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At least 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

Planetary Defence Activities Beyond NASA and ESA

The collision of a significant asteroid or comet with Earth represents a singular natural disaster for a myriad of reasons, including: its extraterrestrial origin; the fact that it is perhaps the only natural disaster that is preventable in many cases, given sufficient preparation and warning; its scope, which ranges from damaging a city to an extinction-level event; and the duality of asteroids and comets themselves---they are grave potential threats, but are also tantalising scientific clues to our ancient past and resources with which we may one day build a prosperous spacefaring future. Accordingly, the problems of developing the means to interact with asteroids and comets for purposes of defence, scientific study, exploration, and resource utilisation have grown in importance over the past several decades. Since the 1980s, more and more asteroids and comets (especially the former) have been discovered, radically changing our picture of the solar system. At the beginning of the year 1980, approximately 9,000 asteroids were known to exist. By the beginning of 2001, that number had risen to approximately 125,000 thanks to the Earth-based telescopic survey efforts of the era, particularly the emergence of modern automated telescopic search systems, pioneered by the Massachusetts Institute of Technology’s (MIT’s) LINEAR system in the mid-to-late 1990s. Today, in late 2019, about 840,000 asteroids have been discovered, with more and more being found every week, month, and year. Of those, approximately 21,400 are categorised as near-Earth asteroids (NEAs), 2,000 of which are categorised as Potentially Hazardous Asteroids (PHAs) and 2,749 of which are categorised as potentially accessible. The hazards posed to us by asteroids affect people everywhere around the world. As well, the opportunities presented by asteroids may benefit our entire species. Thus, with such a large number of currently known asteroids and so many yet to be discovered, it is not surprising that individuals, organisations, institutions, and governments all around the world have become interested in the study of asteroids. Indeed, a variety of government space agencies, private organisations, and individuals have worked on developing the means by which to observe, study, and even interact with asteroids and comets for purposes including science, exploration, pioneering, commerce, and planetary defence. This includes significant individual contributions by amateur asteroid astronomers all over the world. International cooperation in planetary defence within the contexts of the United Nations and the International Asteroid Warning Network (IAWN) are discussed in Chapter 2, and the activities undertaken by the world’s larger space agencies, ESA and NASA, are discussed in Chapters 3 and 4. But, what of the other agencies and institutions around the world who are also working on the problem of defence against hazardous asteroids and comets, or related topics? In this chapter we provide an overview, in alphabetical order, of some of the planetary defence related efforts that have been undertaken around the world beyond the activities at the United Nations, NASA, and ESA.

Barbee, Brent W.↗
NASA NTRSDate not supplied

TPSAS-NF1676L-16833-DND

Semantic Infrastructure is central to realizing the first goal of the ASDC's Strategic Plan: expanding the ASDC's customer base by improving access to ASDC data. ASDC data comprises a widely heterogeneous set of complex products which presents two significant challenges in data access: Helping customers discover, among many available options, the most suitable data products for their purpose; and Guiding customers to easily and appropriately use products. Data products differ significantly in terms of how the data was collected and processed, even with similar subject matter. Understanding differences is critical to using data effectively. To reach a broader customer range, the ASDC must provide prospective users with enough information to quickly and meaningfully compare and evaluate data products. Data formats and structures also differ among products. Applications displaying and analyzing data need access to federated and semantically disambiguated data. Semantic technologies offer functionality for addressing this issue. Ontologies can provide robust, stable domain models serving as common schema for discovering, evaluating, comparing, and integrating data from disparate products. Reasoning engines and triple stores can leverage ontologies to support intelligent search applications allowing users to discover, query, retrieve, and easily reformat data from a broad spectrum of sources.

Beth Huffer↗
NASA NTRS2009

Long-term reliability of hand-soldering M55365 Ta capacitors

This is a continuation of the report “Effects of Hand Soldering MIL-PRF-55365 Tantalum Capacitors,” which discusses the parametric effects of convection reflow soldering, JPL standard hand soldering, and optimized hand soldering on solid tantalum capacitors. The current focus is determining the long-term reliability effects of the various soldering techniques. Unfortunately, the 100-piece sample size chosen for the highly accelerated life tests was too small to reliably determine the effects of the different soldering techniques on the lifespan of the parts. In hindsight, this likely could have been predicted based on the large alphas and small betas of the Weibull failure distributions discovered during the scouting trials. Now that the effects of inadequate sample size are better understood, an opportunity exists to not only discover whether there are real differences in the reliability associated with the various soldering techniques, but also the potential to discover the voltage acceleration model and the temperature acceleration effect for these capacitors. It is recommended that the testing be repeated with a more appropriate sample size to capture this useful information.

