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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

Portable Load Measurement Device for Use During ARED Exercise on ISS

The Advanced Resistive Exercise Device (ARED) (Fig.1) is unique countermeasure hardware available to crewmembers aboard the International Space Station (ISS) used for resistance exercise training to protect against bone and muscle loss during long duration space missions. ARED instrumentation system was designed to measure and record exercise load data, but: - Reliably accurate data has not been available due to a defective force platform. - No ARED data has been recorded since mid-2011 due to failures in the instrumentation power system. ARED load data supports on-going HRP funded research, and is available to extramural researchers through LSDA-Repository. Astronaut Strength, Conditioning, and Rehabilitation specialists (ASCRs) use ARED data to track training progress and advance exercise prescriptions. ARED load data is necessary to fulfill medical requirements. HRP directed task intends to reduce to program risk (HRP IRMA Risk 1735), and evaluate the XSENS ForceShoeTM as a means of obtaining ARED load data during exercise sessions. The XSENS ForceShoes"TM" will fly as a hardware demonstration to ISS in May 2014 (39S). Additional portable load monitoring devices (PLMDs) are under evaluation in the ExPC Lab. PLMDs are favored over platform redesign as they support future exploration needs.

Hanson, A.↗
DOE OSTI2022

EQC: Ensembled Quantum Computing for Variational Quantum Algorithms

Variational quantum algorithms (VQA), which are comprised of a classical optimizer and a parameterized quantum circuit, emerges as one of the most promising approaches of harvesting quantum power in the noisy-intermediate-scale-quantum (NISQ) era. However, the deployment of VQAs on today's NISQ devices often faces considerable system noise and prohibitively slow training speeds. On the other hand, the expensive supporting sources and infrastructure make quantum computers extremely keen on high utilization. In this paper, we propose a novel way of thinking about a quantum backend: rather than relying on one physical device which tends to introduce platform-specific noise and bias, a quantum ensemble, which distributes quantum tasks across parallel devices, can serve as a virtualized quantum computer for offering reduced noise levels through an adaptive mixture and also provide significantly improved training speeds through parallelization. With this idea, we build a distributive VQA optimization framework called DVQA, serving as the first effort in adopting parallel quantum devices for cooperative VQA training. To further constraint noise and speed-up convergence, we design a model for individual NISQ devices concerning their properties and running conditions, and propose a weighting mechanism for regularizing the returned gradients. Extensive evaluations on 10 IBM-Q quantum devices using the VQE example show that the distributive VQA training framework can substantially boost the training speed by 10.5x on average (up to 86x and at least 5.2x) with improved training accuracy.

Stein, Samuel A.↗
NASA NTRS2012

Space Operations Learning Center

The Space Operations Learning Center (SOLC) is a tool that provides an online learning environment where students can learn science, technology, engineering, and mathematics (STEM) through a series of training modules. SOLC is also an effective media for NASA to showcase its contributions to the general public. SOLC is a Web-based environment with a learning platform for students to understand STEM through interactive modules in various engineering topics. SOLC is unique in its approach to develop learning materials to teach schoolaged students the basic concepts of space operations. SOLC utilizes the latest Web and software technologies to present this educational content in a fun and engaging way for all grade levels. SOLC uses animations, streaming video, cartoon characters, audio narration, interactive games and more to deliver educational concepts. The Web portal organizes all of these training modules in an easily accessible way for visitors worldwide. SOLC provides multiple training modules on various topics. At the time of this reporting, seven modules have been developed: Space Communication, Flight Dynamics, Information Processing, Mission Operations, Kids Zone 1, Kids Zone 2, and Save The Forest. For the first four modules, each contains three components: Flight Training, Flight License, and Fly It! Kids Zone 1 and 2 include a number of educational videos and games designed specifically for grades K-6. Save The Forest is a space operations mission with four simulations and activities to complete, optimized for new touch screen technology. The Kids Zone 1 module has recently been ported to Facebook to attract wider audience.

