERSC.

Machine Learning Paves Way for Smarter Particle Accelerators

July 19, 2022

Scientists have developed a new machine-learning platform that makes the algorithms that control particle beams and lasers smarter than ever before.

Exabiome Brings Metagenomics Into the Exascale Era

October 10, 2022

The Exabiome project is developing novel software tools that allow researchers to harness the power of cutting-edge high performance computers (and now exascale supercomputers) alongside graph neural networks to solve previously infeasible problems in metagenomics.

How Do You Solve a Problem Like a Proton? You Smash It to Smithereens – Then Build It Back Together With Machine Learning

October 25, 2022

A new tool decodes proton snapshots captured by history-making particle detector in record time.

Cutting Through the Noise

Berkeley Lab’s Applied Mathematics and Computational Sciences Division and its Physics Division  joined forces to create a new approach to quantum error mitigation. "Noise estimation circuits" could help make quantum computing’s theoretical potential a reality.

February 23, 2022

Plants Buy Us Time to Slow Climate Change – But Not Enough to Stop It

December 8, 2021

 An international team of researchers led by Lawrence Berkeley National Laboratory (Berkeley Lab) and UC Berkeley have used a novel methodology combining remote sensing, machine learning, and terrestrial biosphere models to find that, as our planet warms, plants are also photosynthesizing more CO2.  The difference, however, can't make up for the rate at which humanity is releasing the greenhouse gas into the atmosphere.

FasTensor: Faster Data Analysis Designed for Science

January 26, 2022

A Berkeley Lab team has developed a machine learning tool that can analyze scientific datasets up to 1,000 times faster than its general-purpose counterparts. FasTensor, created specifically for the needs of science, can handle terabyte data-analysis tasks running in parallel on supercomputers.

Deep Learning Tactics Speed Quantum Simulations

October 4, 2021

Berkeley Lab and UC Berkeley researchers are using an AI technique called reinforcement learning to optimize quantum simulations and speed the time it takes to create and test a range of quantum architecture designs.  

Finally! Accurate Protein Prediction Courtesy of Artificial Intelligence

September 7, 2021

The dream of predicting a protein shape just from its gene sequence is now a reality thanks to an artificial intelligence (AI) algorithm recently validated by a research collaboration including Berkeley Lab’s Molecular Biophysics & Integrated Bioimaging Division. Predicting protein shapes is a long sought-after breakthrough for structural biologists because it offers a key to understanding protein functions to accelerate treatments for diseases like cancer and COVID-19.

X-Ray Experiments, Machine Learning Could Trim Years Off Battery R&D

April 2, 2021

Berkeley Lab’s COSMIC X-ray instrument  contributed to a battery study that used an innovative approach to machine learning to speed up the learning curve about a process that shortens the life of fast-charging lithium batteries. 

Supernovae Twins Open Up New Possibilities for Precision Cosmology

May 6, 2021

Using machine learning techniques, Berkeley Lab cosmologists have found a way to double the accuracy of measuring distances to supernova explosions – one of their tried-and-true tools for studying the mysterious dark energy that is making the universe expand faster and faster. 

Researchers Hunt for New Particles in Collider Data

February 19, 2021

Berkeley Lab physicists are employing machine learning algorithms to search for new particles in the Large Hadron Collider's  ATLAS detector data collected from 2015-18. 

Berkeley Lab’s Advanced Monitoring Capabilities Still in Use 10 Years After Fukushima Earthquake and Nuclear Power Plant Disaster

March 11, 2021

Eleven years after Berkeley Lab scientists began monitoring  radiation loosed by an earthquake that struck the Fukushima nuclear powerplant in Japan, their monitoring efforts are still going strong. Now researchers are using machine learning to help improve the efficiency of their long-term monitoring efforts.  

