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DOE OSTI2024

From Latent Dynamics to Meaningful Representations

While representation learning has been central to the rise of machine learning and artificial intelligence, a key problem remains in making the learnt representations meaningful. For this the typical approach is to regularize the learned representation through prior probability distributions. However such priors are usually unavailable or are ad hoc. To deal with this, recent efforts have shifted towards leveraging the insights from physical principles to guide the learning process. In this spirit, we propose a purely dynamics-constrained representation learning framework. Instead of relying on predefined probabilities, we restrict the latent representation to follow overdamped Langevin dynamics with a learnable transition density — a prior driven by statistical mechanics. We show this is a more natural constraint for representation learning in stochastic dynamical systems, with the crucial ability to uniquely identify the ground truth representation. We validate our framework for different systems including a real-world fluorescent DNA movie dataset. Here, we show that our algorithm can uniquely identify orthogonal, isometric and meaningful latent representations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

DONKEY: A Flexible and Accurate Algorithm for Clustering

We propose an accurate clustering algorithm suitable for the varied and multidimensional data sets that correspond to temporal snapshots from on-the-fly nonadiabatic trajectory-based simulations of photoexcited dynamics. The algorithm approximates the underlying probability density function using variable kernel density estimation, with local maxima corresponding to cluster centers. Each data point is then assigned to one of the maxima by employing a maximization procedure. Finally, clusters artificially separated by minor fluctuations in the probability density are merged. The algorithm does not require parameter tuning, which ensures flexibility and reduces the risk of bias. It is tested on several synthetic data sets, where it consistently outperforms conventional clustering algorithms. As a final example, the algorithm is applied to the excited dynamics of the norbornadiene ⇌ quadricyclane (C 7 H 8 ) molecular photoswitch, demonstrating how distinct reaction pathways can be identified.

algorithms↗

DOE OSTI2025

Deriving the Landauer Principle From the Quantum Shannon Entropy

We derive an expression to determine the equilibrium probability distribution of a quantum state in contact with a noisy thermal environment that formally separates contributions from quantum and classical forms of probabilistic uncertainty. A statistical mechanical interpretation of this probability distribution enables us to derive an expression for the minimum free energy costs for arbitrary (reversible or irreversible) quantum state changes. In conclusion, based on this derivation, we demonstrate that–in contrast to classical systems–the free energy required to erase or reset a qubit depends sensitively on both the fidelity of the target state and on the physical properties of the environment, such as the number of quantum bath states, due primarily to the entropic effects of system-bath entanglement.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Theory of Cation Solvation in the Helmholtz Layer of Li-Ion Battery Electrolytes

The solvation environments of Li + in conventional nonaqueous battery electrolytes, such as LiPF 6 in mixtures of ethylene carbaronate (EC) and ethyl methyl carbonate (EMC), are often used to rationalize transport properties and solid electrolyte interphase (SEI) formation. Solvation environments in the compact electrical double layer (EDL) next to the electrode, also known as the Helmholtz layer, determine (partially) what species can react to form the SEI, with bulk solvation environments often being used as a proxy. Here, we develop and test a theory of cation solvation in the Helmholtz layer of nonaqueous Li-ion battery electrolytes. First, we validate the theory against bulk and diffuse EDL atomistic molecular dynamics (MD) simulations of LiPF 6 EC/EMC mixtures as a function of surface charge, where we find the theory can qualitatively capture the solvation environments. Next, we turn to the Helmholtz layer, where we find the main effect of the solvation structures next to the electrode is an apparent reduction in the number of binding sites between Li + and the solvents, again where we find reasonable agreement with our developed theory. Finally, by solving a simplified version of the theory, we find that the probability of Li + binding to each solvent remains equal to the bulk probability, suggesting that the bulk solvation environments are a reasonable place to start when understanding battery electrolytes. Our developed formalism can be parametrized from bulk MD simulations and used to predict the solvation environments in the Helmholtz layer through reducing the number of available coordination sites, which can be used to determine what could react and form the SEI.

