{"id":140587,"date":"2026-08-15T00:14:13","date_gmt":"2026-08-15T00:14:13","guid":{"rendered":"https:\/\/www.europesays.com\/ai\/140587\/"},"modified":"2026-08-15T00:14:13","modified_gmt":"2026-08-15T00:14:13","slug":"livermore-lab-using-autonomous-ai-to-quicken-broaden-experimentation","status":"publish","type":"post","link":"https:\/\/www.europesays.com\/ai\/140587\/","title":{"rendered":"Livermore Lab using autonomous AI to quicken, broaden experimentation"},"content":{"rendered":"

As artificial intelligence becomes increasingly ubiquitous, Lawrence Livermore National Laboratory is turning it into the ultimate lab assistant, with scientists utilizing the evolving technology to accelerate experimentation and speed the discovery<\/a> of metal alloys, chemical compounds and cancer treatments.<\/p>\n

The adoption of AI at the lab has made autonomous experimentation commonplace, allowing scientists to run experiments while they\u2019re away and greatly expand the scope of scientific inquiry.<\/p>\n

\u201cWe\u2019re entering this exciting era, and we\u2019re forging that era here at the (Advanced Manufacturing Laboratory) for the next generation of experimenters,\u201d said Aldair Gongora, a Lawrence Livermore National Laboratory staff scientist who is leading the implementation of AI. \u201cWhether it\u2019s in batteries, whether it\u2019s in biology, whether it\u2019s in alloys, (scientists) now have at their fingertips the ability to run dozens or hundreds, maybe thousands and \u2013 in my dream \u2013 millions of experiments.\u201d<\/p>\n

In the past, the field of experimental science was a slow endeavor as scientists were limited to changing one variable at a time before recording an experiment\u2019s results. As modern labs evolved, onerous tasks like manual pipetting were automated to allow scientists to focus on the brain work of science: forming hypotheses and data analysis.<\/p>\n

\"LawrenceLawrence Livermore National Laboratory staff engineer Aldair Gongora shows a 3D hand created by one of the laboratory’s robots over the years in Livermore, Calif., Saturday, Aug. 7, 2026.\u00a0<\/p>\n

Between 2008 and 2014, advances in an early branch of artificial intelligence known as machine learning<\/a>, which allows computers to learn and adapt, sped up the experimental process at Lawrence Livermore National Laboratory. Yet those developments pale in comparison to the recent growth of artificial intelligence, said Christopher Spadaccini, Materials Engineering Division leader at Lawrence Livermore National Laboratory.<\/p>\n

\u201cWe\u2019re at the tip of the iceberg right now. We\u2019re just learning what this can do,\u201d Spadaccini said. \u201cThat interface with hardware and the physical world is really exciting, and I think it\u2019s set to explode.\u201d<\/p>\n

Inside the Advanced Manufacturing Lab<\/a>, Gongora talked over the hum of whirring automated instruments, including one humorously labeled \u201cPeter Pipetter.\u201d\u00a0As the principal investigator of Project ARMOR<\/a> \u2013 Advanced Robotics for Materials Manufacturing Optimization and Research \u2013 Gongora oversees AI automation of the experimental process across materials science, chemistry and biology.<\/p>\n

Many material properties can only reliably be determined by experiment, so that process remains the \u201cgold standard\u201d of science, he said.<\/p>\n

Take Livermorium<\/a>, for example. Element 116 on the periodic table had been purely theoretical until December 2000, when scientists at Lawrence Livermore National Laboratory synthesized it for the first time.<\/p>\n

Though the radioactive atom lasted less than 80 milliseconds before decaying to flerovium, it was long enough to prove the material existed.<\/p>\n

\u201cWhen you look at PhD dissertations that were written 10 or 20 years ago, especially experimental ones, the amount of experimental data is often very limited,\u201d Gongora said. \u201cBut now with these types of platforms and testbeds, I think even the way that scientists address these problems and the data output can fundamentally change as well.\u201d<\/p>\n

As artificial intelligence ingrains itself within Lawrence Livermore National Laboratory, staff scientists like Rodrigo Telles are building that \u201cconnective tissue\u201d between algorithms and the robots tasked with multi-phase experiments.<\/p>\n