Lui, Ben↗
DOE OSTI2024

Inverse Design of Photonic Surfaces via High throughput Femtosecond Laser Processing and Tandem Neural Networks

Abstract This work demonstrates a method to design photonic surfaces by combining femtosecond laser processing with the inverse design capabilities of tandem neural networks that directly link laser fabrication parameters to their resulting textured substrate optical properties. High throughput fabrication and characterization platforms are developed that generate a dataset comprising 35280 unique microtextured surfaces on stainless steel with corresponding measured spectral emissivities. The trained model utilizes the nonlinear one‐to‐many mapping between spectral emissivity and laser parameters. Consequently, it generates predominantly novel designs, which reproduce the full range of spectral emissivities (average root‐mean‐squared‐error < 2.5%) using only a compact region of laser parameter space 25 times smaller than what is represented in the training data. Finally, the inverse design model is experimentally validated on a thermophotovoltaic emitter design application. By synergizing laser‐matter interactions with neural network capabilities, the approach offers insights into accelerating the discovery of photonic surfaces, advancing energy harvesting technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2025

DuctGPT: A Generative Transformer for Forward Screening of Ductile Refractory Multi-Principal Element Alloys

Designing ductile materials for extreme environments such as fusion reactors requires a deep understanding of the complex interplay between electronic structure, mechanical stability, and wide compositional space. Here, in this work, we introduce DuctGPT, a physics-informed, GPT-powered machine learning platform that enables rapid and accurate prediction of ductility across a wide range of refractory multi-principal element alloys (MPEAs). Trained on both experimental and high-fidelity computational data, DuctGPT integrates descriptors such as density of states at the Fermi level, elastic constants, and valence electron concentration to capture the fundamental mechanisms governing ductile versus brittle behavior. Using this framework, we screen over 1000 compositions in of body-centered cubic (BCC) MPEAs, including two new alloy classes, i.e., NbTa-rich (NbTa $>$ 50 at.%) NbTa-Ti-V and W-rich ($>$ 50 at.%) W-Ti-V MPEAs, to rapidly identify promising alloy compositions with enhanced ductility. Validation against experimental data confirms the model's ability to predict ductility with high fidelity and low uncertainty. By leveraging conversational AI and robust physical modeling, DuctGPT provides a blueprint for the next generation of alloy design assistants, enabling human-AI collaboration in the accelerated discovery of ductile, high-performance materials for fusion, aerospace, and advanced manufacturing.

AI/ML↗
DOE OSTI2019

A Game-Theoretic Data-Driven Approach for Pseudo-Measurement Generation in Distribution System State Estimation

In this paper, we present an efficient computational framework with the purpose of generating weighted pseudo-measurements to improve the quality of distribution system state estimation (DSSE) and provide observability with advanced metering infrastructure (AMI) against unobservable customers and missing data. The proposed technique is based on a game-theoretic expansion of relevance vector machines (RVMs). This platform is able to estimate the nodal power consumption and quantify its uncertainty while reducing the prohibitive computational burden of model training for large AMI datasets. To achieve this objective, the large training set is decomposed and distributed among multiple parallel learning entities. The resulting estimations from the parallel RVMs are then combined using a game-theoretic model based on the idea of repeated games with vector payoff. It is observed that through this approach and by exploiting the seasonal changes in customers’ behavior the accuracy of pseudo-measurements can be considerably improved, while introducing robustness against bad training data samples. The proposed pseudo-measurement generation model is integrated into a DSSE using a closed-loop information system, which takes advantage of a branch current state estimator (BCSE) to further improve the performance of the designed machine learning framework. This method has been tested on a practical distribution feeder model with smart meter data for verification.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Cyber-CHAMP Task Analysis Survey Tool

Cyber-CHAMP Task analysis survey tool is a web-hosted code platform for an individual to select their every day tasking, based on industry documentation and standards, and produce an education and training mapping to provide them the proper associated cyber competency level(s).