Berkeley Lab Scientist’s Work Deciphering Early Universe Secrets Garners 2021 Gruber Cosmology Prize

October 2, 2020

A trio of physicists – including Uroš Seljak of Lawrence Berkeley National Laboratory (Berkeley Lab) – has been awarded the 2021 Gruber Cosmology Prize for their work studying the large-scale structure of the universe as well as the properties of its first instant of existence. Seljak's recent work incorporates machine learning and other advanced computational techniques critical to the success of upcoming cosmology experiments including DESI and CMB-S4

AI for Efficient Flying Qubits

Project Aims to Up the Efficiency of Converting Optic Signals to Quantum Connections

December 2, 2020

How do quantum computers connect over fiber networks? Right now, not very well. A team of Berkeley Lab scientists led by post-doctoral researcher Mekena Metcalf is using artificial intelligence techniques to boost the efficiency of a key process that enables qubits to "fly" across optical fiber.

Artificial Intelligence Finds More Than 1,200 Gravitational Lensing Candidates

February 2, 2021

A research team with participation by Berkeley Lab physicists has used artificial intelligence to identify more than 1,200 possible gravitational lenses – objects that can be powerful markers for the distribution of dark matter. The count, if all of the candidates turn out to be lenses, would more than double the number of known gravitational lenses. 

(Photo Credit: PlasmaChem)

A Machine Learning Solution for Designing Optical Materials

December 2, 2020

Understanding how matter interacts with light – its optical properties – is critical in a myriad of energy and biomedical technologies, but calculating these properties is computationally intensive. And the inverse problem – designing a structure with desired optical properties – is even harder.

Now Berkeley Lab scientists have developed a machine learning model that can be used for both problems – calculating optical properties of a known structure and, inversely, designing a structure with desired optical properties. 

Driver-less Data Acquisition

Self-learning algorithm breaks new ground in data collection and analysis

October 2, 2020

A self-learning algorithm developed by the CAMERA group at Berkeley Lab has enabled researchers at the Institut Laue-Langevin to for the first time run an autonomous data analysis during a neutron scattering experiment. 

Machine Learning Takes on Synthetic Biology

September 25, 2020

Berkeley Lab scientists are developing smart algorithms with the potential to reduce bioengineering development cycles from years to months or even weeks.  The field of synethic biology is rife with possibilities that reach beyond today's  consumer-products applications (from burgers to face creams) and into fields of medicine, agriculture, climate, energy, and materials

ENDURABLE: Berkeley Lab's Aggregate Data Standard for AI Modeling

August 31, 2020

Much of today's scientific data comes in a form that machine learning algorithms can’t use effectively.  Berkeley Lab's ENDURABLE aims to make complex datasets compiled from a wide variety of sources more useful to scientists using the latest artificial intelligence techniques to make new discoveries.

Clues to COVID-19 Treatments Could Be Hiding in Existing Data

Berkeley Lab Scientists Want to Find Them

July 15, 2020

Berkeley Lab computing and bioinformatics experts are working together to develop a platform that consolidates  COVID-19 data sources and uses the unified library to make predictions – about potential drug targets, for example.

Can CT Scans Be Used to Quickly and Accurately Diagnose COVID-19?

June 1, 2020

Berkeley Lab data scientist Daniela Ushizima is exploring whether image recognition algorithms and a data analysis pipeline powered by machine learning can help accurately distinguish COVID-19 abnormalities in CT scans and chest X-rays from other overlapping respiratory illnesses.

Smart Farms of the Future

Making Bioenergy Crops More Environmentally Friendly

June 2, 2020

Berkeley Lab scientists are marrying machine learning with agriculture in field research that also includes molecular biology, biogeochemistry, environmental sensing technologies, and machine learning. This “farm of the future” could revolutionize agriculture and create sustainable farming practices that benefit both the environment and farms.

Mining  the Literature for New Insights

Machine Learning Tool Sets Sites on COVID-19

April 28, 2020

A team of Berkeley Lab scientists who normally spend their time researching things like high-performance materials for thermoelectrics or battery cathodes have built a text-mining tool in record time to help the global scientific community synthesize the mountain of scientific literature on COVID-19 being generated every day.

Estimating COVID-19's Seasonal Cycle

May 20, 2020

Is COVID-19 seasonal like the flu, waning in warm summer months then resurgent in the fall and winter? Berkeley Lab has launched a new project to apply machine-learning methods to health and environmental datasets, combining high-res climate models and seasonal forecasts, to tease out the answer.  

