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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Inhibition of MALT1 and BCL2 Induces Synergistic Antitumor Activity in Models of B-Cell Lymphoma

The activated B cell (ABC) subset of diffuse large B-cell lymphoma (DLBCL) is characterized by chronic B-cell receptor signaling and associated with poor outcomes when treated with standard therapy. In ABC-DLBCL, MALT1 is a core enzyme that is constitutively activated by stimulation of the B-cell receptor or gain-of-function mutations in upstream components of the signaling pathway, making it an attractive therapeutic target. We discovered a novel small-molecule inhibitor, ABBV-MALT1, that potently shuts down B-cell signaling selectively in ABC-DLBCL preclinical models leading to potent cell growth and xenograft inhibition. We also identified a rational combination partner for ABBV-MALT1 in the BCL2 inhibitor, venetoclax, which when combined significantly synergizes to elicit deep and durable responses in preclinical models. This work highlights the potential of ABBV-MALT1 monotherapy and combination with venetoclax as effective treatment options for patients with ABC-DLBCL.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Learning genetic perturbation effects with variational causal inference

Advances in sequencing technologies have enhanced the understanding of gene regulation in cells. In particular, Perturb-seq has enabled high-resolution profiling of the transcriptomic response to genetic perturbations at the single-cell level. This understanding has implications in functional genomics and potentially for identifying therapeutic targets. Various computational models have been developed to predict perturbational effects. While deep learning models excel at interpolating observed perturbational data, they tend to overfit in the lack of enough data and may not generalize well to unseen perturbations. In contrast, mechanistic models, such as linear causal models based on gene regulatory networks, hold greater potential for extrapolation, as they encapsulate regulatory information that can predict responses to unseen perturbations. However, their application has been limited to small studies due to overly simplistic assumptions, making them less effective in handling noisy, large-scale single-cell data. We propose a hybrid approach that combines a mechanistic causal model with variational deep learning, termed Single Cell Causal Variational Autoencoder (SCCVAE). The mechanistic model employs a learned regulatory network to represent perturbational changes as shift interventions that propagate through the learned network. SCCVAE integrates this mechanistic causal model into a variational autoencoder, generating rich, comprehensive transcriptomic responses. Our results indicate that SCCVAE exhibits superior performance over current state-of-the-art baselines for extrapolating to predict unseen perturbational responses. Additionally, for the observed perturbations, the latent space learned by SCCVAE allows for the identification of functional perturbation modules and simulation of single-gene knockdown experiments of varying penetrance, presenting a robust tool for interpreting and interpolating perturbational responses at the single-cell level.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Characterization of the Triassic Newark Basin of New York and New Jersey for Geologic Storage of Carbon Dioxide

The project Characterization of the Triassic Newark Basin of New York and New Jersey for Geologic Storage of Carbon Dioxide is one of 9 site characterization projects that were implemented as part of ARRA (American Recovery and Reinvestment Act). Data from this project was used to improve resolution of data in NATCARB in the area of study. Data related to this study has already been incorporated in NATCARB Atlas. Sandia Technologies, LLC, and co-investigator Conrad Geoscience Corporation, examined the potential for large-scale, permanent CO2 storage in sedimentary strata within the Newark Rift Basin. The Newark Rift Basin underlies an industrialized, developed region comprising parts of New York, New Jersey, and Pennsylvania. The project characterized and investigated the suitability of Triassic age sedimentary formations for potential geologic CO2 storage. The project team drilled and cored two test wells to define the sedimentary geologic formations underlying the basin and to document or reach basement rock. With this geologic characterization phase, an integration of seismic, geologic, borehole, and formation core results provided a higher resolution assessment of CO2 storage potential. The Stockton Formation is known to be a potentially favorable geologic storage formation in the basin. In 2011, the 1-NYSTA Tandem Lot stratigraphic test well was drilled to a depth of 6,855 feet in the northern portion of the Newark Basin in southern New York State. Approximately 9 miles south-southeast on the Lamont Doherty Campus, TW-4 was drilled and cored in 2013 to a depth of 1,802 feet and contacted apparent igneous basement at a depth of 1,712 feet. Both wells penetrated the Palisades Sill ranging from 800 feet thick in the eastern well to approximately 1,800 feet in thickness at the 1-NYSTA Tandem Lot deep drill site. A diabase sill can provide an excellent seal and dense confining layer for potential CO2 storage reservoirs and flow layers that are situated beneath it within the Stockton Sandstone. The Stockton Sandstone was encountered beneath the sill in the TW-4 well on the Lamont campus, and data integration suggests that it was likely observed near total depth in the deep 1-NYSTA Tandem Lot well. The test wells confirm and define reservoirs are present beneath the sill and offer CO2 storage potential. The integration of geologic and reservoir characterization of well logs, formation cores, and formation fluids indicated Triassic age lacustrine playa lake and mudbank shales of the Upper Passaic Group can provide an effective seal for the porous and permeable underlying sandstone reservoir layers. This project acquired seismic data, drilled borehole well logs, acquired core samples, and integrated these findings to provide a better understanding of the subsurface geologic formations in the Newark Rift Basin. These findings have contributed to a higher degree of accuracy in predicting potential geologic storage opportunities, while refining geologic storage capacity estimates for the indicated reservoirs and flow units.