Spence, Penelope↗
NASA NTRSDate not supplied

Adaptive Stress Testing of Collision Avoidance Systems for Small UASs with Deep Reinforcement Learning

The next-generation Airborne Collision Avoidance System for smaller UASs (ACAS sXu) is currently being developed and tested by the Federal Aviation Administration (FAA) to provide detect-and-avoid capability for small unmanned aircraft operating beyond line-of-sight. Due to the complexity and safety-critical nature of the system, safety validation is important not only for the certification of the final system, but also for informing changes during the iterative development process. In this paper, we analyze a prototype of ACAS sXu in simulated aircraft encounters to discover scenarios of small near mid-air collisions (sNMACs), an important safety event in which two aircraft come closer than 50 feet horizontally and 15 feet vertically. Due to the size and complexity of the system as well as rarity of sNMAC events, traditional methods such as Monte Carlo testing often require informed setup and targeting to elicit failures. However, such a dependence on domain knowledge can be incompatible with the independent verification and validation (IV&V) process, the aim of which is to discover unforeseen issues. To address these challenges, we apply an accelerated validation method called adaptive stress testing (AST) to find the most likely sNMAC scenarios without reliance on system introspection. AST uses reinforcement learning to adapt the search towards the most promising areas of the search space as it progresses. We use a state-of-the-art deep reinforcement learning algorithm, proximate policy optimization, to more efficiently search the large and continuous state space. We find that this approach significantly improves the performance of AST compared to a prior approach based on Monte Carlo tree search. We perform experiments using AST to find sNMAC events under various encounter configurations, varying parameters pertaining to dynamics and coordination. Our experiments show AST to be very effective at finding sNMAC scenarios. We summarize our findings, presenting high-level categories of discovered sNMACs and specific examples of encounters in each category.

aircraft collision avoidance↗
NASA NTRSDate not supplied

Chasing Shadows in the Night: How NASA's Kepler and TESS Missions Are Revolutionizing Exoplanet Science

The first planet outside our own solar system was discovered almost thirty years ago in an extremely unlikely place, orbiting a pulsar, and the first exoplanet orbiting a Sun-like star was discovered nearly 26 years ago. In the time since, we’ve detected over 5000 planets and over 75% of these have been detected by transit surveys. The Kepler Mission, launched in 2009, has found the lion’s share of these exoplanets, and demonstrated that each star in the night sky has, on average, at least one planet. Kepler’s success spurred NASA and ESA to select several exoplanet-themed missions to move the field of exoplanet science forward from discovery to characterization: How do these planets form and evolve? What is the structure and composition of the atmospheres and interiors of these planets? Can we detect biomarkers in the atmospheres of these planets and learn the answer to the fundamental question, are we alone? NASA selected the Transiting Exoplanet Survey Satellite (TESS) in 2014 to conduct a nearly all-sky survey for transiting planets with the goal of identifying at least 50 small planets with measured masses that can be followed up by large telescopic assets, such as the upcoming James Webb Space Telescope. TESS has discovered 266 exoplanets so far, 100 of which are smaller than earth with measured masses. In this talk I will describe how we detect weak transit signatures in noisy but beautiful transit survey data sets and present some of the most compelling discoveries made so far by Kepler and TESS.

