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Search Publications by: Brian DeCost (Fed)

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Displaying 1 - 25 of 36

Workshop Report on Autonomous Methodologies for Accelerating X-ray Measurements

November 5, 2024
Author(s)
Zachary Trautt, Austin McDannald, Brian DeCost, Howard Joress, A. Gilad Kusne, Francesca Tavazza, Tom Blanton
The National Institute of Standards and Technology and the International Centre for Diffraction Data co-hosted a workshop on 17-18 October 2023 to identify and prioritize the goals, challenges, and opportunities for critical and emerging technology needs

Driving U.S. Innovation in Materials and Manufacturing using AI and Autonomous Labs

August 14, 2024
Author(s)
Howie Joress, Zachary Trautt, Austin McDannald, Brian DeCost, A. Gilad Kusne, Francesca Tavazza
With the goal of advancing US competitiveness and excellence in the materials and manufacturing industries, we present our vision for the National Center for Autonomous Materials Science. The objective of this center is to enable and promote the use of

A reproducibility study of graph neural networks for materials property prediction

April 30, 2024
Author(s)
Kangming Li, Brian DeCost, Kamal Choudhary, Jason Hattrick-Simpers
Use of machine learning has been increasingly popular in materials science as data-driven materials discovery is becoming the new paradigm. Reproducibility of findings is paramount for promoting transparency and accountability in research and building

Accelerating Defect Predictions in Semiconductors Using Graph Neural Networks

March 27, 2024
Author(s)
Md. Habibur Rahman, Prince Gollapalli, Panayotis Manganaris, Satyesh Kumar Yadav, Ghanshyam Pilania, Arun Kumar Mannodi-Kanakkithodi, Brian DeCost, Kamal Choudhary
First principles computations reliably predict the energetics of point defects in semiconductors, but are constrained by the expense of using large supercells and advanced levels of theory. Machine learning models trained on computational data, especially

Structure-Aware GNN-Based Deep Transfer Learning Framework For Enhanced Predictive Analytics On Small Materials Data

January 2, 2024
Author(s)
Vishu Gupta, Kamal Choudhary, Brian DeCost, Francesca Tavazza, Carelyn E. Campbell, Wei-keng Liao, Alok Choudhary, Ankit Agrawal
Modern data mining methods have been demonstrated to be effective tools to comprehend and predict materials properties. An essential component in the process of materials discovery is to know which material(s) (represented by their composition and crystal

Flexible formulation of value for experiment interpretation and design

December 12, 2023
Author(s)
Matthew Carbone, Hyeong Jin Kim, Chandima Fernando, Shinjae Yoo, Daniel Olds, Howie Joress, Brian DeCost, Bruce D. Ravel, Yugang Zhang, Phillip Michael Maffettone
The challenge of optimal design of experiments pervades materials science, physics, chemistry, and biology. Bayesian optimization has been used to address this challenge but requires framing experimental campaigns through the lens of maximizing some

A Call for Caution in the Era of AI-Accelerated Materials Science

December 6, 2023
Author(s)
Kangming Li, Edward Kim, Yao Fehlis, Daniel Persaud, Brian DeCost, Michael Greenwood, Jason Hattrick-Simpers
It is safe to state that the field of matter has successfully entered the fourth paradigm, where machine learning and artificial intelligence (AI) are universally seen as useful, if not truly intelligent. AI's utilization is near-ubiquitous from the

Understanding and Leveraging Short Range Order in Compositionally Complex Alloys

November 20, 2023
Author(s)
Mitra Taheri, Elaf Anber, Annie Barnett, Nick Birbilis, Brian DeCost, Daniel Foley, Emily Holcombe, Jonathan Hollenbach, Howie Joress, Yevgeny Rakita, James Rondinelli, Nathan Smith, Michael Waters, Chris Wolverton
In this article, we review the opportunities and challenges associated with complex concentrated materials that exhibit short-range order. Although the presence of such phenomena has been theorized, accurate computational representation, characterization

Exploiting redundancy in large materials datasets for efficient machine learning with less data

November 10, 2023
Author(s)
Kamal Choudhary, Brian DeCost, Kangming Li, Daniel "Persaud ", Jason Hattrick-Simpers, Michael Greenwood
Extensive efforts to gather materials data have largely overlooked potential data redundancy. In this study, we present evidence of a significant degree of redundancy across multiple large datasets for various material properties, by revealing that up to

An Experimental High-Throughput to High-Fidelity Study Towards Discovering Al-Cr Containing Corrosion-Resistant Compositionally Complex Alloys.