Seeing the Universe Through New Lenses

May 14, 2020

Berkeley Lab scientists are employing  a  neural network and an award-winning algorithm to sort through troves of ground-based space images and identify strong  gravitational lenses.  Created by galaxies and other massive space objects, the lense  bend light to reveal distant objects and phenomena along the same path, making them powerful probes of dark matter, dark energy, and other cosmic mysteries. 

Applying ‘Decision Science’ to Transportation Behaviors

March 12, 2020

Why do some people adopt electric vehicles, ride-sharing, ride-hailing, or online shopping while others don’t? Berkeley Lab researcher Anna Spurlock spearheads the WholeTraveler Transportation Behavior Study, a three-year project that uses machine learning techniques to try to understand what drives human choices in transportation. 

Microbiome Matchmaker

December 2, 2019

A unique neural network architecture called mmvec (microbe-metabolite vectors) is opening new windows into the complex microbial communities in the environment and our bodies. Such "microbiomes"  are devilishly difficult to untangle, but doing so could help us understand processes that give rise to many biological and medical mysteries, such as inflammatory bowel disease.

Simulating the Universe in TensorFlow

March 6, 2020

In this blog post, Berkeley Lab's Uroŝ Seljak and his collaborators from the Berkeley Center for Cosmological Physics explain how to simulate a  tiny universe in their FlowPM, a machine learning  cosmological simulator that's both faster and smarter than conventional approaches.

Artificial Intelligence to Enable Smart Farming

February 28, 2020

Lawrence Berkeley National Lab scientists Ben Brown and Haruko Wainwright have been working with the University of Arkansas and Glennoe Farms on a pilot project called the “AR1K Smart Farm”. The project aims to improve sustainability in agriculture – increasing the yields while reducing chemical inputs and improving soil health - using sensors and artificial intelligence. 

Stabilizing Synchrotron Light

November 19, 2019

Machine learning can predict noisy fluctuations in synchrotron light sources and correct them before they occur. The breakthrough solves a decades old issue and will make new, higher powered light sources, such as the Advanced Light Source located at Berkeley Lab,  even more useful to scientists.  

Etalumis Reverses Simulations to Reveal New Science

November 12, 2019

Deep neural networks are one key to helping scientists "reverse engineer" simulations, feeding experimental datasets back into models to reveal new science.  Large Hadron Collider data provided the test case for this new framework, called Etalumis.

Deep Learning Expands Study of Nuclear Waste Remediation

November 9, 2019

A research collaboration including Berkeley Lab demonstrated how physics-informed generative adversarial networks (GANs)  can be used in analyzing complex, large-scale science problems. The team modeled subsurface flows in the study of nuclear waste remediation, achieving exaplop/s-throughput in the process. 

Sherry Li

Berkeley Lab Researchers Help Caltrans Predict Traffic Tie-Ups

November 4, 2019

Berkeley Lab researchers are using machine learning  to help improve Caltrans’ real-time decision making with more accurate traffic modeling. The system is being tested in Los Angeles County's I-210 corridor testbed in conjunction with state and local agencies.

Predicting Watershed Behavior

July 30, 2019

Studying watersheds using learning techniques can lead to better predictions about downstream water supply and quality while offering better information for stewarding  these vital resources.

Putting AI Tools in the Hands of Scientists

July 29, 2019

Some 175 researchers attended the DL4Sci School to acquire new tools and skills that advance deep learning studies

Learning to Look for Elusive Inoviruses

July 22, 2019

Using a machine learning approach, a team led by the Joint Genome Institute at Berkeley Lab has opened a new window on the number and diversity of elusive inoviruses. Trawling caches of metagenomic data, the researchers' machine learning method identified 10,000 probable inovirus sequences in six broad families. 

A Clearer Understanding of  Extreme Weather 

July 24, 2019

Berkeley Lab scientists share their cutting edge machine learning and deep learning studies of climate and extreme weather.

Mining for Hidden Knowledge

July 3, 2019

Berkeley Lab study finds that text mining of scientific literature can lead to new discoveries.

To Pump or Not to Pump?

June 3, 2019

Berkeley Lab scientists aim to create a new machine learning tool for smarter groundwater management.