.las↗
DOE OSTI2026

The effect of hydroxyl spacing in diols on the solvation structure, dynamics, and transport properties of choline chloride-based deep eutectic solvents

Deep eutectic solvents (DESs) are a class of liquids that offer great potential in alleviating some of the challenges present in today's long-term energy storage methods because they have physical properties that are favorable for storable electrolyte solutions. In this work, a series of glycols (ethylene glycol, 1,3-propanediol, 1,4-butanediol, and 1,5-pentanediol) were studied as potential hydrogen bond donors (HBD) with a common choline chloride (ChCl) as the hydrogen bond acceptor (HBA). The solvation dynamics of the prepared systems were studied by measuring the solvent reorganization response using femtosecond transient absorption spectroscopy (fs-TA). Conductivity, viscosity, density, ET(30) polarity, and dynamics of the prepared DESs were analyzed, with a particular interest in determining the effect of HBD chain length on these parameters. Here, classical molecular dynamics simulations were employed to investigate how the local liquid structure, solvent dynamics, and bulk solvent properties vary with changes in glycol chain length.

Conductivity↗
DOE OSTI2025

Incentivizing Cooperative Merging Control: Insights from Multi-Agent Deep Reinforcement Learning

Cooperative driving automation enables connected and automated vehicles (CAVs) to devise cooperative merging control, introducing great potentials to alleviate traffic congestion, reduce energy consumption, and enhance safety for highway on-ramp operations. Although numerous CAV cooperative merging algorithms have been developed to improve energy and traffic performance, the agreement-seeking among CAV users and their local benefits have been understudied. This can lead to rejections of cooperative merging plans and jeopardizing CAV performance, as a cooperation may entail certain CAVs to sacrifice their local benefits to achieve a system optimum. To address this issue, the study first leverages multi-agent deep reinforcement learning (MADRL) factoring both local reward and regional reward to demonstrate the discrepancies between CAV users’ local benefits and system optimum. Next, the existence of a correlated equilibrium is proved to characterize the convergence of MADRL training. This further facilitates the incorporation of incentives (computed based on reward discrepancies) to compensate for CAV users’ local benefits and facilitate system-optimal agreements in cooperative merging operations.

Zhou, Anye [ORNL] (ORCID:0000000301455579)↗
DOE OSTI2025

A generative artificial intelligence framework for long-time plasma turbulence simulations

Generative deep learning techniques are employed in a novel framework for the construction of surrogate models capturing the spatiotemporal dynamics of 2D plasma turbulence. The proposed Generative Artificial Intelligence Turbulence (GAIT) framework enables the acceleration of turbulence simulations for long-time transport studies. GAIT leverages a convolutional variational auto-encoder and a recurrent neural network to generate new turbulence data from existing simulations, extending the time horizon of transport studies with minimal computational cost. The application of the GAIT framework to plasma turbulence using the Hasegawa–Wakatani (HW) model is presented, evaluating its performance via various analyses. Very good agreement is found between the GAIT and the HW models in the spatiotemporal Fourier and Proper Orthogonal Decomposition spectra, the flow topology characterized by the Okubo–Weiss parameter, and the time autocorrelation function of turbulent fluctuations. Excellent agreement has also been obtained in the probability distribution function of particle displacements and the effective turbulent diffusivity. In-depth analyses of the latent space of turbulent states, choice of hyperparameters and alternative deep learning models for the time prediction are presented. Our results highlight the potential of Artificial Intelligence-based surrogate models to overcome the computational challenges in turbulence simulation, which can be extended to other situations such as geophysical fluid dynamics.