That\u2019s because scientists\u2019 knowledge is required to \u201corchestrate\u201d sequential actions for an experiment. Otherwise, the autonomous equipment is\u00a0like a band without a conductor.<\/p>\n

\u201cYou can\u2019t load something in the centrifuge and then think that the robot\u2019s going to go grab it, right?\u201d Telles said. \u201cYou need to have steps that make sure that the centrifuge is done, your microwell plate is back where you think it\u2019s supposed to be, your robotic arm can go and grab it and retrieve it. The AI doesn\u2019t really know about that.\u201d<\/p>\n

\"LawrenceLawrence Livermore National Laboratory staff scientist Rodrigo Telles works in front of a Polyedge robot, an automated platform for polymer edging applications, in Livermore, Calif., on Saturday, Aug. 7, 2026.\u00a0<\/p>\n

Once the arrangement has been set, scientists are freed of many laborious burdens of experimentation. Gongora recalled a chemist working in the materials science lab, Sarah Finnegan, who used to spend four to six hours pipetting samples into a tube rotator. The process limited how many hypotheses she could test.<\/p>\n

Since her embrace of AI, she now orders a series of experiments to run Friday afternoon and comes back to full results on Monday, Gongora said. That additional time has allowed her to expand the scope of research.<\/p>\n

While the integration of AI is still in its infancy, scientists believe they\u2019re still at the beginning of comprehending its benefits for scientific discovery. Gongora said AI may be as revolutionary to experimental science as the supercomputer was to mathematics.<\/p>\n

Spadaccini said the scientific field is at the beginning of a new age.<\/p>\n

\u201cI\u2019m not sure we fully understand how AI is going to change how we do science and engineering,\u201d Spadaccini said. \u201cIt\u2019s a hard question to answer because I think it\u2019s so big, and it could impact every step in the process. Right now, we\u2019re just learning what steps to start with.\u201d<\/p>\n

\"LawrenceLawrence Livermore National Laboratory staff engineer Aldair Gongora shows a Polyedge robot, an automated platform for polymer edging applications, in Livermore, Calif., on Saturday, Aug. 7, 2026. LLNL is combining artificial intelligence, robotics and automation to create autonomous laboratories to significantly accelerate research in advanced manufacturing and materials engineering. (Ray Chavez\/Bay Area News Group)\u00a0
\n\"AnAn autonomous, self-driving lab for formulation and chemical screening for different purposes at Lawrence Livermore National Laboratory in Livermore, California, on Saturday, Aug. 7, 2026. LLNL is combining artificial intelligence, robotics and automation to create autonomous laboratories to significantly accelerate research in advanced manufacturing and materials engineering. (Ray Chavez\/Bay Area News Group)\u00a0
\n\"AutonomousAutonomous Alloy Prediction and Experimentation (APEX) platform for alloy discovery iat Lawrence Livermore National Laboratory n Livermore, Calif., on Saturday, Aug. 7, 2026.LLNL is combining artificial intelligence, robotics and automation to create autonomous laboratories to significantly accelerate research in advanced manufacturing and materials engineering. (Ray Chavez\/Bay Area News Group)\u00a0<\/p>\n","protected":false},"excerpt":{"rendered":"As artificial intelligence becomes increasingly ubiquitous, Lawrence Livermore National Laboratory is turning it into the ultimate lab assistant,…\n","protected":false},"author":2,"featured_media":140588,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[24,14768,25,2857,970,13603,38873,63312,2853,66,63313,160],"class_list":["post-140587","post","type-post","status-publish","format-standard","has-post-thumbnail","category-ai","tag-ai","tag-alameda-county","tag-artificial-intelligence","tag-bay-area","tag-california","tag-east-bay","tag-editors-picks","tag-keywee","tag-latest-headlines","tag-news","tag-pm-report","tag-science"],"_links":{"self":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/140587","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/comments?post=140587"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/140587\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media\/140588"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media?parent=140587"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/categories?post=140587"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/tags?post=140587"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}