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At least 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Crystallography companion agent for high-throughput materials discovery

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

36 MATERIALS SCIENCE↗
DOE OSTI2023

Oak Ridge National Laboratory Annual Sustainability Report 2023

ORNL, managed under contract by UT-Battelle LLC, is DOE’s largest science and energy laboratory and, as such, executes the widest range of mission capabilities. Diverse expertise spans a broad range of scientific and engineering disciplines, enabling research and science achievements to accelerate the delivery of solutions to the marketplace. ORNL supports DOE’s national missions of scientific discovery, clean energy, and security. To execute these activities, ORNL has grown significantly over 80 years of continuous operations, consisting of facilities with commissioning dates ranging from the 1940s to the present—an extraordinary set of distinctive scientific facilities and equipment. The complexities of such a variety of facilities require teamwork among divisions, a wide variety of conservation projects, and creative strategies to achieve the desired energy and water savings. Such a diverse and unique set of major facilities, totaling over 5.5 million square feet, with 6,000 employees, requires an innovative plan to accomplish advancements in operational efficiencies. ORNL is tasked with the management of an extraordinary set of distinctive scientific facilities and equipment for DOE. ORNL is mission-driven, and its mission has grown substantially over the decades. ORNL’s core research capabilities provide broad science and technology support for DOE in the areas of energy, environment, and national security. Currently, ORNL is a world leader in materials, neutron, and nuclear science and engineering, and in high-performance computing and data analytics. ORNL’s vast portfolio of research facilities must be maintained and carefully upgraded to protect the nation’s investment in scientific analysis. The goal of sustainable and resilient operations is to enable more effective execution of ORNL’s science and technology mission. Sustainable operational practices and enhanced resilience strive for excellent results while remaining diligent in energy conservation, environmental stewardship, asset management, and community engagement. The Sustainable ORNL Program (Sustainable ORNL) Continuous improvements in operational and business processes must be integrated into the fabric of the ORNL culture to maximize the return from the investment made in modernizing facilities and equipment. The Sustainable ORNL program promotes the legacy of system-wide best practices, management commitment, and employee engagement that will lead ORNL into a future of efficient, resilient, and sustainable operations. ORNL leadership and Sustainable ORNL champions receive regular status reports on the progress of each project and focus area (i.e., roadmap) and periodic summary reports. More information can be found at the program’s website. The Sustainable ORNL roadmap structure endorses 15 vital roadmaps. The figure below summarizes the current project assignments and demonstrates that each project contributes to the wellbeing of the whole. Continuous employee engagement and regular status reports confirm the ideals of the program. The roadmap structure is not static; as the science mission advances and the needs of the organization evolve, the Sustainable ORNL roadmap structure elements are modified to align with developing priorities. In 2022, Sustainable ORNL made roadmap changes to better align ORNL to support new federal requirements that have been issued.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Real-Time Artificial Intelligence for Particle Reconstruction and Higgs Physics

With the discovery of the Higgs boson at the CERN LHC, the world's highest-energy particle accelerator complex, scientists have acquired an important tool to study the fundamental building blocks of the universe. Precision measurements of Higgs bosons produced with large momentum allow for unique insights into the structure of the interactions of the Higgs boson with other particles that may shed light on physics beyond the standard model. While experimentally challenging, exploring such interactions with novel artificial intelligence (AI) methods can advance our understanding of the Higgs sector, including the Higgs boson's self-interaction. Moreover, the LHC is undergoing a major upgrade to further increase its particle collision rate and thereby operate for an additional decade. The experimental detectors at the upgraded facility must process at least a factor of ten more data at rates of hundreds of terabytes per second all under challenging conditions. New AI techniques are required to reconstruct and select, or trigger on, the most physics-sensitive events in real-time to handle the resulting avalanche of data. The proposed research will achieve the goals of the LHC program at the CMS experiment by developing a sub-microsecond event reconstruction system using real-time AI algorithms that employ field-programmable gate array technologies. By harnessing sophisticated AI techniques, this research focuses on measuring the production of Higgs bosons at large momentum while enhancing particle reconstruction methods in the trigger and beyond. Overall, the proposed research has broader implications for the use of AI in resource-constrained, low-latency embedded applications across all fields of science.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Approximate Inverse Chain Preconditioner: Iteration Count Case Study for Spectral Support Solvers

