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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

AI-driven multiscale simulations illuminate mechanisms of SARS-CoV-2 spike dynamics

We develop a generalizable AI-driven workflow that leverages heterogeneous HPC resources to explore the time-dependent dynamics of molecular systems. We use this workflow to investigate the mechanisms of infectivity of the SARS-CoV-2 spike protein, the main viral infection machinery. Our workflow enables more efficient investigation of spike dynamics in a variety of complex environments, including within a complete SARS-CoV-2 viral envelope simulation, which contains 305 million atoms and shows strong scaling on ORNL Summit using NAMD. We present several novel scientific discoveries, including the elucidation of the spike’s full glycan shield, the role of spike glycans in modulating the infectivity of the virus, and the characterization of the flexible interactions between the spike and the human ACE2 receptor. We also demonstrate how AI can accelerate conformational sampling across different systems and pave the way for the future application of such methods to additional studies in SARS-CoV-2 and other molecular systems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Towards a Robust Adaptive Digital Twin for Fusion Applications

The development of a digital twin system for fusion applications is essential for enhancing the prediction, analysis, and optimization of complex plasma processes. Machine learning (ML), particularly deep learning has demonstrated strong capabilities in modeling such highly nonlinear and intricate systems. However, two critical challenges limit the deployment of deep learning-based digital twins: Uncertainty Quantification (UQ) and data drift. UQ is vital for ensuring trustworthy predictions, especially in decision-support scenarios. Additionally, data-driven models are often sensitive to changes in the underlying data distribution, such as shot-to-shot variations in fusion experiments, which can lead to performance degradation over time. To address these challenges, we are developing an uncertainty-aware, adaptive digital twin framework. Our approach incorporates deep learning models enhanced with Gaussian Process approximations for predictive uncertainty estimation, coupled with an online learning mechanism that enables continuous model adaptation to new experimental data. This adaptive capability allows the data driven models to respond effectively to evolving plasma behaviors and equipment conditions. Specifically, to mitigate the effects of shot-to-shot drift, our system updates itself incrementally as new data becomes available, improving both robustness and fidelity. Our vision is to evolve this data driven model into a self-sustaining digital twin system that leverages UQ based feedback to continuously refine itself and potentially support real-time decision making. This presentation will cover a brief background on uncertainty quantification for ML, our ongoing effort on development of UQ capabilities for ML, our data science pipeline from data collection to model development and analysis and online learning framework for modeling coil deflection at DIII-D. I will also briefly touch upon opportunities and challenges in development of digital twin framework.

Sammuli, Brian [General Atomics]↗
DOE OSTI2021

Metal Chalcogenides for Neuromorphic Computing: Emerging Materials and Mechanisms

The approaching end of Moore's Law scaling has significantly accelerated multiple fields of research including neuromorphic-, quantum-, and photonic computing, each of which possesses unique benefits unobtained through conventional binary computers. One of the most compelling arguments for neuromorphic computing systems is power consumption, noting that computations made in the human brain are approximately 106 times more efficient than conventional CMOS logic. This review article focuses on the materials science and physical mechanisms found in metal chalcogenides that are currently being explored for use in neuromorphic applications. We begin by reviewing the key biological signal generation and transduction mechanisms within neuronal components of mammalian brains and subsequently compare with observed experimental measurements in chalcogenides. With robustness and energy efficiency in mind, we will focus on short-range mechanisms such as structural phase changes and correlated electron systems that can be driven by low-energy stimuli, such as temperature or electric field. We aim to highlight fundamental materials research and existing gaps that need to be overcome to enable further integration or advancement of metal chalcogenides for neuromorphic systems.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Design of a Continuous Pellet Fueling System for Wendelstein 7-X