Artificial intelligence↗
DOE OSTI2025

Technology Case Study: Economic, Sustainability, and Deployment Considerations for Sustainable Aviation Fuels Produced via Lignocellulosic Sugar Catalysis

This report presents a technology case study reflecting one exemplary representative pathway for the conversion of lignocellulosic sugars to sustainable aviation fuels (SAF) via aqueous phase reforming (APR) catalysis, considered within a broader integrated biorefinery framework based on biochemical processing operations. While far from the only option for converting sugars to SAF, this pathway was selected as a case study here based on its relatively high technology maturity and simplistic processing approach (avoiding complex separations or other equipment scalability challenges), coupled with the potential for high fuel yields and favorable costs/carbon intensities with opportunities for further near-term optimization. The report considers key process integration and engineering design considerations for a modeled hypothetical, nth-plant commercial biorefinery, reflecting a number of processing options and parameters envisioned to be achievable as future goals. Resultant outputs from Aspen Plus process simulations are evaluated through techno-economic and life cycle analyses (TEA and LCA), including implications for marginal cost of CO2 abatement and inclusion of currently-applicable policy incentives. Moving beyond base case configurations, a number of alternative scenarios are also evaluated for their ability to further improve economics, greenhouse gas (GHG) emissions, and marginal cost of abatement, highlighting a path to achieve deep decarbonization goals of more than 70% GHG reduction for SAF (with the potential to reach net-negative carbon intensities in some cases) under reasonable fuel production costs. The report also highlights future opportunities and gaps for further research on this technology pathway.

09 BIOMASS FUELS↗
DOE OSTI2024

Trap‐Engineering the Persistent Luminescence of Ca 3 Ga 4 O 9 :Tb 3+ via Al 3+ Substitution for Optical Data Storage

Abstract Optically stimulated luminescence (OSL) materials hold great potential for optical data storage (ODS) and anticounterfeiting applications. Nevertheless, the scarcity of suitable luminescent materials with deep‐level traps remains a significant obstacle. Herein, a host substation strategy have been employed to tune the persistent luminescence (PersL) and OSL properties of Ca 3 Ga 4 O 9 :Tb 3+ by Al 3+ substitution through trap engineering and demonstrated their potential. Specifically, the photoluminescence of the Ca 2.985 (Ga 1‐y% Al y% ) 4 O 9 :0.5%Tb 3+ of Tb 3+ is first investigated due to its different occupancies of Ca 2+ . The influence of host substitution on the crystal structure, trap depth, trap density, PersL, and OSL properties have further investigated. A series of strong PersL and OSL peaks from the Ca 2.985 (Ga 1‐y% Al y% ) 4 O 9 :0.5%Tb 3+ with bluish‐green emissions have been observed. The Ca 2.985 (Ga 1‐y% Al y% ) 4 O 9 :0.5%Tb 3+ have shown controllable photon release upon thermal and optical stimuli, enhancing their performance for ODS. Thermally stimulated luminescence suggests that vacancy and defect concentrations inside the Ca 3‐x% (Ga 1‐y% Al y% ) 4 O 9 :x%Tb 3+ can be manipulated by Tb 3+ doping and Al 3+ substitution, which ultimately leads to the formation of deep traps and a broad distribution of traps with increased deep trap concentration. The work demonstrates that trap engineering through Al 3 ⁺ substitution is an effective method for tuning PersL and OSL properties of Ca 2.985 (Ga 1‐y% Al y% ) 4 O 9 :0.5%Tb 3+ for ODS.

Abeywickrama, Thulitha M. [Department of Chemistry↗
DOE OSTI2024

Low‐Temperature Synthesis of Stable CaZn 2 P 2 Zintl Phosphide Thin Films as Candidate Top Absorbers

Abstract The development of tandem photovoltaics and photoelectrochemical solar cells requires new absorber materials with bandgaps in the range of ≈1.5–2.3 eV, for use in the top cell paired with a narrower‐gap bottom cell. An outstanding challenge is finding materials with suitable optoelectronic and defect properties, good operational stability, and synthesis conditions that preserve underlying device layers. This study demonstrates the Zintl phosphide compound CaZn 2 P 2 as a compelling candidate semiconductor for these applications. Phase‐pure, ≈500 nm‐thick CaZn 2 P 2 thin films are prepared using a scalable reactive sputter deposition process at growth temperatures as low as 100 °C, which is desirable for device integration. Ultraviolet‐visible spectroscopy shows that CaZn 2 P 2 films exhibit an optical absorptivity of ≈10 4 cm −1 at ≈1.95 eV direct bandgap. Room‐temperature photoluminescence (PL) measurements show near‐band‐edge optical emission, and time‐resolved microwave conductivity (TRMC) measurements indicate a photoexcited carrier lifetime of ≈30 ns. CaZn 2 P 2 is highly stable in both ambient conditions and moisture, as evidenced by PL and TRMC measurements. Experimental data are supported by first‐principles calculations, which indicate the absence of low‐formation‐energy, deep intrinsic defects. Overall, this study shall motivate future work integrating this potential top cell absorber material into tandem solar cells.