Stailey, ShaneD.↗
DOE OSTI2018

Dynamics of X-pinches Powered by a Capacitor Bank

X-pinches are formed by crossing two metal wires in the shape of the letter ‘X’ between the electrodes of a pulsed power device. The plasma generated then evolves in complex ways and has been the subject of many experiments and simulations, both as a plasma object and for its use as a source of intense soft x-rays, of interest to high energy density plasma (HEDP) studies. The dynamics of the x-pinch that lead to the emission x-ray photons begin soon after this initial explosive phase, resulting in the formation of a micro z-pinch (≤ 300 µm long, ~100 µm diameter) at the crossing point. If the current is high enough, then a subsonic plasma implosion in the micro z-pinch begins, which creates a high density, high temperature plasma. These x-pinches have been typically studied at the nanosecond time scale (rise-time $τ_r ≤ 100 ns, dI/dt > 1 kA/ns$). The microsecond x-pinch ($I_p~350 kA, τ_{1/4} = 1 µs, 0.3 kA/ns$) allows a different parameter space to be studied that compliments HEDP studies in the nanosecond regime (with an attendant lower hard-radiation hazard) but it also creates plasma conditions that are interesting in their own right, with features that make it a good model for studying the hydrodynamics of astrophysical jets, for example. With high electron density and temperatures pushing close to 0.5 keV, it is relevant for high energy density studies and provides a suitable test bed for the spectroscopy of highly ionized atoms. The aim of the original proposal was to initiate a new experimental plasma research program at Florida A&M University with three main objectives: (1) To create x-pinch plasmas from different materials using a direct capacitor bank discharge and to study the dynamics of the high-density plasmas and their associated soft x-ray emission properties and potential applications. (2) Understanding the nature of the supersonic plasma jets observed in the mid-plane of the microsecond x-pinch and their relevance as a laboratory model for studying astrophysical jets. (3) To provide a research platform to support the education of under-represented undergraduate and graduate students at Florida A&M University and to provide training in high energy density laboratory plasma physics. Each of the main objectives has been met and results of the work have been published in refereed journals or presented at several scientific meetings. A number of students both undergraduate and graduate have participated in the research program in a hands-on way and have thereby acquired training in high energy density physics. The success of the work can also be measured in two PhD dissertations (one of which is still in progress). The acquisition of important research equipment and instrumentation means that the work can continue in some fashion.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2011

Reinventing the International Space Station Payload Integration Processes and Capabilities

The fundamental ISS payload integration philosophy, processes and capabilities were established in the context of how NASA science programs were conducted and executed in the early 1990 s. Today, with the designation of the United States (US) portion of ISS as a National Lab, the ISS payload customer base is growing to include other government agencies, private and commercial research. The fields of research are becoming more diverse expanding from the NASA centric physical, materials and human research sciences to test beds for exploration and technology demonstration, biology and biotechnology, and as an Earth and Space science platform. This new customer base has a broader more diverse set of expectations and requirements for payload design, verification, integration, test, training, and operations. One size fits all processes are not responsive to this broader customer base. To maintain an organization s effectiveness it must listen to its customers, understand their needs, learn from its mistakes, and foster an environment of continual process improvement. The ISS Payloads office is evolving to meet these new customer expectations.

Jones, Rod↗
NASA NTRSDate not supplied

Behavioral Health and Performance Operations and Research in Human Spaceflight

There are two distinct functions of the Behavioral Health and Performance (BHP) specialists at NASA Johnson Space Center (JSC), with a shared goal of promoting optimal performance during all mission phases, beginning with selection to the NASA Astronaut Corps through training for and completing a spaceflight mission. The BHP Operations team is active in areas such as selection, training, routine behavioral healthcare services, and work-rest schedule. The BHP Laboratory is engaged in research in space and analog platforms to address the risks and other challenges associated with future deep space missions. Their research focuses on topics that align with the NASA Human Research Program’s Human Factors Behavioral Performance Element goals. This panel will provide details of recent work by BHP Operations and the BHP Laboratory at NASA JSC, and will demonstrate their shared goals, and contributions toward, optimizing performance and maintaining the behavioral health and well-being of astronauts during all mission phases.In 2020-2021, NASA engaged another astronaut applicant screening process to identify its 23rd group of astronaut candidates. The BHP Operations team completed a significant amount of work to evaluate applicants and provide valuable input to the Astronaut Selection Board and the Aerospace Medical Board. One presentation will provide an overview of the work completed by BHP; and one presentation will discuss a critical competency important for successful job performance of the astronauts – Team Orientation. The former will summarize aspects of BHP’s Select-in and Select-out process and provide lessons learned for future selection cycles; the latter will briefly discuss the competencies of BHP’s applicant screening work, with focus on the importance of Team Orientation and the challenges of its measure. A third presentation will provide an overview of BHP’s current fatigue management work with astronauts and mission-critical operators at JSC (e.g., flight directors, pilots), and provide a look at the direction these fatigue management services may take in future exploration missions. Two other presentations will focus on the research completed by the BHP Lab to measure in a standardized way the critical aspects of behavioral health and well-being. One of the presentations will provide an overview of the Exploration Measures being collected during all space and analog missions, and the importance these objective measures have for future research and operations. The final presentation will summarize analyses completed with these measures, as they have been harmonized across both ISS and Analog missions.

