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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A GPT‐4 Reticular Chemist for Guiding MOF Discovery**

Abstract We present a new framework integrating the AI model GPT‐4 into the iterative process of reticular chemistry experimentation, leveraging a cooperative workflow of interaction between AI and a human researcher. This GPT‐4 Reticular Chemist is an integrated system composed of three phases. Each of these utilizes GPT‐4 in various capacities, wherein GPT‐4 provides detailed instructions for chemical experimentation and the human provides feedback on the experimental outcomes, including both success and failures, for the in‐context learning of AI in the next iteration. This iterative human‐AI interaction enabled GPT‐4 to learn from the outcomes, much like an experienced chemist, by a prompt‐learning strategy. Importantly, the system is based on natural language for both development and operation, eliminating the need for coding skills, and thus, make it accessible to all chemists. Our collaboration with GPT‐4 Reticular Chemist guided the discovery of an isoreticular series of MOFs, with each synthesis fine‐tuned through iterative feedback and expert suggestions. This workflow presents a potential for broader applications in scientific research by harnessing the capability of large language models like GPT‐4 to enhance the feasibility and efficiency of research activities.

Chemistry↗
DOE OSTI2024

An evaluation of GPT models for phenotype concept recognition

Clinical deep phenotyping and phenotype annotation play a critical role in both the diagnosis of patients with rare disorders as well as in building computationally-tractable knowledge in the rare disorders field. These processes rely on using ontology concepts, often from the Human Phenotype Ontology, in conjunction with a phenotype concept recognition task (supported usually by machine learning methods) to curate patient profiles or existing scientific literature. With the significant shift in the use of large language models (LLMs) for most NLP tasks, we examine the performance of the latest Generative Pre-trained Transformer (GPT) models underpinning ChatGPT as a foundation for the tasks of clinical phenotyping and phenotype annotation. The experimental setup of the study included seven prompts of various levels of specificity, two GPT models (gpt-3.5-turbo and gpt-4.0) and two established gold standard corpora for phenotype recognition, one consisting of publication abstracts and the other clinical observations. The best run, using in-context learning, achieved 0.58 document-level F1 score on publication abstracts and 0.75 document-level F1 score on clinical observations, as well as a mention-level F1 score of 0.7, which surpasses the current best in class tool. Without in-context learning, however, performance is significantly below the existing approaches. Our experiments show that gpt-4.0 surpasses the state of the art performance if the task is constrained to a subset of the target ontology where there is prior knowledge of the terms that are expected to be matched. While the results are promising, the non-deterministic nature of the outcomes, the high cost and the lack of concordance between different runs using the same prompt and input make the use of these LLMs challenging for this particular task.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Nucleic acids encoding plant glutamine phenylpyruvate transaminase (GPT) and uses thereof

Glutamine phenylpyruvate transaminase (GPT) proteins, nucleic acid molecules encoding GPT proteins, and uses thereof are disclosed. Provided herein are various GPT proteins and GPT gene coding sequences isolated from a number of plant species. As disclosed herein, GPT proteins share remarkable structural similarity within plant species, and are active in catalyzing the synthesis of 2-hydroxy-5-oxoproline (2-oxoglutaramate), a powerful signal metabolite which regulates the function of a large number of genes involved in the photosynthesis apparatus, carbon fixation and nitrogen metabolism.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

MaTableGPT: GPT‐Based Table Data Extractor from Materials Science Literature

Abstract Efficiently extracting data from tables in the scientific literature is pivotal for building large‐scale databases. However, the tables reported in materials science papers exist in highly diverse forms; thus, rule‐based extractions are an ineffective approach. To overcome this challenge, the study presents MaTableGPT, which is a GPT‐based table data extractor from the materials science literature. MaTableGPT features key strategies of table data representation and table splitting for better GPT comprehension and filtering hallucinated information through follow‐up questions. When applied to a vast volume of water splitting catalysis literature, MaTableGPT achieves an extraction accuracy (total F1 score) of up to 96.8%. Through comprehensive evaluations of the GPT usage cost, labeling cost, and extraction accuracy for the learning methods of zero‐shot, few‐shot, and fine‐tuning, the study presents a Pareto‐front mapping where the few‐shot learning method is found to be the most balanced solution owing to both its high extraction accuracy (total F1 score >95%) and low cost (GPT usage cost of 5.97 US dollars and labeling cost of 10 I/O paired examples). The statistical analyses conducted on the database generated by MaTableGPT revealed valuable insights into the distribution of the overpotential and elemental utilization across the reported catalysts in the water splitting literature.

Yi, Gyeong Hoon [Computational Science Research Ce↗
DOE OSTI2023

Shaping the Water-Harvesting Behavior of Metal–Organic Frameworks Aided by Fine-Tuned GPT Models

Here, we construct a data set of metal–organic framework (MOF) linkers and employ a fine-tuned GPT assistant to propose MOF linker designs by mutating and modifying the existing linker structures. This strategy allows the GPT model to learn the intricate language of chemistry in molecular representations, thereby achieving an enhanced accuracy in generating linker structures compared with its base models. Aiming to highlight the significance of linker design strategies in advancing the discovery of water-harvesting MOFs, we conducted a systematic MOF variant expansion upon state-of-the-art MOF-303 utilizing a multidimensional approach that integrates linker extension with multivariate tuning strategies. We synthesized a series of isoreticular aluminum MOFs, termed Long-Arm MOFs (LAMOF-1 to LAMOF-10), featuring linkers that bear various combinations of heteroatoms in their five-membered ring moiety, replacing pyrazole with either thiophene, furan, or thiazole rings or a combination of two. Beyond their consistent and robust architecture, as demonstrated by permanent porosity and thermal stability, the LAMOF series offers a generalizable synthesis strategy. Importantly, these 10 LAMOFs establish new benchmarks for water uptake (up to 0.64 g g –1 ) and operational humidity ranges (between 13 and 53%), thereby expanding the diversity of water-harvesting MOFs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Combining Particle Tracking with Electromagnetic Radiation Showers: Merging GPT and Geant4 with Visualization