Helmholtz↗

DOE OSTI2025

Growth of Hexagonal Boron Nitride from Molten Nickel Solutions: A Reactive Molecular Dynamics Study

Metal flux methods are excellent for synthesizing high-quality hexagonal boron nitride (hBN) crystals, but the atomic mechanisms of hBN nucleation and growth in these systems are poorly understood and difficult to probe experimentally. Here, we harness classical reactive molecular dynamics (ReaxFF) to unravel the mechanisms of hBN synthesis from liquid nickel solvent over time scales up to 30 ns. These simulations mimic experimental conditions by including relatively large liquid nickel slabs containing dissolved boron and a molecular nitrogen gas phase. Overall, the reaction takes place almost exclusively on the surface of the liquid nickel, owing to the low solubility of nitrogen in bulk nickel and the intermediate species’ preference for the metal–gas interface. The formation of hBN invariably begins by reaction of dinitrogen with nickel-solvated boron atoms at the surface, forming intermediate N–N–B species, which typically evolve into B–N–B units through a short-lived intermediate where a single nitrogen atom is coordinated by one nitrogen and two boron atoms. The resulting B–N–B units, in turn, coalesce with growing hBN nuclei and carry nitrogen between hBN nanocrystals in an Ostwald ripening process. The amount of hBN produced on the tens of nanosecond time scale depends critically on the boron concentration, while having a much weaker dependence on the N 2 pressure for the regime considered (N 2 pressures of 2.5–10 MPa, Ni–B solutions with 6–12% boron by atom fraction). The highest rate of hBN formation occurs at the lowest temperature considered (1750 K, just above the melting point of nickel), while no hBN sheets are formed at 2000 K or above. An analysis of the transition pathways for nitrogen atoms shows that the final step, incorporation of small B–N motifs into larger hBN sheets, is the rate-limiting step in the regimes considered. While raising the temperature from 1750 to 2000 K has little effect on the formation of intermediates (N–N–B, B–N–B, etc.), the lack of large hBN sheets at temperatures >1900 K is explained by decreased probability of the final step and increased probability of breakup of hBN into B–N motifs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Highly Active Hydrogen Evolution Reaction (HER) Catalysts Formed by Energetic Pt n Cluster Deposition: Deposition Dynamics and the HER Mechanism

Mass-selected Pt n + (n ≤ 7) were deposited at variable energies on highly oriented pyrolytic graphite (HOPG), creating highly active hydrogen evolution reaction (HER) electrocatalysts. HER mass activities were ~2 to >10 times higher than those for the surface atoms in bulk Pt and for Pt n deposited on several other supports. Thus, high activity reflects the Pt-C structures formed by energetic Pt n -HOPG impacts, in addition to high Pt surface availability. The Pt n /HOPG electrodes were probed by X-ray photoelectron spectroscopy, low energy ion scattering, and electron microscopy. Born-Oppenheimer molecular dynamics (BOMD) was used to simulate Pt n - HOPG impacts, revealing the types of structures formed at different energies, then DFT was used to probe their most important HER pathways. For low deposition energies, the Pt n deposit onto the HOPG surface with sub-unit sticking probability, aggregating at defects. With increasing deposition energy, the sticking probability initially decreases, then rises to unity as subplantation and defect creation allow formation of strongly bonded platinum-carbon structures. Barriers for HER on these structures were found to be low and weakly dependent on Pt n size, consistent with experiment. The activities were highest for small covalently-bonded Pt-C structures created at high deposition energies. The larger aggregated structures formed at low energies were less active, but still substantially better than the bulk Pt surface monolayer. The catalysts were stable in repeated potential cycling at reducing potentials, but electrodes containing subplanted Pt became more active when scanned to oxidizing potentials, due to emergence of subplanted Pt onto the surface.

08 HYDROGEN↗

DOE OSTI2024

The Changing Influence of Precipitation on Soil Moisture Drought With Warming in the Mediterranean and Western North America

Abstract Anthropogenic climate change has already affected drought severity and risk across many regions, and climate models project additional increases in drought risk with future warming. Historically, droughts are typically caused by periods of below‐normal precipitation and terminated by average or above‐normal precipitation. In many regions, however, soil moisture is projected to decrease primarily through warming‐driven increases in evaporative demand, potentially affecting the ability of negative precipitation anomalies to cause drought and positive precipitation anomalies to terminate drought. Here, we use climate model simulations from Phase Six of the Coupled Model Intercomparison Project (CMIP6) to investigate how different levels of warming (1, 2, and 3°C) affect the influence of precipitation on soil moisture drought in the Mediterranean and Western North America regions. We demonstrate that the same monthly precipitation deficits (25th percentile relative to a preindustrial baseline) at a global warming level of 2°C increase the probability of both surface and rootzone soil moisture drought by 29% in the Mediterranean and 32% and 6% in Western North America compared to the preindustrial baseline. Furthermore, the probability of a dry (25th percentile relative to a preindustrial baseline) surface soil moisture month given a high (75th percentile relative to a preindustrial baseline) precipitation month is 6 (Mediterranean) and 3 (Western North America) times more likely in a 2°C world compared to the preindustrial baseline. For these regions, warming will likely increase the risk of soil moisture drought during low precipitation periods while simultaneously reducing the efficacy of high precipitation periods to terminate droughts.