A continuous pellet fueling system (CPFS) is currently being designed at the Oak Ridge National Laboratory (ORNL) for the long pulse operation of the Wendelstein 7-X (W7-X) stellarator. The purpose of the CPFS is to provide deep continuous fueling for feedback-controlled high-density operation and mitigation of predicted hollow density profiles. As described here, the system will provide the capability to inject cylindrical pellets of solid hydrogen or deuterium into the plasma core, with flexibility to vary the pellet size, velocity, and injection frequency. Pellets are nominally of 3 mm in diameter and have a length between 1 and 4 mm. The heart of the CPFS is a vertically oriented, twin-screw extruder, cooled by three Gifford-McMahon cryocoolers in parallel, designed to form a continuous filament of hydrogen or deuterium. The filament width, which determines the pellet length, can be adjusted by means of a variable nozzle driven by a linear actuator at the base of the extruder. Coupled to the nozzle is a solenoid-operated gas gun and cutter assembly. A pneumatic propellant valve pulses a burst of ~60 bar helium to accelerate the cut pellet into W7-X. Three gaps in the guide tubes provide pumping locations to remove the helium propellant before it reaches the plasma. The maximum velocity of the pellet is limited by its ability to survive navigating the guide tube trajectory intact. A microwave cavity located within the guide tube provides the capability to measure the pellet size and velocity. The mechanical and thermal designs of the W7-X extruder, adjustable nozzle, and gun and cutter assembly design are described. A guide tube design and experimental pellet survivability test results are presented.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Data driven drift correction for complex optical systems

To exploit the thousand-fold increase in spectral brightness of modern light sources, increasingly intricate experiments are being conducted that demand extremely precise beam trajectory. Maintaining the optimal trajectory over several hours of an experiment with the needed precision necessitates active drift control. Here, we outline time varying Bayesian optimization (TVBO) as a data driven approach for robust drift correction, and illustrate its application for a split and delay optical system composed of six crystals and twelve input dimensions. Using numerical simulations, we exhibit the application of TVBO for linear drift, non-smooth temporal drift as well as constrained TVBO for multi-objective control settings, representing real-life operating conditions. This approach can be easily adapted to other X-ray beam conditioning and guidance systems, including multi-crystal monochromators and grazing-incidence mirrors, to maintain sub-micrometer and nanoradian beam stability over the course of an experiment spanning several hours.

Bayesian optimization↗
DOE OSTI2026

Automated and High-Throughput Phase Separation Control for Supramolecular Polymer Blends Enabled by Machine Learning

Supramolecular polymer blends (SPBs) offer tunable morphologies that dictate their macroscopic properties, yet their rational design is limited by the absence of predictive structure−morphology models. Here, we introduce a data-driven highthroughput workflow that integrates modular polymer synthesis, robotic formulation, automated morphology characterization, and machine learning (ML) for accelerated SPB discovery. Using a plug-and-play synthetic strategy, 33 hydrogen-bonding endfunctional homopolymers were prepared and orthogonally combined to generate 260 SPBs in 1 day. A fully automated atomic force microscopy (AFM) pipeline enabled systematic imaging, producing 2340 morphology data sets with minimal human intervention. Domain spacings were extracted through complementary imageprocessing methods and used to train ML models. A support vector regression (SVR) model accurately predicted target phase-separation sizes (50, 100, and 150 nm), which were experimentally validated. This work demonstrates the power of coupling high-throughput experimentation with ML to accelerate morphology discovery and provides one of the first large-scale experimental data sets for supramolecular polymer systems.

