Хиймэл оюун ухааны агент технологи бүхий энэхүү төхөөрөмж нь хэрэглэгчийн өгсөн энгийн хэлний зааврын дагуу бичил бүтцийг нарийвчлан судлах боломжтой болжээ
Аргонн үндэсний лабораторийн судлаачид өөрсдийн 26-ID рентген нанопроб төхөөрөмждөө хиймэл оюун ухааны (AI) агент системийг нэгтгэсэн байна. Энэхүү шинэчлэл нь микроскопыг хүний оролцоогүйгээр тодорхой хэсгүүдийг сонгон шинжлэх, өгөгдлийг бодит цаг хугацаанд боловсруулах боломжийг олгож байгаа юм. Туршилтын явцад уг систем нь нэгдсэн хэлхээний хоёр бүсийн заагийг илрүүлэх даалгаврыг амжилттай гүйцэтгэжээ.
Уламжлалт аргаар рентген туяаны дифракцын өгөгдлийг сэргээн боловсруулахад олон цаг эсвэл өдөр шаардагддаг байсан бол AI систем нь үүнийг шууд гүйцэтгэдэг байна. Энэхүү технологи нь SYNAPS-I төслийн хүрээнд хэрэгжиж буй хоёр дахь үе шат бөгөөд хагас дамжуулагчийн үйлдвэрлэлээс эхлээд дэвшилтэт материалын судалгаа хүртэлх олон салбарт нээлт хийх хурдыг нэмэгдүүлэх зорилготой.
Лабораторийн тооцоолон бодох ухааны эрдэмтэн Мэтью Черукарагийн мэдээлснээр, уг систем нь бие даан ажиллах микроскопууд болон автоматжуулсан согог илрүүлэх хэрэгслүүдийг хөгжүүлэх суурь болж байна. Мөн рентген нанопроб төхөөрөмжийг ажиллуулах нарийн төвөгтэй мэдлэггүй судлаачид ч харилцан ярианы интерфейсээр дамжуулан шинжлэх ухааны өндөр чанартай өгөгдлийг олж авах боломжтой болж байгааг эрдэмтэн Тао Жоу онцоллоо.
АНУ-ын Эрчим хүчний яамны удирдлага дор хэрэгжиж буй энэхүү судалгааны ажилд Лоуренс Беркли, Брукхэйвен, SLAC, Оук Рижийн үндэсний лабораториуд хамтран оролцож байна. Энэхүү төсөл нь 2025 оны 11 дүгээр сард Ерөнхийлөгч Трампын эхлүүлсэн Genesis Mission хөтөлбөрийн хүрээнд шинжлэх ухааны судалгаанд хиймэл оюун ухааныг нэвтрүүлэх томоохон алхам болж байна.
Дэлгэрэнгүйг эх сурвалжаас харах
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Researchers at Argonne National Laboratory have plugged an agentic AI platform into an X-ray nanoprobe beamline, creating a system that can get a closer look at areas researchers ask it to focus on. Argonne reported the success of an experimental run of the agentic microscope this week, with the device initially tested on its ability to locate the interface between two regions on an integrated circuit device, and all by just telling the microscope what they wanted it to determine using natural language. “Microelectronics was simply the example used to showcase the technology,” Argonne computational scientist Mathew Cherukara said of the project. “The real achievement is developing a next‑generation, AI‑enhanced microscope that can benefit many scientific fields.” The microscope itself is part of Argonne’s 26-ID hard X-ray nanoprobe beamline, which uses a tightly focused X-ray beam to perform nanoscale imaging and measurements of target samples. Such devices are considered a prime use case for scientific applications of AI because they produce a massive amount of data that can take human researchers considerable time to comb through thanks to their use of a technique known as ptychography. The method collects multiple diffraction patterns as X-rays interact with a sample, with modern X-ray experiments capable of generating millions of images in just a few hours of beam time. Conventional ptychographic reconstruction and analysis can take hours or days, whereas Argonne says its AI system can reconstruct images in real time as the data arrives. Argonne’s X-ray nanoprobe beamline is the sort of scientific facility the Trump administration is looking to boost with AI through its Genesis Mission. Launched by President Trump in November 2025 and led by the Department of Energy, Genesis aims to find ways to use AI in scientific research, including the Synergistic Neutron and Photon Science – Intelligence (SYNAPS-I) project that Argonne is undertaking in partnership with Lawrence Berkeley National Laboratory, which is leading the effort. Brookhaven National Laboratory, SLAC National Accelerator Laboratory, and Oak Ridge National Laboratory are also participating in the project. The inclusion of agentic AI in the Argonne X-ray microscope is actually the second phase of the project; the first was simply to see if, as mentioned above, AI could be used to reduce ptychographic image processing time. That was a success, as the lab reported in April, and phase two’s addition of natural language agentic AI appears to have been a win as well. “Combined, these SYNAPS-I advances lay the groundwork for self-driving microscopes, closed-loop experimentation and automated defect-detection tools that promise to accelerate discovery across fields ranging from semiconductor manufacturing to advanced materials and national user facilities,” Cherukara said of the project’s work thus far. In addition to the speed at which the new machine can process data, the conversational AI added in phase two gives researchers the ability to just ask the microscope to examine an area of interest rather than having to figure out how to aim the beam themselves. The AI can not only handle data collection and processing, but also analyze the results itself and flag additional areas of interest, which it can then further investigate on its own. Tao Zhou, a scientist at Argonne, said in a video from the lab that the new, chattier version of the microscope could open up advanced scientific research to researchers who could benefit from X-ray nanoprobe beamline science but don’t know how to operate a complicated instrument like the one at his lab. “This can be a game changer for a lot of users,” Zhou said. “This kind of interactive chat interface allows them to do their scientific research, get excellent data, without having to spend too much time to learn about the beamline components.” “This will help us attract a lot of new users,” Zhou added. Just be sure not to mention toast. ®