November 8, 2023
Author(s)
Debashish Sur, Emily Holcombe, William Blades, Daniel Foley, Brian DeCost, Elaf Anber, Jason Hattrick-Simpers, Karl Sieradzki, Howie Joress, John Scully, Mitra Taheri
Compositionally complex alloys hold the promise of simultaneously attaining superior combinations of properties, such as corrosion resistance, light-weighting, and strength. Achieving this goal is a challenge due in part to a large number of possible

Recent progress in the JARVIS infrastructure for next-generation data-driven materials design

October 18, 2023
Author(s)
Daniel Wines, Ramya Gurunathan, Kevin Garrity, Brian DeCost, Adam Biacchi, Francesca Tavazza, Kamal Choudhary
The Joint Automated Repository for Various Integrated Simulations (JARVIS) infrastructure at NIST is a large-scale collection of curated datasets and tools with more than 80000 materials and millions of properties. JARVIS uses a combination of electronic

Why is EXAFS for complex concentrated alloys so hard? Challenges and opportunities for measuring ordering with X-ray absorption spectroscopy

October 16, 2023
Author(s)
Howie Joress, Bruce D. Ravel, Elaf Anber, Jonathan Hollenbach, Debashish Sur, Mitra Taheri, Brian DeCost
Short-range order (SRO) is a critical driver of properties (e.g., corrosion resistance and tensile strength) in multicomponent alloys such as complex concentrated alloys (CCAs). Extended X-ray absorption fine structure (EXAFS) is a powerful technique well

AutoEIS: automated Bayesian model selection and analysis for electrochemical impedance spectroscopy

August 9, 2023
Author(s)
Runze Zhang, Robert Black, Debashish Sur, Parisa Karimi, Kangming Li, Brian DeCost, John Scully, Jason Hattrick-Simpers
Electrochemical Impedance Spectroscopy (EIS) is a powerful tool for electrochemical analysis; however, its data can be challenging to interpret. Here, we introduce a new open-source tool named AutoEIS that assists EIS analysis by automatically proposing

AI for Materials

April 25, 2023
Author(s)
Debra Audus, Kamal Choudhary, Brian DeCost, A. Gilad Kusne, Francesca Tavazza, James A. Warren
The application of artificial intelligence (AI) methods to materials re- search and development (MR&D) is poised to radically reshape how materials are discovered, designed, and deployed into manufactured products. Materials underpin modern life, and

AtomVision: A machine vision library for atomistic images

March 1, 2023
Author(s)
Brian DeCost, Ramya Gurunathan, Adam Biacchi, Kamal Choudhary
Computer vision techniques have immense potential for materials design applications. In this work, we introduce an integrated and general-purpose AtomVision library that can be used to generate and curate microscopy image (such as scanning tunneling

Unified graph neural network force-field for the periodic table: solid state applications

February 23, 2023
Author(s)
Kamal Choudhary, Brian DeCost, Lily Major, Keith Butler, Jeyan Thiyagalingam, Francesca Tavazza
Classical force fields (FFs) based on machine learning (ML) methods show great potential for large scale simulations of solids. MLFFs have hitherto largely been designed and fitted for specific systems and are not usually transferable to chemistries beyond

Self-driving Multimodal Studies at User Facilities

January 22, 2023
Author(s)
Bruce D. Ravel, Phillip Michael Maffettone, Daniel Allan, Stuart Campbell, Matthew Carbone, Brian DeCost, Howie Joress, Dmitri Gavrilov, Marcus Hanwell, Joshua Lynch, Stuart Wilkins, Jakub Wlodek, Daniel Olds
Multimodal characterization is commonly required for understanding materials. User facilities possess the infrastructure to perform these measurements, albeit in serial over days to months. In this paper, we describe a unified multimodal measurement of a

Reproducible Sorbent Materials Foundry for Carbon Capture at Scale

September 22, 2022
Author(s)
Austin McDannald, Howie Joress, Brian DeCost, Avery Baumann, A. Gilad Kusne, Kamal Choudhary, Taner N. Yildirim, Daniel Siderius, Winnie Wong-Ng, Andrew J. Allen, Christopher Stafford, Diana Ortiz-Montalvo
We envision an autonomous sorbent materials foundry (SMF) for rapidly evaluating materials for direct air capture of carbon dioxide ( CO2), specifically targeting novel metal organic framework materials. Our proposed SMF is hierarchical, simultaneously

Why big data and compute are not necessarily the path to big materials science

August 30, 2022
Author(s)
Naohiro Fujinuma, Brian DeCost, Jason Hattrick-Simpers, Sam Lofland
Applied machine learning has rapidly spread throughout the physical sciences. In fact, machine learning-based data analysis and experimental decision-making have become commonplace. Here, we reflect on the ongoing shift in the conversation from proving

Leveraging Theory for Enhanced Machine Learning

August 26, 2022
Author(s)
Debra Audus, Austin McDannald, Brian DeCost
The application of machine learning to the materials domain has traditionally struggled with two major challenges: a lack of large, curated data sets and the need to understand the physics behind the machine-learning prediction. The former problem is

Development of an automated millifluidic platform and data-analysis pipeline for rapid electrochemical corrosion measurements: a pH study on Zn-Ni

July 25, 2022
Author(s)
Howie Joress, Brian DeCost, Najlaa Hassan, Trevor Braun, Justin Gorham, Jason Hattrick-Simpers
We describe the development of a millifluidic based scanning droplet cell platform for rapid and automated corrosion. This system allows for measurement of corrosion properties (e.g., open circuit potential, corrosion current through Tafel and linear

Recent Advances and Applications of Deep Learning Methods in Materials Science

February 24, 2022
Author(s)
Kamal Choudhary, Brian DeCost, Chi Chen, Anubhav Jain, Francesca Tavazza, Ryan Cohn, Cheol WooPark, Alok Choudhary, Ankit Agrawal, Simon Billinge, Elizabeth Holm, ShyuePing Ong, Chris Wolverton
Deep learning (DL) is one of the fastest growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data modalities. Deep learning allows analysis of unstructured data and automated