ML-guided polymer design↗
DOE OSTI2016

Analytical Solution to the Pneumatic Transient Rod System at ACRR

The ACRR pulse is pneumatically driven by nitrogen in a system of pipes, valves and hoses up to the connection of the pneumatic system and mechanical linkages of the transient rod (TR). The main components of the TR pneumatic system are the regulator, accumulator, solenoid valve and piston-cylinder assembly. The purpose of this analysis is to analyze the flow of nitrogen through the TR pneumatic system in order to develop a motion profile of the piston during the pulse and be able to predict the pressure distributions inside both the cylinder and accumulators. The predicted pressure distributions will be validated against pressure transducer data, while the motion profile will be compared to proximity switch data. By predicting the motion of the piston, pulse timing will be determined and provided to the engineers/operators for verification. The motion profile will provide an acceleration distribution to be used in Razorback to more accurately predict reactivity insertion into the system.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Attack-resilient algorithms and testbed federation for wide-area protection and control in smart grid

Today’s energy infrastructure is undergoing a massive transformation across all generation, transmission, and distribution systems to provide reliability, efficiency, and sustainability to the power system network. During the past 10 years, there has been a remarkable enhancement in developing close-loop wide-area protection and control (WAPAC) applications, such as automatic generation control (AGC), wide-area protection scheme (WAPS), synchrophasor-based wide-area voltage control system (WAVCS), etc., in the energy management system (EMS) to provide an adequate situational awareness to control center operators during physical disturbances and natural events. The rapid digitalization, unencrypted communication, and numerous data-sharing devices have increased digital access points that have exposed the grid network to countless cyberattacks pertinent to the WAPAC cybersecurity. Given the amount of complex grid network coupled with legacy infrastructures, it is untrustworthy to completely rely on information technology (IT)-based conventional security measures to defend against evolving cyberattacks. Hence, there is a compelling need to go beyond the traditional paradigm of ‘security by obscurity’ and ‘bolt-on’ security measures, and develop a suite of innovative security solutions at multiple layers to transform the current “fault-resilient” grid into “fault and attack-resilient” grid of the future. This dissertation specifically focuses on developing application-specific attack-resilient solutions for critical WAPAC applications in the smart grid. The first part of the study, Attack-Resilient Wide-Area Protection Scheme, presents a cyber-physical attack-resilient system (CPARS) for WAPS, where two anomaly detection methods are proposed to detect multi-level cyberattacks followed by the rules-based intrusion mitigation system (RIMS) to initiate effective mitigation strategies, tailored to these attacks, to restore the normal grid operation after disturbances. The first anomaly detection component, Machine learning-based Anomaly Detection for Centralized WAPS, discusses a novel architecture and methodology for developing a machine-learning-based anomaly detection system (ADS) for the centralized wide-area protection system (CWAPS) by applying the variational mode decomposition (VMD) technique and decision tree (DT) algorithms using cyber logs and synchrophasor measurements. The second component, Multi-Agent-based Anomaly Detection for Decentralized WAPS, presents a two-level hierarchical multi-agent-based decentralized WAPS against the system-aware stealthy cyber-attacks. Finally, for the RIMS, the state transition diagram-based mitigation measures are defined based on the grid operation states and experimental testing and evaluation are presented in a cyber-physical testbed environment. The second part of the study, Attack-Resilient Wide-Area Voltage Control System, addresses the existing vulnerabilities in the wide-area voltage control system (WAVCS), presents the methodology for performing an impact analysis during data integrity attacks, and proposes a data-driven attackresilient system (DARS) by incorporating machine learning-based ADS and rules-based anomaly mitigation system (RAMS) to detect these attacks and provide necessary mitigation actions to maintain the transient voltage stability after disturbances. Finally, the third part, Networked Federation Testbed (NEFTSec) for Smart Grid Cybersecurity, focuses on developing an interconnected federated testbed through a common network like the internet, describes its conceptual architectures, and also presents its design components for experimental testing, validation, and evaluation. Further, it presents the co-simulation interface algorithm (CIA) to facilitate the geographically-dispersed real-time distributed simulation (GD-RTDS) during the cyber-physical federation. In general, this dissertation presents innovative attack-resilient solutions for WAPAC applications that are experimentally tested and evaluated using the PowerCyber testbed and NEFTSec platform to accelerate the transition of state-of-the-art research works to the real-field deployments.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1994

Use of rotation to suppress thermosolutal convection in directionally solidified binary alloys