As the growing availability of computational power slows, there has been an increasing reliance on algorithmic advances. However, faster algorithms alone will not necessarily bridge the gap in allowing computational scientists to study problems at the edge of scientific discovery in the next several decades. Often, it is necessary to simplify or precondition solvers to accelerate the study of large systems of linear equations commonly seen in a number of scientific fields. Preconditioning a problem to increase efficiency is often seen as the best approach; yet, preconditioners which are fast, smart, and efficient do not always exist. Following the progress of [1], we present a new preconditioner for symmetric diagonally dominant (SDD) systems of linear equations. These systems are common in certain PDEs, network science, and supervised learning among others. Based on spectral support graph theory, this new preconditioner builds off of the work of [2], computing and applying a V-cycle chain of approximate inverse matrices. This preconditioner approach is both algebraic in nature as well as hierarchically-constrained depending on the condition number of the system to be solved. Due to its generation of an Approximate Inverse Chain of matrices, we refer to this as the AIC preconditioner. We further accelerate the AIC preconditioner by utilizing precomputations to simplify setup and multiplications in the con-text of an iterative Krylov-subspace solver. While these iterative solvers can greatly reduce solution time, the number of iterations can grow large quickly in the absence of good preconditioners. Initial results for the AIC preconditioner have shown a very large reduction in iteration counts for SDD systems as compared to standard preconditioners such as Incomplete Cholesky (ICC) and Multigrid (MG). We further show significant reduction in iteration counts against the more advanced Combinatorial Multigrid (CMG) preconditioner. We have further developed no-fill sparsification techniques to ensure that the computational cost of applying the AIC preconditioner does not grow prohibitively large as the depth of the V-cycle grows for systems with larger condition numbers. Our numerical results have shown that these sparsifiers maintain the sparsity structure of our system while also displaying significant reductions in iteration counts.1 2

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Approximate Inverse Chain Preconditioner: Iteration Count Case Study for Spectral Support Solvers

As the growing availability of computational power slows, there has been an increasing reliance on algorithmic advances. However, faster algorithms alone will not necessarily bridge the gap in allowing computational scientists to study problems at the edge of scientific discovery in the next several decades. Often, it is necessary to simplify or precondition solvers to accelerate the study of large systems of linear equations commonly seen in a number of scientific fields. Preconditioning a problem to increase efficiency is often seen as the best approach; yet, preconditioners which are fast, smart, and efficient do not always exist. Following the progress of [1], we present a new preconditioner for symmetric diagonally dominant (SDD) systems of linear equations. These systems are common in certain PDEs, network science, and supervised learning among others. Based on spectral support graph theory, this new preconditioner builds off of the work of [2], computing and applying a V-cycle chain of approximate inverse matrices. This preconditioner approach is both algebraic in nature as well as hierarchically-constrained depending on the condition number of the system to be solved. Due to its generation of an Approximate Inverse Chain of matrices, we refer to this as the AIC preconditioner. We further accelerate the AIC preconditioner by utilizing precomputations to simplify setup and multiplications in the con-text of an iterative Krylov-subspace solver. While these iterative solvers can greatly reduce solution time, the number of iterations can grow large quickly in the absence of good preconditioners. Initial results for the AIC preconditioner have shown a very large reduction in iteration counts for SDD systems as compared to standard preconditioners such as Incomplete Cholesky (ICC) and Multigrid (MG). We further show significant reduction in iteration counts against the more advanced Combinatorial Multigrid (CMG) preconditioner. We have further developed no-fill sparsification techniques to ensure that the computational cost of applying the AIC preconditioner does not grow prohibitively large as the depth of the V-cycle grows for systems with larger condition numbers. Our numerical results have shown that these sparsifiers maintain the sparsity structure of our system while also displaying significant reductions in iteration counts.1 2

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1985

Giant molecular clouds as regions of particle acceleration

One of the most interesting results of investigations carried out on the satellites SAS-II and COS-B is the discovery of unidentified discrete gamma sources. Possibly a considerable part of them may well be giant molecular clouds. Gamma emission from clouds is caused by the processes with participation of cosmic rays. The estimation of the cosmic ray density in clouds has shown that for the energy E approx. = I GeV their density can 10 to 1000 times exceed the one in intercloud space. We have made an attempt to determine the mechanism which could lead to the increase in the cosmic ray density in clouds.