Environmental Sciences & Ecology↗

DOE OSTI2024

Urban Effect on Precipitation and Deep Convective Systems Over Dallas‐Fort Worth

A range of multi-year observational data sets are used to characterize the hydroclimate of the Dallas Fort-Worth area (DFW) and to investigate the impact of urban land cover on daily accumulated precipitation, RADAR composite reflectivity (cREF), and cloud top height (CTH) during the warm season. Analyses of observational data indicate rainfall rates (RR) in a 45° annulus sector 50–100 km downwind of the city are enhanced relative to an upwind area of comparable size. Enhancement of mean precipitation intensity in this annulus sector is not observed on days with spatially averaged RR > 6 mm/day. Under some flow directions, the probability of cREF >30 dBZ, occurrence of hail, and the probability of CTH >10,000 geopotential meters are also enhanced up to 200 km downwind of DFW. Two deep convection events that passed over DFW are simulated with the Weather Research and Forecasting model using a range of microphysical schemes and evaluated using RADAR observations. Model configurations that exhibit the highest fidelity in these control simulations are used in a series of perturbation experiments where the areal extent of the city is varied between zero (replacement with grassland) and eight times its current size. These perturbation experiments indicate a non-linear response of Mesoscale Convective System properties to the urban areal extent and a very strong sensitivity to the microphysical scheme used. The impact on precipitation from the urban area, even when it is expanded to eight-times the current extent, is much less marked for deep convection with stronger synoptic forcing.

58 GEOSCIENCES↗

DOE OSTI2024

Meteorological Drivers of North American Monsoon Extreme Precipitation Events

Abstract In this paper the meteorological drivers of North American Monsoon (NAM) extreme precipitation events (EPEs) are identified and analyzed. First, the NAM area and its subregions are distinguished using self‐organizing maps applied to the Climate Prediction Center global precipitation data set. This reveals distinct subregions, shaped by the inhomogeneous geographic features of the NAM area, with distinct extreme precipitation character and drivers. Next, defining EPEs as days when subregion‐mean precipitation exceeds the 95th percentile of rainy days, five synoptic features and one mesoscale feature are investigated as potential drivers of EPEs. Essentially all EPEs can be associated with at least one selected driver, with only one event remaining unclassified. This analysis shows the dominant role of Gulf of California moisture surges, mesoscale convective systems and frontal systems in generating NAM extreme precipitation. Finally, a frequency and probability analysis is conducted to contrast precipitation distributions conditioned on the associated meteorological drivers. The findings demonstrate that the co‐occurrence of multiple features does not necessarily enhance the EPE probability.

Meteorology & Atmospheric Sciences↗

DOE OSTI2025

A Probabilistic Model for Global EMIC Wave Activity Using Van Allen Probes Observations

Electromagnetic ion cyclotron (EMIC) waves play a key role in radiation belt dynamics through resonant interactions. However, their low occurrence probability, high variability, and spatial intermittency pose challenges for accurate modeling. In this study, we present a machine learning (ML)-based global EMIC wave model built on the entire data set from the Van Allen Probes mission. To capture the distinct statistical characteristics of wave occurrence and amplitude, the model is separated into two modules: an occurrence model trained using ML techniques, and a wave amplitude model sampled from observed probability distributions. The input parameters are limited to real-time or predictable variables to ensure practical applicability. Our model shows strong performance across the entire test set and demonstrates improved predictive capability over a baseline random occurrence model, particularly during quiet geomagnetic conditions. Evaluation during both quiet and active periods confirms the model's ability to represent the clustered and intermittent nature of EMIC wave activity. Furthermore, the model provides global estimates of wave power, enabling integration with radiation belt electron data and showing signatures consistent with wave-induced scattering. We found a good correlation between the global wave activity from the model and relativistic electron observation by Van Allen Probes, regardless of the availability of in situ wave observations. The modular structure of the model also allows for straightforward expansion for additional wave properties, such as wave frequency, which can be modeled independently. This flexible, event-sensitive approach offers a promising framework for data-driven radiation belt simulations and space weather applications.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