The Nature of Dark Matter

May 14, 2019

Using deep learning a Lab-led team aims  to enhance the use of gravitational lensing to study dark matter.

The Future of Scientific Machine Learning

April 15, 2019

Early, mid-career scientists identify applied math challenges, sow seeds of future collaborations.

The Quest to Convert CO₂ into Fuels

April 8, 2019

Materials Project's machine learning approach opens new vistas in photocathode research. 

Identifying Suicide Risks in Veterans

April 4, 2019

Berkeley Lab team uses deep learning to help VA address veterans' psychological, medical challenges. 

Better Simulations on the Cheap

March 10, 2019

ExaLearn will interpolate details from sparse data to create surrogate models, first for cosmology.

Detecting Global Climate Patterns

February 25, 2019

ClimateNet will use deep learning to shed light on the changing behavior of climate and extreme weather.

Combing Cosmological Data for the Big Picture

January 25, 2019

Berkeley Lab researcher wins Large Synoptic Survey Telescope machine-learning competition.

Tracing the Nature of Neutrinos

December 21, 2018

IceCube research garners best paper award at IEEE machine learning conference.

Identifying Extreme Weather

November 20, 2018

Berkeley Lab-led team shares 2018 Gordon Bell Prize for deep learning climate application.

Optimizing Traffic  Models

October 28, 2018

Berkeley Lab team using machine learning in smart and sustainable mobility solutions.

Supporting Sustainable Farming

October 28, 2018

Harnessing the power of machine learning and microbiology

Topology, Physics & Machine Learning Take on Climate Research Data Challenges

September 4, 2018

New data analytics tools could dramatically impact large-scale science data projects

Probing the Fabric of the Cosmos

September 5, 2018

Deep Learning and 3D simulations assist scientists  in exploring the physics of the universe.

Learning from Living Cells

August 23, 2018

Infrared beams, machine learning show cell types in a different light

A Groundwater Early Warning System

 August 13, 2018

Berkeley Lab researchers devise system to monitor contaminant plumes

Berkeley Lab-BIDS Fellows Share Machine Learning Expertise 

August 30, 2018

Partnership Enriches ML4Sci Workshop and California Water Data Hackathon

Pinpointing Earthquake Impacts

June 28, 2018

Using ML techniques, new simulations can break down potential major quake impacts by building location and size.

The Challenges of Big Data and Advanced Computing Solutions

July 12, 2018

Berkeley Lab's Katherine Yelick contributes expert testimony to Congressional Committee hearing.

A ‘Google for Science’

June 19, 2018

Berkeley Lab team automates metadata discovery to search scientific images and data.

New ECP Co-Design Center to Focus on Exascale Machine Learning

July 20, 2018

Berkeley Lab One of Eight National Labs Participating in 'ExaLearn'

Physicists, Machine Learning Experts Team Up to Tackle TrackML

June 11, 2018

ML challenge aims to quickly reconstruct the paths of millions of electrically charged particles created in colliders.

Biofuels Bonanza

May 29, 2018

Predicting microbial biofuel production to speed up bioengineering

Extreme Weather

March 29, 2018

Deep learning at 15 pflops to identify extreme weather at scale

Metagenomic Clustering

March 12, 2018

Making sense of a genomic ‘data deluge’

Teaching Computers to Guide Science

March 6, 2018

Berkeley Lab, UC Berkeley's “Iterative Random Forests” deliver science insights

‘Minimalist Machine Learning’

February 21, 2018

Machine learning algorithm analyzes experimental images from limited training data, speeding up the deployment of learning tools

Targeting More Effective Cancer Drugs

August 16, 2017

Machine learning model predicts protein binding for better drug candidates

Machine Learning Enhances Predictive Modeling of 2D Materials

March 2, 2017

Using machine learning algorithms could reduce the time it takes to accurately predict the physical, chemical, and mechanical properties of nanomaterials from years to months.

Unlocking Mysteries of the Universe

January 30, 2018

Teaching machines to analyze simulations of exotic subatomic ‘soup’

JCAP Study Uses Neural Net to Predict Materials' Optical Properties