Dogiel, V. A.↗
NASA NTRS1996

Grant Proposal for the Continuation of the Voyager Interstellar Mission: LECP Investigation

This proposal documents the plans of the Low Energy Charged Particle (LECP) investigation team for participation in NASA's Voyager Interstellar Mission (VIM) as the Voyager 1 and 2 spacecraft explore the outer reaches of the heliosphere and search for the termination shock and the heliopause. The proposal covers the four year period from 1 January 1997 to 31 December 2000. The LECP instruments on Voyager 1 and 2 measure in situ intensities of charged particles with energies from about 30 keV to 100 MeV for ions, and about 20 keV to greater than 10 MeV for electrons. The instruments provide detailed spectral, angular, and compositional information about the particles. Composition is available for greater than 200 keV/nuc using multi-parameter measurements. Angular information is obtained by a mechanically scanned platform that rotates at various commanded rates. Measurements of low energy ion and electron intensities versus time and spatial location within the heliosphere contain an abundance of information regarding various transport and acceleration processes on both local (approx. 1 hr, approx. 0.01 AU) and global (approx. 11 yrs, approx. 100 AU) scales. The LECP instruments provide unique observations of such dynamical processes, and we anticipate that it will return critical information regarding the boundaries of the heliosphere. Several recent and exciting discoveries based on LECP measurements emphasize the important role that low energy charged particle distributions play in physical processes in the interplanetary medium. Yet, at the same time, these discoveries also underscore the fact that our understanding of processes in the outer heliosphere is, in most cases, incomplete, and in others, only rudimentary at best. Among the discoveries referred to above are the following: (1) Shocks: Examination of greater than 30 keV ion intensities have revealed: (a) a total absence of acceleration beyond only -100-200 keV at a strong transient shock in May 1991 at 35 AU, despite an enhanced level of seed particles; (b) a large transient shock in September 1991 of global scale, with intensities of shock-accelerated ions greater than or equal to 30 keV to approx. 30 MeV showing complex, highly energy-dependent spatial evolution, and small-scale (approx. few gyroradii), often anisotropic, micro-structures; (c) recurrent intensity increases in greater than or equal to 30 keV to -few MeV ions, with structures that, in some cases, show no correlation with the associated corotating shock. (2) Superthermal ion pressure: A global merged interaction region with a leading shock, downstream of which the superthermal ion (greater than or equal to 30 keV to approx. 4 MeV) pressure is comparable to that of the thermal plasma, and the total particle pressure yields a plasma beta of order unity. (3) Pickup ions: Measurements of the C/O ratio within transient structures at 35-45 AU showing the first clear evidence that transient shocks can pre-accelerate interstellar pickup ions from approx. 1 keV/nuc to at least 1 MeV/nuc. (4) Seed particles: Injection of ions for acceleration to high energies at the termination shock is unlikely to be a problem, since interplanetary transient and recurrent shocks are continually accelerating ions, of solar wind or interstellar origin, to highly superthermal energies. (5) Precursor electrons: Ambient solar electrons (greater than or equal to few tens of keV) that exist in the outer heliosphere ca form a broad precursor, several days wide, that is upstream of the termination shock and potentially observable a few months prior to the shock crossing. (6) Solar wind velocity at Voyager 1: We can use LECP ion data to obtain the solar wind velocity at Voyager 1, enabling us to provide critical measurement of the plasma flow as we approach and encounter the termination shock and other regions (necessary due to the partial failure of the Voyager 1 PLS experiment). The work of the LECP investigator team during the VIM will include: (1) Continuing operations with regard to the receipt, processing, verification, cataloging, display, and distribution of the data from the LECP instruments on Voyager 1 and 2, (2) Monitoring the health and performance of the LECP instruments, and evaluating and characterizing the response of the LECP instruments to various energetic particle and plasma environments, (3) Participating in, and supporting Voyager Project planning exercises and other coordinated activities relevant to exploration of the outer heliosphere, (4) Developing analysis techniques and operational procedures suitable for searching for and characterizing the boundaries and unique regions of the outher heliosphere, (5) Continuing the preparation of data sets appropriate for submission to the National Space Sciences Data Center (NSSDC) and, where appropriate, the Planetary Data System (PDS), (6) Maintaining direct Web access to online LECP data through the JHU/APL Voyager LECP home page, (7) Performing scientific evaluations of the Voyager 1 and 2 LECP data sets in conjunction with other data sets and other investigators, with particular focus on the outer regions of the heliosphere, and (8) Publishing the results of these evaluations in the scientific literature and presenting the results in scientific conferences.