Gary Beven↗
DOE OSTI2019

Adaptive Learning for Concept Drift in Application Performance Modeling

Supervised learning is a promising approach for modeling the performance of applications running on large HPC systems. A key assumption in supervised learning is that the training and testing data are obtained under the same conditions. However, in production HPC systems these conditions might not hold because the conditions of the platform can change over time as a result of hardware degradation, hardware replacement, software upgrade, and configuration updates. These changes could alter the data distribution in a way that affects the accuracy of the predictive performance models and render them less useful; this phenomenon is referred to as concept drift. Ignoring concept drift can lead to suboptimal resource usage and decreased efficiency when those performance models are deployed for tuning and job scheduling in production systems. To address this issue, we propose a concept-drift-aware predictive modeling approach that comprises two components: (1) an online Bayesian changepoint detection method that can automatically identify the location of events that lead to concept drift in near-real time and (2) a moment-matching transformation inspired by transfer learning that converts the training data collected before the drift to be useful for retraining. We use application input/output performance data collected on Cori, a production supercomputing system at the National Energy Research Scientific Computing Center, to demonstrate the effectiveness of our approach. The results show that concept-drift-aware models obtain significant improvement in accuracy; the median absolute error of the best-performing Gaussian process regression improved by 58.8% when the proposed approaches were used.

adaptive learning↗
DOE OSTI2024

A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity

Recent advancements have highlighted the limitations of current quantum systems, particularly the restricted number of qubits available on near-term quantum devices. This constraint greatly inhibits the range of applications that can leverage quantum computers. Moreover, as the available qubits increase, the computational complexity grows exponentially, posing additional challenges. Consequently, there is an urgent need to use qubits efficiently and mitigate both present limitations and future complexities. To address this, existing quantum applications attempt to integrate classical and quantum systems in a hybrid framework. In this study, we concentrate on quantum deep learning and introduce a collaborative classical-quantum architecture called co-TenQu. The classical component employs a tensor network for compression and feature extraction, enabling higher-dimensional data to be encoded onto logical quantum circuits with limited qubits. On the quantum side, we propose a quantum-state-fidelity-based evaluation function to iteratively train the network through a feedback loop between the two sides. co-TenQu has been implemented and evaluated with both simulators and the IBM-Q platform. Compared to state-of-the-art approaches, co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting. Additionally, it outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.

42 ENGINEERING↗
DOE OSTI2019

High spatial resolution imaging of biological tissues using nanospray desorption electrospray ionization mass spectrometry

Mass spectrometry imaging (MSI) enables label-free spatial mapping of hundreds of biomolecules in tissue sections. This capability provides valuable information on tissue heterogeneity that is difficult to obtain using population-averaged assays. Despite substantial developments in both instrumentation and methodology, MSI of tissue samples at single-cell resolution remains challenging. In our research, we describe a protocol for robust imaging of tissue sections with a high (better than 10-μm) spatial resolution using nanospray desorption electrospray ionization (nano-DESI) mass spectrometry, an ambient ionization technique that does not require sample pretreatment before analysis. In this protocol, mouse uterine tissue is used as a model system to illustrate both the workflow and data obtained in these experiments. We provide a detailed description of the nano-DESI MSI platform, fabrication of the nano-DESI and shear force probes, shear force microscopy experiments, spectral acquisition, and data processing. A properly trained researcher (e.g., technician, graduate student, or postdoc) can complete all the steps from probe fabrication to data acquisition and processing within a single day. We further describe a new strategy for acquiring both positive- and negative-mode imaging data in the same experiment. This is achieved by alternating between positive and negative data acquisition modes during consecutive line scans. Using our imaging approach, hundreds of high-quality ion images were obtained from a single uterine section. This protocol enables sensitive and quantitative imaging of lipids and metabolites in heterogeneous tissue sections with high spatial resolution, which is critical to understanding biochemical processes occurring in biological tissues.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Evolution at the Edge: Real-Time Evolution for Neuromorphic Engine Control

