skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.


This content will become publicly available on December 18, 2025

Title: Hybrid-LLM-GNN: integrating large language models and graph neural networks for enhanced materials property prediction
This study combines Graph Neural Networks (GNNs) and Large Language Models (LLMs) to improve material property predictions. By leveraging both embeddings, this hybrid approach achieves up to a 25% improvement over GNN-only model in accuracy.  more » « less
Award ID(s):
2331329
PAR ID:
10638142
Author(s) / Creator(s):
; ; ; ; ; ; ;
Publisher / Repository:
Royal Society of Chemistry
Date Published:
Journal Name:
Digital Discovery
Volume:
4
Issue:
2
ISSN:
2635-098X
Page Range / eLocation ID:
376 to 383
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Data from: Stone and Wessinger 2023, "Ecological diversification in an adaptive radiation of plants: the role of de novo mutation and introgression"DOI: 10.1101/2023.11.01.565185The code used to conduct analyses from this study can be found here: https://github.com/benstemon/MBE-23-0936The raw sequencing reads generated from this study have been deposited on the SRA under Project number: PRJNA1057825This repository contains a README.md file, which contains information on all files included. 
    more » « less
  2. This artifact contains the source code for FlakeRake, a tool for automatically reproducing timing-dependent flaky-test failures. It also includes raw and processed results produced in the evaluation of FlakeRake   Contents:   Timing-related APIs that FlakeRake considers adding sleeps at: timing-related-apis Anonymized code for FlakeRake (not runnable in its anonymized state, but included for reference; we will publicly release the non-anonymized code under an open source license pending double-blind review): flakerake.tgz Failure messages extracted from the FlakeFlagger dataset: 10k_reruns_failures_by_test.csv.gz  Output from running isolated reruns on each flaky test in the FlakeFlager dataset: 10k_isolated_reruns_all_results.csv.gz (all test results summarized into a CSV), 10k_isolated_reruns_failures_by_test.csv.gz (CSV including just test failures, including failure messages), 10k_isolated_reruns_raw_results.tgz (includes all raw results from reruns, including the XML files output by maven) Output from running the FlakeFlagger replication study (non-isolated 10k reruns):flakeFlaggerReplResults.csv.gz (all test results summarized into a CSV), 10k_reruns_failures_by_test.csv.gz (CSV including just failures, including failure messages), flakeFlaggerRepl_raw_results.tgz (includes all raw results from reruns, including the XML files output by maven - this file is markedly larger than the 10k isolated reruns results because we ran *all* tests in this experiment, whereas the 10k isolated rerun experiment only re-ran the tests that were known to be flaky from the FlakeFlagger dataset). Output from running FlakeRake on each flaky test in the FlakeFlagger dataset: For bisection mode: results-bis.tgz For one-by-one mode: results-obo.tgz Scripts used to execute FlakeRake using an HPC cluster: execution-scripts.tgz Scripts used to execute rerun experiments using an HPC cluster: flakeFlaggerReplScripts.tgz Scripts used to parse the "raw" maven test result XML files in this artifact into the CSV files contained in this artifact: parseSurefireXMLs.tgz  Output from running FlakeRake in “reproduction” mode, attempting to reproduce each of the failures that matched the FlakeFlagger dataset (collected for bisection mode only): results-repro-bis.tgz Analysis of timing-dependent API calls in the failure inducing configurations that matched FlakeFlagger failures: bis-sleepyline.cause-to-matched-fail-configs-found.csv 
    more » « less
  3. Abstract To understand human language—both spoken and signed—the listener or viewer has to parse the continuous external signal into components. The question of what those components are (e.g., phrases, words, sounds, phonemes?) has been a subject of long‐standing debate. We re‐frame this question to ask: What properties of the incoming visual or auditory signal are indispensable to eliciting language comprehension? In this review, we assess the phenomenon of language parsing from modality‐independent viewpoint. We show that the interplay between dynamic changes in the entropy of the signal and between neural entrainment to the signal at syllable level (4–5 Hz range) is causally related to language comprehension in both speech and sign language. This modality‐independent Entropy Syllable Parsing model for the linguistic signal offers insight into the mechanisms of language processing, suggesting common neurocomputational bases for syllables in speech and sign language. This article is categorized under:Linguistics > Linguistic TheoryLinguistics > Language in Mind and BrainLinguistics > Computational Models of LanguagePsychology > Language 
    more » « less
  4. Key messages Grounding practices within the materiality of geography is an important technique for studying the complexity of digital phenomena.The DIGO (Discourses, Infrastructures, Groupings, and Outcomes) framework uses these categories to guide data selection for locating digital phenomenon in material geographies.This article applies the DIGO framework to blockchain (using data about tweets, miners, firms, and ICOs) to show how this digital practice connects to and across material geographies. 
    more » « less
  5. Summary Powdery mildew is an economically important disease caused byc. 1000 different fungal species.Erysiphe vacciniiis an emerging powdery mildew species that is impacting the blueberry industry. Once confined to North America,E. vacciniiis now spreading rapidly across major blueberry‐growing regions, including China, Morocco, Mexico, and the USA, threatening millions in losses.This study documents its recent global spread by analyzing both herbarium specimens, some over 150‐yr‐old, and fresh samples collected world‐wide.Our findings were integrated into a ‘living phylogeny’ via T‐BAS to simplify pathogen identification and enable rapid responses to new outbreaks. We identified 50 haplotypes, two primary introductions world‐wide, and revealed a shift from a generalist to a specialist pathogen.This research provides insights into the complexities of host specialization and highlights the need to address this emerging global threat to blueberry production. 
    more » « less