Krimigis, Stamatios M.↗
DOE OSTI2021

Particle Accelerators: Their Triumphant History and Uncertain Future

The history of particle physics can be considered nothing less than a huge triumph for science. Over the course of a little more than a century of effort, our understanding of the world of atomic and subatomic physics went from a vague understanding of atoms, to one that is much more detailed. Early in this hundred-year-long period, we learned about electrons (1897), then how they circle a dense nucleus (1911), followed by the discovery of the protons (1917) and neutrons (1932) that form the nucleus. From the 1930s onward, researchers used both cosmic rays and particle accelerators to discover antimatter (1932), and particles that don’t exist in atoms (e.g., the muon [1936] and neutrino [1956], as well as a huge number of others).

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Overview of H- radio frequency ion sources for particle accelerators

Particle accelerators are among the most important scientific tools of the modern era. Large accelerator complexes have supported scientific user facilities which have had an enormous societal impact spanning many decades and enabling the work of thousands of scientific users worldwide contributing to many Nobel prizes in physics, biology and chemistry [1]. Many of the large hadron facilities employ accelerator complexes which include cyclotrons, synchrotrons, storage rings, linear or tandem accelerators and deliver ion beams of very high-intensity and/or very high-energy to their user facilities. These accelerator complexes require the injection of high-intensity beams of ions which are produced within an ion source and formed within a plasma or by bombardment of a surface [2]. Increasingly, RF systems are being utilized, to generate these ion-rich plasmas due to their high reliability and minimal use of consumable components. This report will first discuss the basic mechanisms of ion formation and plasma generation as well as some specifics of RF/microwave generators, matching circuits and plasma coupling structures typically employed. A detailed discussion will then be given of the RF-driven negative ion source systems employed by US Spallation Neutron Source as well as those used in similar facilities located around the globe. REFERENCES [1]Vladimir Shiltsev, “Particle beams behind physics discoveries”, Physics Today 73, Issue 4, 32 (2020) [2]Robert Welton et al, “Negative hydrogen ion sources for particle accelerators: Sustainability issues and recent improvements in long-term operations”, Journal of Physics Conf Series, 2244 012045

Welton, Robert F.↗
DOE OSTI2023

Artificial Intelligence-Enhanced, Multi-Level, Modular System Design

As Moore’s Law and Dennard Scaling come to an end, it is becoming increasingly important to develop non-von Neumann computing architectures that can perform low-power computing in the domains of scientific computing, artificial intelligence, embedded systems, and edge computing. Next-generation computing technologies, such as neuromorphic computing and quantum computing, have the potential to revolutionize computing. However, in order to make progress in these fields, it is necessary to fundamentally change the current computing paradigm by codesigning systems across all system level, from materials to software. Because skilled labor is limited in the field of next-generation computing, we are developing artificial intelligence-enhanced tools to automate the codesign and co-discovery of next-generation computers. Here, we develop a method called Modular and Multi-level MAchine Learning (MAMMAL) which is able to perform analog codesign and co-discovery across multiple system levels, spanning devices to circuits. We prototype MAMMAL by using it to design simple passive analog low-pass filters. We also explore methods to incorporate uncertainty quantification into MAMMAL and to accelerate MAMMAL by using emerging technologies, such as crossbar arrays. Ultimately, we believe that MAMMAL will enable rapid progress in developing next-generation computers by automating the codesign and co-discovery of electronic systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Overestimated prediction using polygenic prediction derived from summary statistics

When polygenic risk score (PRS) is derived from summary statistics, independence between discovery and test sets cannot be monitored. We compared two types of PRS studies derived from raw genetic data (denoted as rPRS) and the summary statistics for IGAP (sPRS). Two variables with the high heritability in UK Biobank, hypertension, and height, are used to derive an exemplary scale effect of PRS. sPRS without APOE is derived from International Genomics of Alzheimer’s Project (IGAP), which records ΔAUC and ΔR 2 of 0.051 ± 0.013 and 0.063 ± 0.015 for Alzheimer’s Disease Sequencing Project (ADSP) and 0.060 and 0.086 for Accelerating Medicine Partnership - Alzheimer’s Disease (AMP-AD). On UK Biobank, rPRS performances for hypertension assuming a similar size of discovery and test sets are 0.0036 ± 0.0027 (ΔAUC) and 0.0032 ± 0.0028 (ΔR 2 ). For height, ΔR 2 is 0.029 ± 0.0037. Considering the high heritability of hypertension and height of UK Biobank and sample size of UK Biobank, sPRS results from AD databases are inflated. Independence between discovery and test sets is a well-known basic requirement for PRS studies. However, a lot of PRS studies cannot follow such requirements because of impossible direct comparisons when using summary statistics. Thus, for sPRS, potential duplications should be carefully considered within the same ethnic group.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Revisiting Experimental Signatures of the Ponderomotive Force