Role of Intersections in Fracture Connectivity

Networks of intersecting fractures often provide the flow paths through subsurface reservoirs. Assessing network connectivity is challenging because fracture intersections compose a vanishingly small fraction of the network void volume. In this paper, motivated by 3D X-ray imaging of the simplest element of fracture network, that is, two orthogonal fractures, we perform a percolation and finite-size scaling analysis to study the connectivity provided by fracture intersections. The conditions when an intersection enhances connectivity across a sample depend on spatial correlations in the fracture aperture distributions, on the stress state, and on the direction of flow. Here we consider three flow directions: (a) across intersections, (b) parallel to intersections and (c) around corners. For (a), intersections provide minimal enhancement of connectivity because they contribute little additional void area. For (b), intersections increase the probability of a connected path near threshold by enabling 3D connected pathways that are not possible in parallel fractures. Flow around corners, (c), is fundamentally the result of the intersection connecting two fractures in series and spatial correlations are broken around corners, suppressing the connectivity relative to (a). When the connected fractures are stressed equally, a joint percolation threshold emerges that continues to have scale invariance. However, when the fractures are stressed unequally, the system has mixed percolation without clearly defined percolation thresholds. In all cases, percolation probabilities are found to be scale dependent which has important consequences for the connectivity of larger fracture networks composed of the fundamental element studied here.

02 PETROLEUM↗

DOE OSTI2025

Functional protein mining with conformal guarantees

Molecular structure prediction and homology detection offer promising paths to discovering protein function and evolutionary relationships. However, current approaches lack statistical reliability assurances, limiting their practical utility for selecting proteins for further experimental and in-silico characterization. To address this challenge, we introduce a statistically principled approach to protein search leveraging principles from conformal prediction, offering a framework that ensures statistical guarantees with user-specified risk and provides calibrated probabilities (rather than raw ML scores) for any protein search model. Our method (1) lets users select many biologically-relevant loss metrics (i.e. false discovery rate) and assigns reliable functional probabilities for annotating genes of unknown function; (2) achieves state-of-the-art performance in enzyme classification without training new models; and (3) robustly and rapidly pre-filters proteins for computationally intensive structural alignment algorithms. Our framework enhances the reliability of protein homology detection and enables the discovery of uncharacterized proteins with likely desirable functional properties.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Ferroelectrically switched valley-dependent transmission in SnTe-PbTe-SnTe monolayer lateral heterostructures

A special class of valleytronic two-dimensional (2D) semiconductors possesses carrier pockets (i.e., valleys) along certain directions in the first Brillouin zone, which can be applied as a new degree of freedom for information storage and processing. Here we show that members of this family that are ferroelectric allow the location of these valleys to be switched by rotating the ferroelectric polarization. This makes possible the control of electronic state transmission probability through an energy barrier by ferroelectrically switching the polarization direction, thereby creating or eliminating valley matching in reciprocal space. We apply molecular beam epitaxy to grow lateral sandwich heterostructures with monolayer-thick ferroelectric SnTe separated by nanometer-wide paraelectric PbTe as the barriers. Using scanning tunneling microscopy, we show that the transmission probability of the 2D hole states at the valence band maximum of SnTe monolayer strongly relies on the relative orientation between the polarization directions of the two SnTe electrodes. The transmission can be switched from a suppressed state to a permitted state by rotating the ferroelectric polarization of one SnTe electrode by 90 degrees. Our work demonstrates the electric-field-control of valley locations and its potential for tunnel junction valleytronic devices.

electronic devices↗

DOE OSTI2024

Fast pseudorandom quantum state generators via inflationary quantum gates

Abstract We propose a mechanism for reaching pseudorandom quantum states, computationally indistinguishable from Haar random, with shallow log-ndepth quantum circuits, wherenis the number of qudits. We argue that$$\log n$$ log n depth 2-qubit-gate-based generic random quantum circuits that are claimed to provide a lower bound on the speed of information scrambling, cannot produce computationally pseudorandom quantum states. This conclusion is connected with the presence of polynomial (inn) tails in the stay probability of short Pauli strings that survive evolution through such shallow circuits. We show, however, that stay-probability-tails can be eliminated and pseudorandom quantum states can be accomplished with shallow$$\log n$$ log n depth circuits built from a special universal family of “inflationary” quantum (IQ) gates. We prove that IQ-gates cannot be implemented with 2-qubit gates, but can be realized either as a subset of 2-qudit-gates inU(d 2 ) withd ≥ 3 anddprime, or as special 3-qubit gates.