Effects of rotation on onset of convection during plane-front directional solidification of Pb-Sn and the pseudobinary system mercury cadmium telluride (Hg(1-x)Cd(x)Te), and on dendritic solidification of Pb-Sn have been studied by means of linear stability analysis. Incorporating Coriolis and centrifugal accelerations into the momentum equation of Coriell et al., we find that under realistic processing conditions, a large degree of stabilization can be achieved using modest rotation rates for both Pb-Sn and mercury cadmium telluride. At a growth velocity of 5 micron/sec and nominal liquid-side temperature gradient of 200 K/cm in Pb-Sn, rotation at 500 rpm results in a hundredfold increase in the critical Sn concentration. Large increases in the maximum allowable growth velocity at fixed melt composition are also attainable with modest rotation rates. The effect is amplified under conditions of reduced gravitational acceleration. For Hg(1-x)Cd(x)Te, we have also studied the nonrotating case. The key differences are due to the existence of a composition range for Hg(1-x)Cd(x)Te in which the melt density has a local maximum as a function of temperature. When the melt solidifies by cooling from below, the liquid density may initially increase with distance above the interface, before ultimately decreasing as the melt temperature increases above the value at which the local density maximum occurs. In contrast to the Pb-Sn case where density depends monotonically on temperature and composition, for Hg(1-x)Cd(x)Te there exists a critical value of the growth velocity above which plane-front solidification is unstable for all bulk CdTe mole fractions. Again, rotation leads to significant inhibition of onset. We identify the predicted stabilization with the Taylor-Proudman mechanism by which rotation inhibits thermal convection in a single-component fluid heated from below. In a binary liquid undergoing solidification, rotation inhibits the onset of buoyancy-driven convection, and has no effect on the short-wavelength morphological instability. At large growth velocities, the plane-front interface between liquid and solid becomes unstable with respect to a morphological instability and solidification occurs dendritically, with a mushy zone of dendrites and interdendritic fluid separating the solid from the melt. For the Pb-Sn system, rotation substantially suppresses the onset of convection in the mushy zone and in the overlying liquid, holding open the promise that rotation can suppress freckling and other macrosegregation defects.

Pearlstein, Arne J.↗
DOE OSTI2026

Mechanistic Understanding of Interphase-driven Aging in Silicon Anodes

Conventional solid electrolyte interphases (SEIs) strongly adhere to micro-silicon (µ-Si) and crack under volume changes, causing poor cycling performance. Nano-silicon improves cycling performance but remains costly with limited calendar life. Here potentiostatic ageing tests demonstrate that both calendar and cycle ageing are governed by SEI cracking and dissolution with different relative contributions. When the system is not dominated by SEI dissolution, the relative calendar life of Si anodes could correlates positively with their cycle life. LiF-rich SEI that enables long cycle life in µ-Si is therefore expected to enhance calendar life as well. Using this framework, we screened electrolytes, SEIs and electrodes and validated them with full-cell storage. LiF-rich SEI minimizes cracking and dissolution, enabling μ-Si to achieve excellent calendar life, whereas nano-silicon suffers from SEI dissolution and needs reduced electrolyte–electrode contact for better calendar life. This work clarifies calendar-ageing behaviour and accelerates electrolytes and SEI development for long-life Si anodes.

Johnson, Christopher S.↗
DOE OSTI2026

Plant‐Induced Changes Mediate Belowground Carbon Cycling in an Experimentally Warmed Peatland