The classical theory of single-electron dynamics in focused laser pulses is the foundation of both the relativistic ponderomotive force (RPF), which underlies models of laser-collective-plasma dynamics, and the discovery of novel strong-field radiation dynamics. Despite this bedrock importance, consensus eludes the community as to whether acceleration of single electrons in vacuum has been observed in experimental conditions. We analyze an early experiment on the RPF with respect to several features that were neglected in modeling and that can restore consistency between theory predictions and experimental data. The right or wrong pulse profile function, laser parameters, or initial electron distribution can each make or break the agreement between predictions and data. The laser phase at which the electron’s interaction with the pulse begins has a large effect, explaining why much larger energies are achieved by electrons liberated in the focal region by photoionization from high-Z atoms and by electrons ejected from a plasma mirror. Finally, we compute the difference in a typical electron spectrum arising from fluctuating focal spot size in state-of-the-art ultra-relativistic laser facilities. Our results emphasize the importance of thoroughly characterizing laser parameters in order to achieve quantitatively accurate predictions and the precision required for discovery science.

Hegelich, Bjorn Manuel↗
DOE OSTI2022

Sub-threshold ultrafast one-photon photoemission from a Cu(111) photocathode

Single-photon photoemission of electrons for incident photon energies below the surface work function is reported for a single-crystal Cu(111) photocathode. Spectral characterization of the quantum efficiency and mean transverse energy of the emitted electrons is shown to be consistent with emission from a thermalized hot electron distribution photoexcited on sub-picosecond time scales into an upper conduction band at the L-point of the Brillouin zone of copper. To our knowledge, this is the first time that such excited-state thermionic emission has been observed from a planar metal photocathode, and then from a commonly used photocathode material. The results, therefore, indicate the potential importance of such band structure dependent sub-threshold photoemission mechanisms on the performance of photocathodes employed for the generation of short electron pulses using sub-picosecond laser pulses. Consequently, the combined experimental and theoretical work presented in this paper contributes to solid-state photocathode-based research aimed at our understanding and selection (or discovery) of high brightness photo-electron sources required for many electron-based diffraction, imaging, and accelerator applications.

43 PARTICLE ACCELERATORS↗
NASA NTRS2011

From SOHO to STEREO: Understanding Propagation of Coronal Mass Ejections

Direct comparison between coronal mass ejections (CMEs) from near the Sun and their solar wind counterparts became possible roughly a decade after the discovery of CMEs (Lindsay et aL 1999). This comparison revealed that fast CMEs decelerate and slow CMEs accelerate due to the interaction with the solar wind. Gopalswamy et al (2000) quantified this interaction as an interplanetary acceleration which is useful in predicting the arrival time and speed of CMEs at 1 AU. The interplanetary acceleration is essentially due to the aerodynamic drag between the CME and the solar wind because the propelling force and the solar gravity are effective only near the Sun. Combined remote-sensing and in situ observations from SOHO and Wind/ACE have helped us estimate the influence of the solar wind on the propagation of CMEs. However, these measurements have severe limitations because the remote sensed and in-situ observations correspond to different portions of the CME. Furthermore, the true speeds of Earth-directed CMEs cannot be measured accurately from a spacecraft located along the Sun-Earth line. There have been attempts to model the CME as a cone and get the space speed of the CME, which did improve the travel time predictions. Instruments on board the Solar Terrestrial Relations Observatory (STEREO) mission were able to provide observations of Earth-arriving CMEs without projection effects, while the same CMEs were observed at Sun-Earth L1 by Wind and ACE spacecraft. The quadrature between STEREO and L1 spacecraft presented an ideal situation to study the interplanetary evolution of CMEs and test earlier model results. The quadrature observations did improve the CME travel time predictions, but additional factors such as the unusually slow solar wind, CME cannibalism, and coronal-hole deflection need to be considered to reconcile the difference between observed and predicted travel times. This point is illustrated using the 2011 February 15 CME