Physics↗

DOE OSTI2025

Electrochemical loading enhances deuterium fusion rates in a metal target

Nuclear fusion research for energy applications aims to create conditions that release more energy than required to initiate the fusion process1. To generate meaningful amounts of energy, fuels such as deuterium need to be spatially confined to increase the collision probability of particles2, 3–4. We therefore set out to investigate whether electrochemically loading a metal lattice with deuterium fuel could increase the probability of nuclear fusion events. Here we report a benchtop fusion reactor that enabled us to bombard a palladium metal target with deuterium ions. These deuterium ions undergo deuterium–deuterium fusion reactions within the palladium metal. We showed that the in situ electrochemical loading of deuterium into the palladium target resulted in a 15(2)% increase in deuterium–deuterium fusion rates. This experiment shows how the electrochemical loading of a metal target at the electronvolt energy scale can affect nuclear reactions at the megaelectronvolt energy scale.

Chen, Kuo-Yi↗

DOE OSTI2026

Direct observation of the superallowed α-decay of 104 Te

The radioactivity of the α particle is among the most compelling evidence for the existence of cluster structures in atomic nuclei. During the decay process, a pre-existing α particle tunnels through the potential barrier formed by the residual nucleus1,2. The degree of preformation of the α particle, a strongly bound system of two protons and two neutrons, is extracted from the data by dividing the α-decay probability by the barrier penetrability for a given particle energy. The preformation probability changes rapidly near nuclear shell closures, which is direct evidence that clustering is connected to nuclear structure3. Enhanced preformation was observed in the lightest α-particle emitters, spherical tellurium and xenon isotopes decaying to magic isotopes of tin. Here we show the most extreme case of α-particle preformation from the measurement of the decay of tellurium-104 (104Te). With a half-life of , 104Te is the fastest ground-state α-emitting nucleus known so far. The deduced preformation demonstrates that the enhancement is greater for 104Te than for any other nucleus. One nuclear model that can explain our observation postulates that the α particle can exist only in the low-nuclear-matter-density regions on the surface of the nucleus. The uniquely high preformation for 104Te is attributed to its relation to doubly magic tin-100 (100Sn), creating conditions conducive to form an α particle.

Cox, Ian↗

DOE OSTI2024

Innovative dead-time correction and background subtraction for neutron multiplicity measurements using neural networks

Abstract The number of neutrons emitted from a nuclear reaction plays a crucial role in various fields, including nuclear theory, nuclear nonproliferation, nuclear energy and nuclear criticality safety. Accurate determination of neutron multiplicities requires the application of several corrections, with dead-time correction and background subtraction being particularly significant. These corrections become more challenging for neutron detectors with time-dependent neutron capture. In this work, we perform a comprehensive study of three existing methods used for dead-time correction and background subtraction in neutron detectors with time-dependent neutron capture. The methods were tested for dead-times in the range from 0 to 1 μs using a Monte Carlo model simulating the dead-time and background effects in the standard neutron multiplicity probability distribution of $$^{252}$$ 252 Cf. The previous methods showed larger than desired uncertainty or systematic trade off. Those uncertainties prompted the development of a novel approach using neural networks trained with data from Monte Carlo simulations. The Neural Network method enabled the correction of neutron multiplicity probabilities more accurately than the other methods with fractional errors smaller than 3% for multiplicities around the peak of $$^{252}$$ 252 Cf. A similar approach using neural networks could be applied to problems where the system being studied can be accurately simulated without having an accurate analytical description available. The neural network method presented in this paper can be easily expanded if multiplicities greater than 10 are expected.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

Complexity-calibrated benchmarks for machine learning reveal when prediction algorithms succeed and mislead

Abstract Recurrent neural networks are used to forecast time series in finance, climate, language, and from many other domains. Reservoir computers are a particularly easily trainable form of recurrent neural network. Recently, a “next-generation” reservoir computer was introduced in which the memory trace involves only a finite number of previous symbols. We explore the inherent limitations of finite-past memory traces in this intriguing proposal. A lower bound from Fano’s inequality shows that, on highly non-Markovian processes generated by large probabilistic state machines, next-generation reservoir computers with reasonably long memory traces have an error probability that is at least $$\sim 60\%$$ ∼ 60 % higher than the minimal attainable error probability in predicting the next observation. More generally, it appears that popular recurrent neural networks fall far short of optimally predicting such complex processes. These results highlight the need for a new generation of optimized recurrent neural network architectures. Alongside this finding, we present concentration-of-measure results for randomly-generated but complex processes. One conclusion is that large probabilistic state machines—specifically, large $$\epsilon$$ ϵ -machines—are key to generating challenging and structurally-unbiased stimuli for ground-truthing recurrent neural network architectures.

97 MATHEMATICS AND COMPUTING↗