National Energy Research Scientific Computing Center

We present the design for a thermodynamic computer that can perform arbitrary nonlinear calculations in or out of equilibrium. Simple thermodynamic circuits, fluctuating degrees of freedom in contact with a thermal bath and confined by a quartic potential, display an activity that is a nonlinear function of their input. Such circuits can therefore be regarded as thermodynamic neurons, and can serve as the building blocks of networked structures that act as thermodynamic neural networks, universal function approximators whose operation is powered by thermal fluctuations. We simulate a digital model of a thermodynamic neural network, and show that its parameters can be adjusted by genetic algorithm to perform nonlinear calculations at specified observation times, regardless of whether the system has attained thermal equilibrium. This work expands the field of thermodynamic computing beyond the regime of thermal equilibrium, enabling fully nonlinear computations, analogous to those performed by classical neural networks, at specified observation times.

Small regulatory RNAs (sRNAs) are fast-acting non-coding RNAs (ncRNAs), stress-responsive regulators that fine-tune bacterial gene expression, shaping virulence, antimicrobial resistance, metabolism, and biofilm development. At the post-transcriptional level, sRNAs pair with target mRNAs to block or enhance translation, remodel secondary structures, adjust transcript stability, and act as molecular sponges for other sRNAs. Staphylococcus aureus , a leading cause of hospital-acquired infections, relies on a multiple-layered regulatory network, including post-transcriptional mechanisms, to transition between planktonic and biofilm lifestyles. Here, we expand the cross-lineage sRNA repertoire of S. aureus by integrating newly generated RNA-seq data from the Brazilian ST239 Bmb9393 strain with public datasets from five USA-lineage strains previously uncharacterized for sRNAs. Using sequence homology and covariance models, we predicted and annotated candidate sRNA loci across all analyzed genomes, quantified their expression under planktonic and biofilm conditions, and assigned genomic context. Integration of differential-expression (DE) profiles with weighted gene co-expression network analysis (WGCNA) identified sRNAs associated with biofilm and virulence, in modules that include well-known regulators ( sarA , mgrA , RNAIII) and the adhesin clfA . To prioritize functional target interactions, we combined DEG concordance, network features, and interaction-energy thresholds, depleting millions of initial predictions to thousands of high-confidence sRNA-mRNA pairs. Our integrative bioinformatics framework provides additional insights into sRNA-mediated regulation in S. aureus , highlighting biofilm- and resistance-linked candidates, and yields a ranked, reusable set of sRNA-mRNA interactions to guide hypothesis-driven experiments across diverse genetic backgrounds.

Simulating dynamics of physical systems is a key application of quantum computing, with potential impact in fields such as condensed matter physics and quantum chemistry. However, current quantum algorithms for Hamiltonian simulation yield results that are inadequate for real use cases and suffer from lengthy execution times when implemented on near-term quantum hardware. In this work, we introduce a framework for evaluating the performance of quantum simulation algorithms, focusing on the computation of observables, such as energy expectation values. Our framework provides end-to-end demonstrations of algorithmic optimizations that utilize Pauli term groups based on <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex>-commutativity, generate customized Clifford measurement circuits, and implement weighted shot distribution strategies across these groups. These demonstrations span multiple quantum execution environments, allowing us to identify critical factors influencing runtime and solution accuracy. We integrate enhancements into the QED-C Application-Oriented Benchmark suite, utilizing problem instances from the opensource HamLib collection. Our results demonstrate a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{2 7. 1} \boldsymbol{\%}$</tex> error reduction through Pauli grouping methods, with an additional 37.6% improvement from the optimized shot distribution strategy. Our framework provides an essential tool for advancing quantum simulation performance using algorithmic optimization techniques, enabling systematic evaluation of improvements that could maximize near-term quantum computers’ capabilities and advance practical quantum utility as hardware evolves.

Spontaneous self-organization is ubiquitous in systems far from thermodynamic equilibrium. While organized structures that emerge dominate transport properties, universal representations that identify and describe these key objects remain elusive. Here, we introduce a theoretically grounded framework for describing emergent organization that, via data-driven algorithms, is constructive in practice. Its building blocks are spacetime lightcones that embody how information propagates across a system through local interactions. We show that predictive equivalence classes of lightcones-local causal states-capture organized behaviors in complex spatiotemporal systems. Employing an unsupervised physics-informed machine learning algorithm and a high-performance computing implementation, we demonstrate automatically discovering organized structures in two real-world domain science problems. We show that local causal states identify vortices and track their power-law decay behavior in two-dimensional fluid turbulence. We then show how to detect and track familiar extreme weather events-hurricanes and atmospheric rivers-and discover other novel structures associated with precipitation extremes in high-resolution climate data at the grid-cell level.