Warming and elevated atmospheric CO 2 profoundly impact peatland ecosystems, particularly through changes in plant species composition. Plants regulate the initial input of organic compounds to peatland belowground systems, controlling the availability of electron donors and electron acceptors that fuel microbially mediated organic matter decomposition to CO 2 and CH 4 . However, explicit links between porewater CO 2 and CH 4 dynamics and plant-derived chemical compounds remain relatively undefined. In a whole ecosystem warming experiment, we investigated how warming affects plant leaf chemical composition and species assemblages, and how the alteration of leaf-derived organic compounds supplied to the subsurface impacts belowground CO 2 and CH 4 production. While earlier studies at our site found no temperature-dependent changes in CH 4 production pathways, our extended timeseries has revealed increased acetoclastic methanogenesis at higher temperatures in certain peat depths, correlated with elevated porewater phenolics. These changes appear driven by the observed increased plant productivity and altered vegetation inputs, which accelerate decomposition and fuel CH 4 production through enhanced substrate availability. In conclusion, we observed warming-induced changes in molecular composition both between and within plant species, suggesting that plant-mediated controls on belowground carbon processing are more complex than previously recognized.

Wilson, Rachel M. [Florida State Univ., Tallahasse↗
DOE OSTI2024

Modeling of Macroscopic Dynamics in Three-Dimensional Plasma Configurations (Final Technical Report)

A numerical simulation-based study to investigate the stability properties of inherently three-dimensional plasma magnetic configurations is presented. These configurations, called stellarators and heliotrons, use external helical coils to provide the twisting magnetic field that is required to confine ionized plasma particles. In contrast to the tokamak configuration, which nominally has the property of axisymmetry, electrical current does not need to be induced in stellarator and heliotron plasma. Experiments have shown that these three-dimensional configurations are remarkably robust to macroscopic instability, even when driven to relatively high levels of plasma pressure. Our understanding of how the plasma is able to withstand macroscopic instability will be enhanced by the work proposed here. The effort will generalize existing numerical modeling capability, and it will apply the new capability to model macroscopic dynamics in these three-dimensional configurations. The development work has been applied to the NIMROD simulation code (https://nimrodteam.org) that was originally based on axisymmetric problem domains. It has been generalized to accommodate the three-dimensional shaping of the plasma volume in stellarators and heliotrons. Its use of equilibrium data has also been generalized, and the implementation allows different numerical representation of the magnetic field. Analysis has been performed to inform the selection of numerical methods, and a method to accelerate the iterative solution of the necessary algebraic systems has been developed and implemented. Verification of the new NIMSTELL code on benchmark problems has also been an important part of the effort.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Results and lessons learned from accelerating radio frequency modeling using machine learning [slides]

The “advanced tokamak” reactor concept is a leading candidate for a steady state fusion pilot plant. An advanced tokamak (AT) sustains a majority of the required plasma current with effects resulting from maintenance of the peaked pressure at the device center. This current is augmented by auxiliary current drive sources. These auxiliary actuators may consist of neutral particle beams and/or radio frequency (RF) systems such as lower hybrid current drive (LHCD) and high harmonic fast wave (HHFW) current drive using radio and microwaves from antennas. The primary focus of this work is to develop models of RF current profile control suitable for use in integrated modeling frameworks and for real-time control in experiments. Direct physics models of RF current drive can be computationally intensive. In order to achieve predictive times appropriate for the thousands of calls needed in real-time control of experiments and for use in integrated models, we will apply modern machine learning (ML) techniques to accelerate these models and interpolate their results. To generate the fast and accurate models for use in control level algorithms and integrated modeling we need to replace present models with high dimensional interpolation of their results. We will perform additional simulations across a broader parameter range for EAST and other tokamaks in different physics regimes (Alcator C-Mod, DIII-D, WEST, CFETR, ARC, ITER) and combine them into a larger database for training and testing of the ML models. Further testing of the control level models with experimental current profile data from EAST and C-Mod tokamaks will provide additional confirmation of the control level model before integration in a tokamak control system or integrated modeling suite. ML will be used to optimize the selection of training data consisting of RF current driven at different values of density profile, temperature profile, plasma current, and wavenumber. ML will also be used to facilitate classification of current drive from these input data. The output of this effort will be a validated classifier capable of determining the current drive profiles for HHFW CD and LHCD on a mille-second timescale. This will provide a breakthrough capability enabling real-time control of RF driven current profiles in experiments including ITER ICRF and use integrated modeling frameworks requiring thousands of current profile calculations in discharge simulations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2011