14 SOLAR ENERGY↗
DOE OSTI2026

Performance and transport in the ARC tokamak

The ARC TM tokamak, a high-field (𝐵 𝑇 = 11.4 T) fusion power plant, under development by Commonwealth Fusion Systems, is studied using a suite of integrated modelling tools to predict its fusion power generation (𝑃𝑓⁡𝑢⁢𝑠), transport and confinement properties. Analysis is based off an ARC operational point scoped first with zero-dimensional (0-D) plasma operational contour (POPCON) modelling to produce 1.13 GW of fusion power. A suite of integrated modelling tools (TRANSP, ASTRA and TORAX) were applied to predict the performance and kinetic profiles of the ARC design point, yielding a range of predicted performance spanning from ∼900 to 1300 MW in rough quantitative agreement with POPCON predictions. The sensitivity of these results to uncertain modelling inputs was probed using scans of pedestal boundary conditions around EPED-predicted values (total pressure and temperature ratios), tungsten concentration and seperatrix density around their nominal assumptions. Pedestal pressure and pedestal top (𝑇 𝑖 /𝑇 𝑒 ) play a large role in 1.5-dimensional performance predictions, able to modify the predicted 𝑃 𝑓⁡𝑢⁢𝑠 by a factor of 2 within reasonable assumptions. High-fidelity core nonlinear gyrokinetic profile predictions, performed using CGYRO (Candy et al. 2016 J. Comput. Phys., vol. 324, pp. 73–93) coupled with the PORTALS (Rodriguez-Fernandez et al. 2024 Nucl. Fusion, vol. 64, 076034; Phys. Plasmas, vol. 31, 2024, 062501) framework, yield substantially lower performance (𝑃 𝑓⁡𝑢⁢𝑠 =677 MW) compared with 0-D and medium-fidelity modelling for nominal assumptions, showing that there is non-negligible uncertainty between models and that future work on SPARC may help resolve discrepancies. Lower overall performance results from significantly reduced volume-averaged densities and temperatures, along with reduced levels of density and temperature peaking. Turbulence and transport are largely dominated by ion temperature gradient across the profile, confirmed by both linear stability and the response of the nonlinear fluxes to changes in gradients, with some impact of kinetic ballooning modes in the deep core. This work represents one of the most complete scoping of potential fusion power plant conditions performed to date. The extensive integrated modelling provides confidence in ARC performance approaching 1 GW, while nonlinear gyrokinetic modelling results in open questions into the physics of density and temperature peaking in fusion-power-plant-relevant operational space. A discussion of results and the role that the SPARC tokamak (Creely et al. 2020 J. Plasma Phys., vol. 86, 865860502) will play in informing ARC design, performance and operation is presented.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Tuning the Solvation and Solubility Properties of Molecularly Heterogeneous Nonionic Deep Eutectic Solvents via Interface Organization

Common separation techniques, such as liquid− liquid extraction, are usually used for extractions and purifications due to their industrial scalability and affordability. However, these well-established practices are hindered by low selectivity and challenges in recovering solutes and solvents. Deep eutectic solvents (DES), a fairly new type of solvent, have the potential to overcome these issues. DESs are binary mixtures whose physical properties can be tuned by selecting the appropriate precursors to facilitate and/or enhance processes such as extraction. A promising DES for selective separations is formed when lauric acid (LA) is mixed with N-methylacetamide (NMA). This LA-NMA DES has a heterogeneous microscopic structure that can solvate compounds with completely different polarities. This study explores how forming an organized structure on the mesoscale affects the solubility of nonpolar solutes in nonionic DESs. To this end, the molecular and mesoscale structures and their effect on the solubility and solvation properties are evaluated for the LA-NMA DES and two new DESs with slight chemical variations in their precursors. It is observed that the organization of the nonpolar DES domains and, consequently, of their interfaces directly relates to the solubility of nonpolar compounds. Specifically, correctly selecting the DES precursors that form organized nonpolar domains leads to an organized interface in which the nonpolar solutes are solvated, thereby increasing the solubility. Additionally, enhanced dissolution power was observed in a completely different DES with mesoscale order in its molecular structure and composed of menthol and lauric acid. The latter result further validates the proposed tunability of the DES dissolution power through organized interfaces, extending it beyond a specific DES family and opening the possibility of new extraction-tailored designer solvents.