Neuromorphic computing systems are attractive for real-time control at the edge because of their low power operation, real-time processing capabilities and their potential ability to do online learning. In this work, we describe an approach for performing real-time evolution of spiking neural networks for neuromorphic systems at the edge called Neuromorphic Optimization using Dynamic Evolutionary Systems or NODES. We apply this approach to real-time combustion engine control and develop an engine-specific hardware platform for NODES called FireBox. We demonstrate how the real-time evolution approach works in simulation and the performance of networks trained in simulation on the physical engine.

Maldonado Puente, Bryan [ORNL] (ORCID:000000033880↗
DOE OSTI2023

BuildingsBench: A Benchmark for Universal Building Load Forecasting [SWR-23-51]

The residential and commercial building stock in the United States is responsible for a significant percentage of energy consumption and greenhouse gas emissions. Electrification of end-uses, as well as decarbonizing the electrical grid through renewable energy sources such as solar and wind, constitutes the pathway to zero-emission buildings. Forecasting day-ahead building energy consumption is an integral part of this solution. Currently, specialized forecasting models are hand-made for each individual building, which is time-consuming, expensive, and leads to duplicated efforts. BuildingsBench is a Python software framework for training and comparing generalized machine learning models for universal building load forecasting. This challenge tasks a single foundational model to generalize its forecasts for a wide variety of buildings, across geographic regions, building types, weather patterns, and more. This software provide code for pre-training such models and subsequently evaluating their performance on a suite of hundreds of diverse real and synthetic buildings. BuildingsBench is a platform for: - Large-scale pretraining with the synthetic Buildings-900K dataset for short-term load forecasting (STLF). Buildings-900K is statistically representative of the entire U.S. building stock and is extracted from the NREL End-Use Load Profiles database. - Benchmarking on two tasks evaluating generalization: zero-shot STLF and transfer learning for STLF. We provide an index-based PyTorch Dataset for large-scale pretraining, easy data loading for multiple real building energy consumption datasets as PyTorch Tensors or Pandas DataFrames, simple (persistence) to advanced (transformer) baselines, metrics management, and more.

Emami, Patrick↗
DOE OSTI2014

A Multi-State Model for Catalyzing the National Home Energy Retrofit Market (Final Technical Report)