TESS↗
NASA NTRSDate not supplied

Prospects for Future Human Space Flight Missions to Near-Earth Asteroids

The forthcoming Near-Earth Object (NEO) Surveyor space telescope is a foundational asset designed to complete NASA’s congressionally-mandated goal of cataloging ≥90% of NEOs ≥140 meters in size as soon as practical, and discover Earth impactors far in advance. NEO Surveyor is also critical to near-Earth asteroid (NEA) exploration because it will find suitable low-Δv NEAs for robotic and human missions, often on Earth-like orbits with long synodic periods. Such NEAs have usually not been detected until imminent Earth launch opportunities because they were not observable until close to Earth. Discovering them far enough in advance to deploy missions requires a deep-space infrared survey telescope such as NEO Surveyor. In addition to posing hazards and being scientifically important, NEAs contain resources, such as water (OH), that could be utilized off-Earth. They also offer unique opportunities for the most ambitious human voyages ever undertaken. The Apollo program forever changed humanity’s perspective by showing us Earthrise from our Moon through human eyes. Crewed missions to NEAs will forever change our perspective again by showing us Earth as a distant point of light in the heavens as seen from an asteroid by astronauts. Planetary defense endeavors to understand asteroid and comet impact risks and develop mitigation capabilities. NEA exploration is synergistic in multiple ways. NEO Surveyor will discover and help characterize Earth impactors and accessible NEAs. Heavy-lift launch is highly enabling for both human NEA missions and planetary defense. Reconnaissance missions for planetary defense can also characterize NEAs prior to crewed missions. In situ resource utilization (ISRU) systems may be applicable to deflecting or destroying hazardous NEAs. NEA characterization data critically inform planetary defense efforts and indicate NEA types suitable for human exploration or ISRU. In 2010, NASA performed the Near-Earth Object (NEO) Human Space Flight (HSF) Accessible Targets Study (NHATS), creating an automated online system monitoring mission accessibility of NEAs 2 . NHATS database NEAs meet criteria that require less total mission Δv and/or round-trip mission duration than the Martian surface or even Mars orbit. There are currently 4,658 NHATS NEAs, and many rival or exceed lunar orbit/surface accessibility. Thus, NHATS NEAs could be explored by humans prior to attempting a Mars mission. Human missions to NEAs would test human-rated spacecraft systems with less cost and risk than Mars missions. Thus, human missions to NEAs are compelling in their own right while also providing prudent preparation for more demanding Mars missions. Lunar missions can offer similar opportunities, but they differ from NEA missions in important ways. Lunar missions pose less demanding psychological challenges to crew and ground staff. Earth light-time delay for communications is significantly longer during NEA missions. NEA missions are available that require more propulsion system consumables than lunar missions but less than Mars missions. In this paper, we summarize NEA accessibility for human missions, discuss motivations for such missions, survey knowledge about NEAs that informs future human missions, present exemplar mission opportunities to the currently known NHATS NEAs, and describe future work towards enabling human NEA missions.

Near-Earth asteroids↗
NASA NTRS2022

Multidecadal Trends in Ozone Chemistry in the Baltimore-Washington Region

Over the past four decades, policy-led reductions in anthropogenic emissions have improved air quality over the Baltimore-Washington region (BWR). Most of the improvements in meeting the ozone air quality metrics (NAAQS) did not occur until the early 2000s despite large reductions in ozone precursors (NOx, CO, and volatile organic compounds (VOCs)) in the prior decades. We use observations of ozone and ozone precursors from satellites, ground-based sites, and the 2011 DISCOVER-AQ aircraft campaign in Maryland to illustrate how ozone chemistry in the BWR evolved between 1972 and 2019. Analysis of weekday vs weekend probability of ozone exceedance indicates the BWR transitioned to the NOx-limited regime by 2000–2003. A data-constrained box model agrees with this transition period and illustrates the key roles of reduced emissions of formaldehyde (HCHO), aromatics, and other VOCs since 1996, which reduced the peak of ozone production at the time of the transition and likely prevented the BWR from experiencing worsening surface air quality as the region transitioned to NOx-limited chemistry. Analysis of satellite observations of tropospheric column HCHO to NO2 analyzed using a new approach for evaluation of chemical regimes derived from DISCOVER-AQ data also provide a consistent depiction of the timing of the transition period that we infer from ground-based observations and the box model. Finally, despite significant improvements in air quality over the past two decades, the BWR still has not met the EPA standard for surface ozone. With predominantly NOx-limited ozone chemistry over the BWR, continued decreases in emission of NOx will slow the rate of ozone production and help improve air quality. We highlight emissions of NO2 from the diesel truck fleet as a worthwhile focus for future policy because emissions from this source appear to influence day-of-week variations in observed NO2, with an accompanying effect on ozone.