Extraction↗
DOE OSTI2025

Understanding Redox Organic Behavior in Deep Eutectic Solvents: Considerations for Molecular Design

Electrolytes based on deep eutectic solvents (DESs) coupled with redox active organic molecules have shown potential as a versatile and energy dense electrochemical energy storage system. However, progress in these systems has been held back by a lack of understanding of the irregular behavior displayed when redox active organic molecules are transitioned from other solvent systems. In this work, the hydrogen bonding characteristics of a series of redox organic molecules were investigated through infrared spectroscopy and molecular modeling. New understanding of these interactions was then used to explain their electrochemical behavior in a DES electrolyte. A model was used to predict the behavior of new derivatives towards the design of an optimized redox organic-DES system. Hydrogen bonding between the redox molecules and the solvent was found to significantly shift the potential of a redox reaction more positive when a hydrogen bond forms at the redox active site. It was predicted that functionalizing a molecule with electron withdrawing groups to lower the electron density of the redox active functional group lowers the strength of the hydrogen bond and thus alleviates the undesirable potential shift. This hypothesis was demonstrated by the addition of nitro groups to fluorenones.

25 ENERGY STORAGE↗
DOE OSTI2026

Defining Infrastructure Feasibility for Hub-Scale Offshore Atlantic Carbon Storage in the Northeastern United States

In the Northeast U.S., deep rock formations along the Atlantic outer continental shelf may have the potential to sequester 150–1136 million metric tons of CO2. However, the design and infrastructure necessary to develop offshore carbon storage in this region is not well defined because there has been little oil and gas exploration and no commercial production. Consequently, an infrastructure feasibility design was completed for a hub-scale offshore CO2 storage system along the Northeast U.S. Atlantic. The design included development of a detailed, site-specific geological model for a location near the Great Stone Dome geological structure in the Baltimore Canyon Trough off the coast of Delaware, Maryland, and New Jersey. A field injection system topology design was completed to portray a design with eight wells in two clusters connected by central manifolds. Reservoir simulations were completed for the injection system that showed the hub may be able to inject 17 million metric tons (MMT) of CO2 per year for thirty years, but injection rates varied substantially across the eight wells. A CO2 pipeline design determined feasible routes from the east coast shoreline to the injection field. Finally, the CO2 injection system design included subsea injection trees, manifolds, and power umbilicals. This is the first study to define large-scale carbon storage design and infrastructure options for the offshore Atlantic, which can help to progress this region towards field characterization and early-mover deployment for future decarbonization in the region.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Recent Advances in the Use of Ionic Liquids and Deep Eutectic Solvents for Lignocellulosic Biorefineries and Biobased Chemical and Material Production

Biorefineries, which process biomass feedstocks into valuable (bio)products, aim to replace fossil fuel-based refineries to produce energy and chemicals, reducing environmental and health hazards, including climate change, and supporting a sustainable economy. In particular, lignocellulose-based biorefineries, utilizing the most abundant renewable feedstock on Earth, have significant potential to supply sustainable energy, chemicals and materials. Ionic liquids (ILs, organic salts with low melting temperatures) and deep eutectic solvents (DESs, mixtures with eutectic points lower than the ideal mixture) are capable of dissolving some of the key lignocellulose polymers, and even the whole biomass. Furthermore, they have intrinsic advantages over molecular solvents, including safer usage profiles and high tunability, which allow tailored physicochemical properties. Such properties provide unique opportunities for the development of new processes that could unlock the full potential of future biorefineries. Here, we review the current state of lignocellulosic biomass processing with ILs and DESs, with a specific focus on the pretreatment chemistry, process flow and products from each component; followed by discussions on sustainability assessments and technological challenges. We aim to inform the research community about the opportunities, challenges and perspectives in developing truly sustainable lignocellulose-based biorefineries.

