Artificial intelligence (AI) is reshaping how research is conceived, conducted, and communicated across fields from chemistry to biomedicine. This commentary examines how AI is transforming the research workflow. AI systems now help researchers manage the information deluge, filtering the literature, surfacing cross-disciplinary links for ideas and collaborations, generating hypotheses, and designing and executing experiments. These developments mark a shift from AI as a mere computational tool to AI as an active collaborator in science. Yet this transformation demands thoughtful integration and governance. We argue that at this time AI must augment but not replace human judgment in academic workflows such as peer review, ethical evaluation, and validation of results. This paper calls for the deliberate adoption of AI within the scientific practice through policies that promote transparency, reproducibility, and accountability.

Machine learning inter-atomic potentials (MLIPs) are revolutionizing the field of molecular dynamics (MD) simulations. Recent MLIPs have tended towards more complex architectures trained on larger datasets. The resulting increase in computational and memory costs may prohibit the application of these MLIPs to perform large-scale MD simulations. Here, we present a teacher-student training framework in which the latent knowledge from the teacher (atomic energies) is used to augment the students' training. We show that the light-weight student MLIPs have faster MD speeds at a fraction of the memory footprint compared to the teacher models. Remarkably, the student models can even surpass the accuracy of the teachers, even though both are trained on the same quantum chemistry dataset. Our work highlights a practical method for MLIPs to reduce the resources required for large-scale MD simulations.

They're balloons – but not as we know them. Scientists at New Mexico's Los Alamos National Laboratory are working on a'vacuum balloon' with a hard shell that could eventually carry humans and travel'as fast as a commercial airliner'. Miles Beaux, a physicist at the lab, told DailyMail.com in an exclusive interview that if his experiments are successful the craft could be used for transport, surveillance, and even for parcel delivery drones. Beaux and his chemist colleague Chris Hamilton have been making small, hollow spheres out of a super-lightweight material called aerogel, then sucking the air out of them in an attempt to create a solid ball that is lighter than the surrounding atmosphere – allowing it to hover. The'vacuum balloons' would trump traditional helium or hydrogen balloons, which slowly lose their lift, and could potentially carry objects in the air indefinitely.

We consider the problem of computing a sparse binary representation of an image. To be precise, given an image and an overcomplete, non-orthonormal basis, we aim to find a sparse binary vector indicating the minimal set of basis vectors that when added together best reconstruct the given input. We formulate this problem with an $L_2$ loss on the reconstruction error, and an $L_0$ (or, equivalently, an $L_1$) loss on the binary vector enforcing sparsity. This yields a so-called Quadratic Unconstrained Binary Optimization (QUBO) problem, whose solution is generally NP-hard to find. The contribution of this work is twofold. First, the method of unsupervised and unnormalized dictionary feature learning for a desired sparsity level to best match the data is presented. Second, the binary sparse coding problem is then solved on the Loihi 1 neuromorphic chip by the use of stochastic networks of neurons to traverse the non-convex energy landscape. The solutions are benchmarked against the classical heuristic simulated annealing. We demonstrate neuromorphic computing is suitable for sampling low energy solutions of binary sparse coding QUBO models, and although Loihi 1 is capable of sampling very sparse solutions of the QUBO models, there needs to be improvement in the implementation in order to be competitive with simulated annealing.

A team of scientists at Los Alamos National Laboratory is applying machine-learning algorithms to subsurface imaging that will impact a variety of applications, including energy exploration, carbon capture and sequestration and estimating pathways of subsurface contaminant transport, according to new research published in IEEE Signal Processing Magazine. "The subsurface is extremely complex and full of uncertainty, and knowledge of its physical properties is vital for a variety of applications," said Youzuo Lin of Los Alamos' Energy and Earth System Science group and lead author of the paper. "This paper is the first systematic survey on physics-guided machine-learning techniques for computational wave imaging." The authors reviewed more than a 100 research articles, organizing them within a structured framework that highlights the most significant recent innovations in this area. These insights will be of value not only for subsurface imaging, but also for other computational wave imaging problems such as medical ultrasound imaging and acoustic sensing for materials science. The process of obtaining subsurface data from surface measurements is called seismic inversion.