Field emitted electrons can seriously affect the operation of high-field, high-duty factor electron accelerators. Accelerated field emission can result in high average power beams which can radiation damage beamline components. In addition, localized losses generate thermal hot spots whose outgassing degrades the ultra-high vacuum required in photoinjectors and cryomodules. However, despite their importance, the effects of field emission are rarely included in the design and engineering of electron injectors. This work attempts to remedy this situation by combining two well-known and well-documented programs, GPT and Geant4, to track electrons and their losses in an injector beamline. This paper describes a system of programs which simulates electron paths and losses along the beamline. In addition, the tracking results can be zoomed and panned along and about the beampipe envelope using an open-source 3D CAD program. The scattering albedo calculations of the combined program, GPT-Geant4, are shown to be in good agreement with the literature albedos. The paper concludes with a dark current simulation for the LCLS-II injector from the cathode to the collimator at 1.5 m from the cathode.

Dowell, David H.↗
DOE OSTI2022

Implementation and testing of the GPT sensitivities in TRIPOLI-4

Computing the sensitivity of given reactor parameters with respect to nuclear data is mandatory in key applications such as the assessment of reactor safety or core design. In recent years, the development of Monte Carlo methods capable of estimating sensitivity coefficients based on the Standard or Generalized Perturbation Theory (SPT and GPT, respectively) has attracted intensive research efforts, especially in view of having reference golden standard simulations to be compared to faster but approximate deterministic calculations. Following our previous implementation and validation of the SPT functionalities in the Monte Carlo TRIPOLI-4, devoted to the sensitivity of the k eigenvalue, in this work we detail the development of GPT methods for ratios of reaction rates, and validate them against a few relevant benchmark results. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Achieving GPT-4o level performance in astronomy with a specialized 8B-parameter large language model

AstroSage-Llama-3.1-8B is a domain-specialized natural-language AI assistant tailored for research in astronomy, astrophysics, cosmology, and astronomical instrumentation. Trained on the complete collection of astronomy-related arXiv papers from 2007 to 2024 along with millions of synthetically-generated question-answer pairs and other astronomical literature, AstroSage-Llama-3.1-8B demonstrates remarkable proficiency on a wide range of questions. AstroSage-Llama-3.1-8B scores 80.9% on the AstroMLab-1 benchmark, greatly outperforming all models—proprietary and open-weight—in the 8-billion parameter class, and performing on par with GPT-4o. This achievement demonstrates the potential of domain specialization in AI, suggesting that focused training can yield capabilities exceeding those of much larger, general-purpose models. AstroSage-Llama-3.1-8B is freely available, enabling widespread access to advanced AI capabilities for astronomical education and research.

AI assistant↗
DOE OSTI2026

?hem-GPT series of AI models

A suite of chemistry GPT models built using Transform Your World are available at DOI, from which one example is mirrored on Hugging Face. https://huggingface.co/asalij/Xhem-GPT-2

Salij, Andrew↗
DOE OSTI2021

Simulating Electron Impact Ionization using a General Particle Tracer (GPT) Custom Element

A new C++ custom element has been developed with the framework of General Particle Tracer (GPT) to simulate electron impact ionization of residual gas molecules. The custom element uses Monte-Carlo routines to determine both the ion production rate and the secondary electron kinetic energy based on user-defined gas densities and theoretical values for the ionization cross section and the secondary electron differential cross section. It then uses relativistic kinematics to track the secondary electron, the scattered electron, and the newly formed ion after ionization. The ion production rate and the secondary electron energy distribution determined by the custom element have been benchmarked against theoretical calculations and against simulations made using the simulation package IBSimu. While the custom element was originally built for particle accelerator simulations, it is readily extensible to other applications. The custom element will be described in detail and examples of applications at the Thomas Jefferson National Accelerator Facility will be presented for ion production in a DC high voltage photo-gun.

Yoskowitz, J. T.↗
DOE OSTI2022

Modeling a Nb3Sn Cryounit in GPT at UITF

Nb₃Sn is a prospective material for future superconducting RF (SRF) accelerator cavities. The material can achieve higher quality factors, higher temperature operation and potentially higher accelerating gradients (E_{acc} 96 MV/m) compared to conventional niobium. In this work, we performed modeling of the Upgraded Injector Test Facility (UITF) at Jefferson Lab utilizing newly constructed Nb₃Sn cavities. We studied the effects of the buncher cavity and varied the gun voltages from 200-500 keV. We have calibrated and optimized the SRF cavity gradients and phases for the Nb₃Sn five-cell cavities energy gains with the framework of General Particle Tracer (GPT). Our calculations show the beam goes cleanly through the unit. There is full energy gain out of the second SRF cavity but not from the first SRF cavity due to non-relativistic phase shifts.

Pokharel, S.↗