Comparison of accelerator technologies for use in ADSS

Accelerator Driven Subcritical (ADS) fission is an interesting candidate basis for nuclear waste transmutation and for nuclear power generation. ADS can use either thorium or depleted uranium as fuel, operate below criticality, and consume rather than produce long-lived actinides. A case study with a hypothetical, but realistic nuclear core configuration is used to evaluate the performance requirements of the driver proton accelerator in terms of beam energy, beam current, duty factor, beam distribution delivered to the fission core, reliability, and capital and operating cost. Comparison between a CW IC and that of a SRF proton linac is evaluated. Future accelerator R&D required to improve each candidate accelerator design is discussed. ADS fission has interesting potential for electric power generation and also for destruction of long-lived actinide waste produced by conventional critical reactors. ADS systems offer several interesting advantages in comparison to critical reactors: (1) ADS provides greater flexibility for the composition and placement of fissile, fertile, or fission product waste within the core, and require less enrichment of fissile content; (2) The core can be operated with a reactivity k{sub eff} that cannot reach criticality by any failure mode; (3) When the beam is shut off fission ceases in the core; (4) Coupling the fast neutron spectrum of the spallation drive to fast core neutronics offers a basis for more complete burning of long-lived actinides; and (5) ADS designs can provide sufficient thermal mass that meltdown cannot occur from radioactive heat after fission is stopped. In order to drive a {approx}GW{sub e} fission core a CW proton beam of >700 MeV and {approx}15 MW beam power is required. A previous study of the accelerator performance required for ADS systems concluded that present accelerator performance is approaching those requirements, but accelerator system cost and reliability remain particular concerns. The obvious candidates for accelerators that can provide intense CW proton beams are isochronous cyclotrons (IC) and superconducting linacs. We have examined a case study using a hypothetical ADS core configuration to guide our thinking in evaluating those two accelerator technologies for use in ADS systems. Issues of accelerator power, multiplicity of accelerators, and options for core neutronics and fuel form are discussed.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

A Simulation and Optimization Framework for Managing Wind-Driven Loading on PV Systems

As PV modules continue to trend toward larger, thinner, and more flexible forms they grow more susceptible to damage from dynamic wind loading. As a result, understanding the impact of wind on PV systems, particularly when mounted on solar-tracking hardware, and identifying robust, stable array layouts and stow strategies is becoming increasingly important for the PV community. We are developing an open-source software package, PVade (PV aerodynamic design engineering), to simulate the cascading fluid-structure interaction that occurs within solar-tracking arrays to enable researchers to test hardware, layout, and tracker control changes, leading to enhanced stability and a reduction in wind-driven damage. We will give an overview of the PVade software and present the latest outcomes from our ongoing validation campaign in which we compare statistical structural responses with field data. From there, we will present simulated results from a larger, multi-row array and highlight the effect of varying tracker angles on stability as measured by both acceleration and deformation, focusing on the stability differences between positive and negative tilt angles.

fluid structure interaction↗
DOE OSTI2025

Unveiling X-ray absorption signatures of boron nitride via first-principles simulation and machine learning