LOS ALAMOS, N.M., Nov. 10, 2021--Researchers at Los Alamos National Laboratory are applying machine learning algorithms to help interpret massive amounts of ground deformation data collected with Interferometric Synthetic Aperture Radar (InSAR) satellites; the new algorithms will improve earthquake detection. "Applying machine learning to InSAR data gives us a new way to understand the physics behind tectonic faults and earthquakes," said Bertrand Rouet-Leduc, a geophysicist in Los Alamos' Geophysics group. New satellites, such as the Sentinel 1 Satellite Constellation and the upcoming NISAR Satellite, are opening a new window into tectonic processes by allowing researchers to observe length and time scales that were not possible in the past. However, existing algorithms are not suited for the vast amount of InSAR data flowing in from these new satellites, and even more data will be available in the near future. In order to process all of this data, the team at Los Alamos developed the first tool based on machine learning algorithms to extract ground deformation from InSAR data, which enables the detection of ground deformation automatically--without human intervention--at a global scale.

Artificial intelligence helps solve the most complex problems beneath our feet By Hari Viswanathan For The New Mexican May 9, 2021 Save Few technological developments have captured the minds -- and fear -- of humanity like artificial intelligence. Whether it's robots rising up to subdue their makers like in the Westworld series, or the malevolent computer Hal 9000 from the classic movie 2001: A Space Odyssey, machines that can learn are depicted as threats to the world as we know it. Obviously, these futures are the work of imaginative screenwriters. In fact, artificial intelligence, or AI, is at work in the field of geological science right now helping to preserve the world and save lives. That topic is the focus of a virtual seminar series throughout the summer called "Machine Learning in Solid Earth Geoscience," a series that has been hosted in Santa Fe in pre-COVID-19 years.

A new theorem shows that information run through an information scrambler such as a black hole will reach a point where any algorithm will be unable to learn the information that has been scrambled. A black hole permanently scrambles information that can't be recovered with any quantum machine learning algorithm, shedding new light on the classic Hayden-Preskill thought experiment. A new theorem from the field of quantum machine learning has poked a major hole in the accepted understanding about information scrambling. "Our theorem implies that we are not going to be able to use quantum machine learning to learn typical random or chaotic processes, such as black holes. In this sense, it places a fundamental limit on the learnability of unknown processes," said Zoe Holmes, a post-doc at Los Alamos National Laboratory and coauthor of the paper describing the work published on May 12, 2021, in Physical Review Letters. "Thankfully, because most physically interesting processes are sufficiently simple or structured so that they do not resemble a random process, the results don't condemn quantum machine learning, but rather highlight the importance of understanding its limits," Holmes said.

Los Alamos National Laboratory, a multidisciplinary research institution engaged in strategic science on behalf of national security, is managed by Triad, a public service oriented, national security science organization equally owned by its three founding members: Battelle Memorial Institute (Battelle), the Texas A&M University System (TAMUS), and the Regents of the University of California (UC) for the Department of Energy's National Nuclear Security Administration. Los Alamos enhances national security by ensuring the safety and reliability of the U.S. nuclear stockpile, developing technologies to reduce threats from weapons of mass destruction, and solving problems related to energy, environment, infrastructure, health, and global security concerns.

LOS ALAMOS, N.M., April 22, 2021--A new machine-learning model that generates realistic seismic waveforms will reduce manual labor and improve earthquake detection, according to a study published recently in JGR Solid Earth. "To verify the efficacy of our generative model, we applied it to seismic field data collected in Oklahoma," said Youzuo Lin, a computational scientist in Los Alamos National Laboratory's Geophysics group and principal investigator of the project. "Through a sequence of qualitative and quantitative tests and benchmarks, we saw that our model can generate high-quality synthetic waveforms and improve machine learning-based earthquake detection algorithms." Quickly and accurately detecting earthquakes can be a challenging task. Visual detection done by people has long been considered the gold standard, but requires intensive manual labor that scales poorly to large data sets.