In the fall of 2010, the Alabama Department of Economic and Community Affairs (ADECA) launched the Multi-State Model for Catalyzing the National Home Energy Retrofit Market Project (Multi-State Project). This residential energy efficiency pilot program was a collaborative effort among the states of Alabama, Massachusetts, Virginia, and Washington, and was funded by competitive State Energy Program (SEP) awards through the U.S. Department of Energy (DOE). The objective of this project was to catalyze the home energy efficiency retrofit market in select areas within the state of Alabama. To achieve this goal, the project addressed a variety of marketplace elements that did not exist, or were underdeveloped, at the outset of the effort. These included establishing minimum standards and credentials for marketplace suppliers, educating and engaging homeowners on the benefits of energy efficiency and addressing real or perceived financial barriers to investments in whole-home energy efficiency, among others. The anticipated effect of the activities would be increased market demand for retrofits, improved audit to retrofit conversion rates and growth in overall community understanding of energy efficiency. The four-state collaborative was created with the intent of accelerating market transformation by allowing each state to learn from their peers, each of whom possessed different starting points, resources, and strategies for achieving the overall objective. The four partner states engaged the National Association of State Energy Officials (NASEO) to oversee a project steering committee and to manage the project evaluation for all four states. The steering committee, comprised of key program partners, met on a regular basis to provide overall project coordination, guidance, and progress assessment. While there were variances in program design among the states, there were several common elements: use of the Energy Performance Score (EPS) platform; an audit and home energy rating tool; emphasis on community based coordination and partnerships; marketing and outreach to increase homeowner participation; training for market actors; access to financing options including rebates, incentives, and loan products; and an in depth process evaluation to support continual program improvement and analysis. In Alabama, Nexus Energy Center operated energy efficiency retrofit programs in Huntsville and Birmingham. In the Huntsville community the AlabamaWISE program was available in five Alabama counties: Cullman, Lawrence, Limestone, Madison, and Morgan. In Birmingham, the program was available to residents in Jefferson and Shelby Counties. In both communities, the program was similar in terms of program design but tailored marketing and partnerships to address the unique local conditions and population of each community. ADECA and the Southeast Energy Efficiency Alliance (SEEA) provided overall project management services and common resources to the local program administrator Nexus Energy Center, including contracted services for contractor training, quality assurance testing, data collection and reporting, and compliance. The fundamental components of the AlabamaWISE program included a vertical contractor-based business model; comprehensive energy assessments; third-party quality assurance; rebates for installation of energy saving measures; accessible, low-interest financing; targeted and inbound marketing; Energy Performance Score (EPS) tool to engage and educate homeowners; training for auditors, contractors, and real estate professionals; and online resources for education and program enrollment. Program participants were eligible to receive rebates or financing toward the assessments and upgrades to their home provided they reached at least 20 percent deemed or modeled energy savings. The design of each program focused on addressing several known barriers including: limited homeowner knowledge on the benefits of energy efficiency, lack of financing options, lack of community support for energy efficiency programs, and lack of trained market actors including contractors and real estate professionals. The programs were able to make progress on addressing all of these barriers and were most successful in offering financing options and training market actors. The most challenging barriers proved to be the act of building a market for energy efficiency where none previously existed, convincing homeowners of the value in investing in energy efficiency (and therefore completing retrofits), engaging electric and natural gas utilities to partner on delivery, and achieving the overall project target of 1,365 completed retrofits. The components that proved to be the most valuable to program success were engaged contractor networks that could promote and endorse the program, partnerships with local business and organizations, and the access to rebates, incentives and financing mechanisms. The programs were successful in building relationships with a variety of community participants including: local contractors, Associations of REALTORS, home builders associations, universities, utilities, local and state governments, and other non-profit organizations. Throughout this program, 933 building audits and 795 building retrofits were completed making homes in Alabama more comfortable, less expensive to operate, more valuable to the marketplace, and safer and healthier for families. Continuing on this momentum, Nexus Energy Center plans to continue operating and expanding operations in Alabama as a Home Performance with ENERGY STAR sponsor and will continue to provide energy services and education to communities in Alabama.

Grimes, Elizabeth M.↗
DOE OSTI2024

Git and Software Tools for CTS

This presentation will provide an overview of the progress made helping the EDX4CCS, SMART, and NRAP projects virtualize their CO2 Transport and Storage software tools to boost their stakeholder availability through the DisCO2ver platform. This presentation will also cover helpful software development tools with a focus on researchers and developers who have not had formal training or many years of experience in software engineering best practices.

Zaengle, Dakota↗
DOE OSTI2025

Developing Machine Learning Interatomic Potential for Fe-Cr-Ni Alloys

Accurate prediction of creep and fatigue behavior of stainless steel at elevated temperatures in hydrogen environment requires fundamental understanding of alloy-hydrogen interaction at cross-scale including bulk lattice and key defects such as vacancies, grain boundaries, surfaces, stacking faults, dislocations, and precipitates. This project aims to predict creep behavior of 347H stainless steel with H using machine learning interatomic potentials based on first-principles density functional theory simulations. The Moment Tensor Potentials platform is adopted for this work since it demonstrates a fine balance between model accuracy and computational efficiency. The potential is well trained based on large amount of high-fidelity density functional theory calculations. The validation is carried out by comparing various important properties including short range order, coefficient of thermal expansion, elastic properties, stacking fault energy, grain boundary energy, and surface energy. This work lays the foundation for reliable atomistic simulation of high temperature hydrogen attack of stainless steel.

density functional theory (DFT)↗