Boron nitride (BN) allotropes hold great promise in many advanced applications ranging from optical and photonic devices to energy storage and battery systems to tribological components. The diverse functionalities of this material stem from BN’s highly tunable structural and electronic properties, which are governed by the versatile boron–nitrogen bonding configurations. Exploring the structural landscape of BN can unveil novel structures possessing unique properties suited for specific applications, therefore accelerating the design of next-generation advanced functional materials. In this work, we leverage boron K-edge X-ray absorption spectroscopy (XAS) as an effective probe for local structural features and chemical environments. A total of 210 BN crystal structures are generated via analogies to the extensive array of carbon allotropes, and XAS is simulated for each unique local motif within the resulting collection of structures. A mapping between structural features and spectral signatures was established by synergizing first-principle simulations with data-driven based post-analysis approaches. Specifically, we developed a neural network model that can satisfactorily predict spectra line shapes from local structural descriptors. Toward automatic spectroscopic interpretation of any new BN structures, supervised machine learning models, trained on this structure–spectrum dataset, can accurately infer local coordination environments from simulated XAS, highlighting the strength of this unique approach of combining high-fidelity first-principles simulation and machine-learning to accelerate target design of novel BN materials via rational understanding of local structure-spectrum correlations.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Investigations of DNA damage induction and repair resulting from cellular exposure to high dose-rate pulsed proton beams

Studies regarding the radiobiological effects of low dose radiation, microbeam irradiation services have been developed in the world and today laser acceleration of protons and heavy ions may be used in radiation therapy. The application of different facilities is essential for studying bystander effects and relating signalling phenomena in different cells or tissues. In particular the use of ion beams results advantageous in cancer radiotherapy compared to more commonly used X-rays, since the ability of ions in delivering lethal amount of doses into the target tumour avoiding or limiting damage to the contiguous healthy tissues. At the INFN-LNS in Catania, a multidisciplinary radiobiology group is strategically structured aimed to develop radiobiological research, finalised to therapeutic applications, compatible with the use of high dose laser-driven ion beams. The characteristic non-continuous dose rates with several orders of magnitude of laser-driven ion beams makes this facility very interesting in the cellular systems' response to ultra-high dose rates with non-conventional pulse time intervals cellular studies. Our group have projected to examine the effect of high dose laser-driven ion beams on two cellular types: foetal fibroblasts (normal control cells) and DU145 (prostate cancer cells), studying the modulation of some different bio-molecular parameters, in particular cell proliferation and viability, DNA damage, redox cellular status, morphological alterations of both the cytoskeleton components and some cell organelles and the possible presence of apoptotic or necrotic cell death. Our group performed preliminary experiments with high energy (60 MeV), dose rate of 10 Gy/min, doses of 1, 2, 3 Gy and LET 1 keV/μm on human foetal fibroblasts (control cells). We observed that cell viability was not influenced by the characteristics of the beam, the irradiation conditions or the analysis time. Conversely, DNA damage was present at time 0, immediately following irradiation in a dose-dependent manner. The analysis of repair capability showed that the cells irradiated with 1 and 2 Gy almost completely recovered from the damage, but not, however, 3 Gy treated cells in which DNA damage was not recovered. In addition, the results indicate the importance of the use of an appropriate control in radiobiological in vitro analysis.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

bnl/pub-Maffettone_2020_08

The application of the XCA package as first demonstrated in aXiv:2008.00283. ABSTRACT: The discovery of new structural and functional materials is driven by phase identification, often using X-ray diffraction (XRD). Automation has accelerated the rate of XRD measurements, greatly outpacing XRD analysis techniques that remain manual, time consuming, error prone, and impossible to scale. With the advent of autonomous robotic scientists or self-driving labs, contemporary techniques prohibit the integration of XRD. Here, we describe a computer program for the autonomous characterization of XRD data, driven by artificial intelligence (AI), for the discovery of new materials. Starting from structural databases, we train an ensemble model using a physically accurate synthetic dataset, which output probabilistic classifications --- rather than absolutes --- to overcome the overconfidence in traditional neural networks. This AI agent behaves as a companion to the researcher, improving accuracy and offering unprecedented time savings, and is demonstrated on a diverse set of organic and inorganic materials challenges. This innovation is directly applicable to inverse design approaches, robotic discovery systems, and can be immediately considered for other forms of characterization such as spectroscopy and the pair distribution function.

Maffettone, PhillipM [Brookhaven National Lab. (BN↗