Ozone↗
DOE OSTI2021

Accelerated Discovery and Design of Ultralow Lattice Thermal Conductivity Materials Using Chemical Bonding Principles

Semiconductors with very low lattice thermal conductivities are highly desired for applications relevant to thermal energy conversion and management, such as thermoelectrics and thermal barrier coatings. Although the crystal structure and chemical bonding are known to play vital roles in shaping heat transfer behavior, material design approaches of lowering lattice thermal conductivity using chemical bonding principles are uncommon. In this work, an effective strategy of weakening interatomic interactions and therefore suppressing lattice thermal conductivity based on chemical bonding principles is presented and a high-efficiency approach of discovering low κ L materials by screening the local coordination environments of crystalline compounds is developed. The resulting first-principles calculations uncover 30 hitherto unexplored compounds with (ultra)low lattice thermal conductivities from 13 prototype crystal structures contained in the Inorganic Crystal Structure Database. Furthermore, an approach of rationally designing high-performance thermoelectrics is demonstrated by additionally incorporating cations with stereochemically active lone-pair electrons. Here these results not only provide atomic-level insights into the physical origin of the low lattice thermal conductivity in a large family of copper/silver-based compounds but also offer an efficient approach to discover and design materials with targeted thermal transport properties.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Machine Learning Design of Perovskite Catalytic Properties

Abstract Discovering new materials that efficiently catalyze the oxygen reduction and evolution reactions is critical for facilitating the widespread adoption of solid oxide fuel cell and electrolyzer (SOFC/SOEC) technologies. Here, machine learning (ML) models are developed to predict perovskite catalytic properties critical for SOFC/SOEC applications, including oxygen surface exchange, oxygen diffusivity, and area specific resistance (ASR). The models are based on trivial‐to‐calculate elemental features and are more accurate and dramatically faster than the best models based on ab initio‐derived features, potentially eliminating the need for ab initio calculations in descriptor‐based screening. The model of ASR enables temperature‐dependent predictions, has well calibrated uncertainty estimates and online accessibility. Use of temporal cross‐validation reveals the model to be effective at discovering new promising materials prior to their initial discovery, demonstrating the model can make meaningful predictions. Using the SHapley Additive ExPlanations (SHAP) approach, detailed discussion of different approaches of model featurization is provided for ML property prediction. Finally, the model is used to screen more than 19 million perovskites to develop a list of promising cheap, earth‐abundant, stable, and high performing materials, and find some top materials contain mixtures of less‐explored elements (e.g., K, Bi, Y, Ni, Cu) worth exploring in more detail.

25 ENERGY STORAGE↗
DOE OSTI2021

Interfacial Defect of Lithium Metal in Solid‐State Batteries

Abstract All‐solid‐state battery with Li metal anode is a promising rechargeable battery technology with high energy density and improved safety. Currently, the application of Li metal anode is plagued by the failure at the interfaces between lithium metal and solid electrolyte (SE). However, little is known about the defects at Li–SE interfaces and their effects on Li cycling, impeding further improvement of Li metal anodes. Herein, by performing large‐scale atomistic modeling of Li metal interfaces with common SEs, we discover that lithium metal forms an interfacial defect layer of nanometer‐thin disordered lithium at the Li–SE interfaces. This interfacial defect Li layer is highly detrimental, leading to interfacial failure such as pore formation and contact loss during Li stripping. By systematically studying and comparing incoherent, coherent, and semi‐coherent Li–SE interfaces, we find that the interface with good lattice coherence has reduced Li defects at the interface and has suppressed interfacial failure during Li cycling. Our finding discovered the critical roles of atomistic lithium defects at interfaces for the interfacial failure of Li metal anode, and motivates future atomistic‐level interfacial engineering for Li metal anode in solid‐state batteries.

Yang, Menghao↗
DOE OSTI2021

Interfacial Defect of Lithium Metal in Solid-State Batteries

All-solid-state battery with Li metal anode is a promising rechargeable battery technology with high energy density and improved safety. Currently, the application of Li metal anode is plagued by the failure at the interfaces between lithium metal and solid electrolyte (SE). However, little is known about the defects at Li–SE interfaces and their effects on Li cycling, impeding further improvement of Li metal anodes. Herein, by performing large-scale atomistic modeling of Li metal interfaces with common SEs, we discover that lithium metal forms an interfacial defect layer of nanometer-thin disordered lithium at the Li–SE interfaces. Further, this interfacial defect Li layer is highly detrimental, leading to interfacial failure such as pore formation and contact loss during Li stripping. By systematically studying and comparing incoherent, coherent, and semi-coherent Li–SE interfaces, we find that the interface with good lattice coherence has reduced Li defects at the interface and has suppressed interfacial failure during Li cycling. Our finding discovered the critical roles of atomistic lithium defects at interfaces for the interfacial failure of Li metal anode, and motivates future atomistic-level interfacial engineering for Li metal anode in solid-state batteries.