Gopalswamy, Natchimuthuk↗
DOE OSTI2024

Technological developments and accelerator improvements for the FRIB beam power ramp-up

The Facility for Rare Isotope Beams (FRIB) began operation with 1 kW beam power for scientific users in May 2022 upon completion of 8 years of project construction. The ramp-up to the ultimate beam power of 400 kW, planned over a 6-year period, will enable the facility to reach its full potential for scientific discovery in isotope science and applications. In December 2023, a record-high beam power of 10.4 kW uranium was delivered to the target. Technological developments and accelerator improvements are being made over the entire facility and are key to completion of the power ramp-up. Major technological developments entail the phased deployment of high-power beam-intercepting systems, including the charge strippers, the charge selection systems, the production target, and the beam dump, along with support systems, including non-conventional utilities (NCU) and remote handling facilities. Major accelerator improvements include renovations to aging legacy systems associated with experimental beam lines and system automation for improved operational efficiency and better machine availability. Experience must be gained to safely handle the increased radiological impacts associated with high beam power; extensive machine studies and advanced beam tuning procedures are needed to minimize uncontrolled beam losses for the desired operating conditions. This paper discusses the technological developments and accelerator improvements with emphasis on major R&D efforts.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Universal Electronic‐Structure Relationship Governing Intrinsic Magnetic Properties in Permanent Magnets

An electronic-structure-centered perspective is presented on permanent-magnet (PM) design, highlighting two key levers, that is, saturation magnetization (M s ), governed by 3d-band filling and exchange physics, and magnetocrystalline anisotropy energy (MAE), arising from spin-orbit coupling (SOC) on anisotropic orbital populations. Reviewing current practices, including DFT-based MAE/J ij extraction, atomistic-spin and micromagnetic modeling, and high-throughput machine learning (ML) pipelines, three bottlenecks limiting predictive discovery is identified that is i) electronic-structure accuracy for small MAE (sensitive to functional choice, Hubbard U, and many-body effects), ii) finite-temperature and kinetic realism (phonon/magnon renormalization, ordering kinetics), and iii) descriptor and multiscale decoupling (lack of SOC-weighted and orbital-resolved fingerprints). Deep dives into the electronic-structure of Nd─Fe─B and Fe─N show how these fingerprints govern magnetic performance, motivating DFT- and quantum-mechanics-based descriptors for discovery. Unbiased, structure-driven exploration, coupled with high-throughput simulations, ML, generative AI, and reasoning models, accelerates candidate identification and propagates insights across scales. Addressing supply-chain risks, on future needs of designing “critical-element-free” magnets with tailored microstructure and high energy products is emphasized. By integrating electronic fingerprints, AI reasoning, and multiscale modeling, a practical roadmap is provided for rare-earth-lean or rare-earth free, high-performance, sustainable PMs.

Singh, Prashant [Ames Laboratory, and Iowa State U↗
DOE OSTI2025

Decoding crops one cell at a time: from cell atlases to single-cell genetics

Understanding the mechanisms underlying key agricultural traits remains a central challenge in crop research, but recent advances in technologies are providing powerful tools to address this issue. Among these, single-cell and spatial transcriptomics have revealed tissue heterogeneity and spatial organization, offering unique insights into cellular gene expression dynamics and the coordinated activity of multiple cell types. These approaches help uncover how specific cell types contribute to agricultural traits and refine candidate loci lists through integration with trait-associated loci. Additionally, single-cell and spatial transcriptomics have the potential to serve as cell-level readout platforms integrating cellular perturbations, enabling high-throughput discovery of causal relationships between genotype and gene expression at the cellular level in plants. Successful implementation will accelerate the identification of key genetic variants for crop improvement. Furthermore we review lessons learned from application of single-cell screening in mammalian cells, highlight major technical and biological barriers to its use in plants, and outline potential strategies to overcome these challenges. Together, the widespread application and integration of single-cell and spatial transcriptomics with other technologies enable not only the descriptive cataloging of cell states but also the causal interrogation of sequence functions and regulatory networks at cell type resolution, ultimately advancing gene function studies and accelerating crop improvement.

Cellular heterogeneity↗