09 BIOMASS FUELS↗
DOE OSTI2024

Accelerating Resonance Searches via Signature-Oriented Pre-training

The search for heavy resonances beyond the Standard Model (BSM) is a key objective at the LHC. While the recent use of advanced deep neural networks for boosted-jet tagging significantly enhances the sensitivity of dedicated searches, it is limited to specific final states, leaving vast potential BSM phase space underexplored. We introduce a novel experimental method, Signature-Oriented Pre-training for Heavy-resonance ObservatioN (Sophon), which leverages deep learning to cover an extensive number of boosted final states. Pre-trained on the comprehensive JetClass-II dataset, the Sophon model learns intricate jet signatures, ensuring the optimal constructions of various jet tagging discriminates and enabling high-performance transfer learning capabilities. We show that the method can not only push widespread model-specific searches to their sensitivity frontier, but also greatly improve model-agnostic approaches, accelerating LHC resonance searches in a broad sense.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Unifying Quantum Materials Modeling and Experiments: The Role of Machine Learning Interatomic Potentials

Computational experiments have emerged as a powerful complement to traditional experiments in the design of new materials. The development of machine learning (ML) and deep learning techniques, combined with database construction and data mining, has significantly enhanced traditional quantum mechanical methods. This synergy enables the rapid development of structure-property relationships. In this talk, I will discuss our recent efforts in applying Machine Learning Interatomic Potentials (MLIAPs) to accelerate materials modeling across various material classes and challenging applications where traditional methods fall short. First, I will highlight the success of MLIAPs in accurately modeling the melting behavior of complex materials. Our results demonstrate high fidelity with experimental observations and also with calculated reference melting temperatures. In the second application, I will discuss how MLIAPs are trained and applied to elucidate the interplay between segregation tendencies and surface reconstructions in CuNi alloys under oxidizing conditions. A key factor in the success of these MLIAP applications is the design of minimalistic yet flexible datasets along with a computational framework for training MLIAPs.

Saidi, Wissam↗
DOE OSTI2026

Dynamic asymmetric strain imprinted into substrates by an oxide thin film

In film-substrate systems, the substrate role is often considered to be limited to providing static mechanical constraints. Dynamic film-substrate interactions when a structural change in the film modifies the substrate are generally disregarded. Here, using combined x-ray and electron microscopies, we observed that an electrically induced filament in a vanadium dioxide film created strong asymmetric strain in an underlying sapphire substrate. This asymmetric substrate strain fed back into the film and defined the filament expansion direction, revealing the importance of film-substrate dynamic interactions in determining film functionality. Furthermore, the strain imprint propagated at least tens of micrometers deep into the substrate, exceeding the film thickness by more than 200-fold, potentially enabling substrate functionalization as an active mechanical coupling media in three-dimensional integrated microelectronic architectures.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Applications of Nickelate perovskites for neuromorphic computing from electronic structure and Machine Learning

While the limit of Moore's law is presently being reached with current microelectronic technologies, we need to develop new paradigms that overcome this limitation. In that respect, neuromorphic computing is a concept that emulates the neural behavior and response of the human brain, and it has been recognized as a promising alternative approach. In this research project, we will perform multi-fidelity scale bridging to explore the potential use of materials with metal to insulator transition for neuromorphic applications. In particular, rare earth nickelates are promising for such purposes, as the transition in these materials is quite sensitive to a broad set of different external stimuli. Our multi-fidelity approach will bridge the high-fidelity electronic structure calculations with classical potentials. We will bridge dynamical mean field theory with a classical atomistic representation via a deep learning force field. The neural network is trained with energies, charges, and forces obtained by accurate electronic structure theories based on Dynamical Mean Field Theory. The configurational space is generated from known crystal phases, ab initio molecular dynamics with exchange-correlation functionals corrected with the Hubbard model, disordered phases with different concentrations of oxygen vacancies, and nonsymmetrical positions and induced strain by grain interfaces or contact with a substrate. Strategies to train the model with a reduced number of training examples are obtained from active learning methods, and new structures for improving the learning process are generated by using machine learning autoencoders. This classical potential will be validated through a diversity of electronic structure methods and represents an important step to combine the flexibility and accuracy of first-principles with the speed of classical potentials. The generated multi-fidelity surrogate model will be used to understand the role of strain, oxygen vacancies, proton doping, the variation of the crystal phase, substrate effects, vibrational effects as the octahedral rotation, grain boundaries and defect effects on the response of a Metal to Insulator Transition (MIT) in correlated materials. Long time and large-scale simulations will help understand the role of different stimuli to control the hysteresis of the MIT, as it has been experimentally suggested. Selected configurations will be analyzed with higher-level theories to provide an accurate electronic description and to study how the orbitals and charges are rearranged under different conditions.

36 MATERIALS SCIENCE↗