Machine learning-based simulations, especially calorimeter simulations, are promising tools for approximating the precision of classical high energy physics simulations with a fraction of the generation time. Nearly all methods proposed so far learn neural networks that map a random variable with a known probability density, like a Gaussian, to realistic-looking events. In many cases, physics events are not close to Gaussian and so these neural networks have to learn a highly complex function. We study an alternative approach: Schrödinger bridge Quality Improvement via Refinement of Existing Lightweight Simulations (SQuIRELS). SQuIRELS leverages the power of diffusion-based neural networks and Schrödinger bridges to map between samples where the probability density is not known explicitly. We apply SQuIRELS to the task of refining a classical fast simulation to approximate a full classical simulation. On simulated calorimeter events, we find that SQuIRELS is able to reproduce highly non-trivial features of the full simulation with a fraction of the generation time.

Winter storms in the Beaufort Sea can trigger large-scale sea ice breakup, opening leads and enhancing ice mobility. These events can influence the mixed layer through various processes, like brine rejection and shear-driven mixing. However, their rel

The aesthetic platonic solids have been known since ancient times, and the structure of all five platonic solids is also found in chemical compounds. While gold sub‐nanometer clusters and gold nanoparticles with an icosahedral structure have been known for a long time to exist, a multi‐shell icosahedral gold cluster at the intermediate size between 13 and thousands of atoms has been elusive. Here we present the synthesis and crystallographic characterization of the first triple‐shell icosahedral metal cluster, Au147(SPh)30(PPh3)121. The gold core in 1 is stabilized by phosphines and thiolates, but surprisingly no staple motifs are formed. A second cluster, Au146(SPh)30(PPh3)122, cocrystallizes and is identified as having a closed electronic shell but can be considered as a geometrically open pendant of 1. The unique clusters are characterized experimentally by EDX, UV/vis, DLS, and EPR and theoretically by quantum chemical calculations.

DIII-D research is being accelerated by leveraging high performance computing (HPC) and data resources available through the National Energy Research Scientific Computing Center (NERSC) Superfacility initiative. As part of this initiative, a high-resolution, fully automated, whole discharge kinetic equilibrium reconstruction workflow was developed that runs at the NERSC for most DIII-D shots in under 20 min. This has eliminated a long-standing research barrier and opened the door to more sophisticated analyses, including plasma transport and stability. These capabilities would benefit from being automated and executed within the larger Department of Energy Advanced Scientific Computing Research program’s Integrated Research Infrastructure (IRI) framework. The goal of IRI is to empower researchers to meld DOE’s world-class research tools, infrastructure, and user facilities seamlessly and securely in novel ways to radically accelerate discovery and innovation. For transport, we are looking at producing flux matched profiles and also using particle tracing to predict fast ion heat deposition from neutral beam injection before a shot takes place. Our starting point for evaluating plasma stability focuses on the pedestal limits that must be navigated to achieve better confinement. This information is meant to help operators run more effective experiments, so it needs to be available rapidly inside the DIII-D control room. So far this has been achieved by ensuring the data is available with existing tools, but as more novel results are produced new visualization tools must be developed. In addition, all of the high-quality data we have generated has been collected into databases that can unlock even deeper insights. This has already been leveraged for model and code validation studies as well as for developing AI/ML surrogates. The workflows developed for this project are intended to serve as prototypes that can be replicated on other experiments and can be run to provide timely and essential information for ITER, as well as next stage fusion power plants.

Complex data center network providers have long been characterized by their closed and proprietary nature. A programmatic approach allows for the dynamic management of network entities that can simplify and streamline the deployment, configuration, and management processes while building the foundational tools towards an open standards Software Defined Networking (SDN). The introduction of a new automated model into the data center network world immensely enhances programmability. As increasingly complicated multivendor networks emerge, the need for a new automation model becomes even more critical. A dynamic workflow provides an avenue for the engineering teams to automate the multi-vendor device onboarding process, without having to rely on manual configuration and provisioning, thus reducing the time and effort for the heterogeneous network entities. This paper expands this vision, describing some of the motivating use cases in more detail, delineating our proposed architecture and implementation, chronicling our progress to date, and outlining future plans.