25 ENERGY STORAGE↗
DOE OSTI2025

Compactly‐Supported Nonstationary Kernels for Computing Exact Gaussian Processes on Big Data

The Gaussian process (GP) is a widely used method for analyzing large-scale data sets, including spatio-temporal measurements of nonlinear processes that are now commonplace in the environmental sciences. Traditional implementations of GPs involve stationary kernels (also termed covariance functions) that limit their flexibility, and exact methods for inference that prevent application to data sets with more than about 10,000 points. Modern approaches to address stationarity assumptions generally fail to accommodate large data sets, while all attempts to address scalability focus on approximating the Gaussian likelihood, which can involve subjectivity and lead to inaccuracies. In this work, we explicitly derive an alternative kernel that can discover and encode both sparsity and nonstationarity. We embed the kernel within a fully Bayesian GP model and leverage high-performance computing resources to enable the analysis of massive data sets. We demonstrate the favorable performance of our novel kernel relative to existing exact and approximate GP methods across a variety of synthetic data examples. Furthermore, we conduct space–time prediction based on more than 1 million measurements of daily maximum temperature and verify that our results outperform state-of-the-art methods in the Earth sciences. More broadly, having access to exact GPs that use ultra-scalable, sparsity-discovering, nonstationary kernels allows GP methods to truly compete with a wide variety of machine learning methods.

Gaussian processes↗
DOE OSTI2024

Highly accelerated life testing (HALT): A review from a statistical perspective

Despite its use in one form or another for at least four decades, HALT and related techniques [e.g., highly accelerated-stress screening (HASS) and stress audits (HASA)] are not well understood within the statistical community and remain controversial. This largely reflects a conflict in motivation between engineers, testing under harsh conditions to discover and eliminate failure modes, and statisticians, taking a more cautious approach to develop quantitative estimates of parameters such as mean time between failures (MTBF). Here, this review article will clarify HALT concepts and methods and explain where it fits within the universe of methods that involve the application of accelerating factors to compress the time required to evaluate or enhance product reliability. A major distinction is between methods such as HALT, a high-stress test-analyze-fix-test iterative process directed at improving reliability by discovering and fixing weak points in a design, and quantitative accelerated life testing (QALT), whose goal is the estimation of product life for a fixed design. We discuss methods such as physics of failure that offer some hope of bridging the gap between the qualitative nature of HALT, and purely quantitative statistical methods. We present a variety of engineering applications of HALT including metal fatigue, piping and pressure vessels, structural damage, radiation damage, and rotating machinery. We also discuss potential synergies between HALT and QALT, such as rapid identification, through HALT, of failure modes requiring quantitative analysis. For further study, extensive references to the applicable literature are provided as well as an appendix that describes related methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

The Baker-Coon-Romans N -point amplitude and an exact field theory limit of the Coon amplitude

We study the N-point Coon amplitude discovered first by Baker and Coon in the 1970s and then again independently by Romans in the 1980s. This Baker-Coon-Romans (BCR) amplitude retains several properties of tree-level string amplitudes, namely duality and factorization, with a q-deformed version of the string spectrum. Although the formula for the N-point BCR amplitude is only valid for q > 1, the four-point case admits a straightforward extension to all q ≥ 0 which reproduces the usual expression for the four-point Coon amplitude. At five points, there are inconsistencies with factorization when pushing q < 1. Despite these issues, we find a new relation between the five-point BCR amplitude and Cheung and Remmen’s four-point basic hypergeometric amplitude, placing the latter within the broader family of Coon amplitudes. Finally, we compute the q → ∞ limit of the N-point BCR amplitudes and discover an exact correspondence between these amplitudes and the field theory amplitudes of a scalar transforming in the adjoint representation of a global symmetry group with an infinite set of non-derivative single-trace interaction terms. This correspondence at q = ∞ is the first definitive realization of the Coon amplitude (in any limit) from a field theory described by an explicit Lagrangian.

1/N Expansion↗
DOE OSTI2025

Neural Active Manifolds: Nonlinear Dimensionality Reduction for Uncertainty Quantification

We present a new approach for nonlinear dimensionality reduction, specifically designed for computationally expensive mathematical models. We leverage autoencoders to discover a one-dimensional neural active manifold (NeurAM) capturing the model output variability, through the aid of a simultaneously learnt surrogate model with inputs on this manifold. Our method only relies on model evaluations and does not require the knowledge of gradients. The proposed dimensionality reduction framework can then be applied to assist outer loop many-query tasks in scientific computing, like sensitivity analysis and multifidelity uncertainty propagation. In particular, we prove, both theoretically under idealized conditions, and numerically in challenging test cases, how NeurAM can be used to obtain multifidelity sampling estimators with reduced variance by sampling the models on the discovered low-dimensional and shared manifold among models. Several numerical examples illustrate the main features of the proposed dimensionality reduction strategy and highlight its advantages with respect to existing approaches in the literature.

Autoencoders↗
DOE OSTI2022

Radiochronometric analysis of an historic Cs-137 activity standard

In this work, a 137 Cs activity standard discovered during routine inventory was analyzed to determine its model age using radiochronometry. The aqueous activity standard was separated using an established separation method that employs commercially available Sr resin. The method was also tested against a nuclear forensics reference material developed specifically for benchmarking 137 Cs radiochronometry methods. Results of the analyses showed good agreement between the results and the certified values of the reference material. Analysis of the discovered activity standard were also in good agreement with the activity certification date, though uncertainty was higher due to natural Ba contaminating the sample and the stable 133 Cs used as a carrier during the standard’s production.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2023

Crystal chemistry at high pressure

The chemistry we are taught in school, and we experience in our daily existence occurs at 1 atm. However, pressure spans an astounding 62 orders of magnitude in the Universe in going from the void of interstellar space to the crushing conditions at the center of a neutron star. The way in which pressure affects chemistry is important for Earth and planetary sciences, materials science, in understanding the extreme conditions experienced in nuclear explosions, and it may be key in addressing the future energy needs of our society. In this article we outline how the often neglected pressure variable affects chemistry, beginning from the way in which atomic energy levels are altered. This lays the foundation for understanding the unique crystal and electronic structures that emerge when matter is squeezed, as well as pressure’s effect on chemical reactivity. Finally, we give an overview of the main concepts behind conventional, or phonon-mediated, superconductivity, and describe how the pressure variable may be key in discovering and designing light-element based materials whose superconducting critical temperatures approach room temperature. Here, we discuss some of the main families of superconducting hydrides that have been predicted computationally, and the experimental successes in this exciting and rapidly developing field.

Chemical bonding↗
DOE OSTI2022

Machine-learning enabled thermodynamic model for the design of new rare-earth compounds

We employ a descriptor based machine-learning approach to assess the effect of chemical alloying on formation-enthalpy of rare-earth intermetallics. Application of machine-learning approaches in rare-earth intermetallic design have been sparse due to limited availability of reliable datasets. In this work, we developed an ‘in-house’ rare-earth database with more than 600 + compounds, each entry was populated with formation enthalpy and related atomic features using high-throughput density-functional theory (DFT). The SISSO (sure independence screening and sparsifying operator) based machine-learning method with meaningful atomic features was used for training and testing the formation enthalpies of rare earth compounds. The complex lattice function coupled with the machine-learning model was used to explore the effect of transition metal alloying on the energy stability of Ce based cubic Laves phases (MgCu 2 type). The SISSO predictions show good agreement with high-fidelity DFT calculations and X-ray powder diffraction measurements. Our study provides quantitative guidance for compositional considerations within a machine-learning model and discovering new metastable materials. The electronic-structure of Ce-Fe-Cu based compound was also analyzed to get an in-depth understanding of the electronic origin of phase stability. The interpretable analytical models in combination with density-functional theory and experiments provide a fast and reliable design guide for discovering technologically useful materials.

36 MATERIALS SCIENCE↗