U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

Search Publications by: Brian DeCost (Fed)

Search Title, Abstract, Conference, Citation, Keyword or Author
Displaying 26 - 50 of 59

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

Physics in the Machine: Integrating Physical Knowledge in Autonomous Phase-Mapping

February 16, 2022
Author(s)
A. Gilad Kusne, Austin McDannald, Brian DeCost
Application of artificial intelligence (AI), and more specifically machine learning, to the physical sciences has expanded significantly over the past decades. In particular, science-informed AI, also known as scientific AI or inductive bias AI, has grown

Uncertainty Prediction for Machine Learning Models of Material Properties

November 23, 2021
Author(s)
Francesca Tavazza, Brian DeCost, Kamal Choudhary
Uncertainty quantification in artificial intelligence (AI)-based predictions of material properties is of immense importance for the success and reliability of AI applications in materials science. While confidence intervals are commonly reported for

An Open Combinatorial Diffraction Dataset Including Consensus Human and Machine Learning Labels with Quantified Uncertainty for Training New Machine Learning Models

June 9, 2021
Author(s)
Jason Hattrick-Simpers, Brian DeCost, Aaron Gilad Kusne, Howard Joress, Winnie Wong-Ng, Debra Kaiser, Andriy Zakutayev, Caleb Phillips, Tonio Buonassisi, Shijing Sun, Janak Thapa
Modern machine learning and autonomous experimentation schemes in materials science rely on accurate analysis of the data ingested by these models. Unfortunately, accurate analysis of the underlying data can be difficult, even for domain experts

On-the-fly closed-loop materials discovery via Bayesian active learning

November 24, 2020
Author(s)
Aaron Gilad Kusne, Heshan Yu, Huairuo Zhang, Jason Hattrick-Simpers, Brian DeCost, Albert Davydov, Leonid A. Bendersky, Apurva Mehta, Ichiro Takeuchi
Active learning—the field of machine learning (ML) dedicated to optimal experiment design—has played a part in science as far back as the 18th century when Laplace used it to guide his discovery of celestial mechanics. In this work, we focus a closed-loop

The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design

November 12, 2020
Author(s)
Kamal Choudhary, Kevin Garrity, Andrew C. Reid, Brian DeCost, Adam Biacchi, Angela R. Hight Walker, Zachary Trautt, Jason Hattrick-Simpers, Aaron Kusne, Andrea Centrone, Albert Davydov, Francesca Tavazza, Jie Jiang, Ruth Pachter, Gowoon Cheon, Evan Reed, Ankit Agrawal, Xiaofeng Qian, Vinit Sharma, Houlong Zhuang, Sergei Kalinin, Ghanshyam Pilania, Pinar Acar, Subhasish Mandal, David Vanderbilt, Karin Rabe
The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an integrated infrastructure to accelerate materials discovery and design using density functional theory (DFT), classical force-fields (FF), and machine learning (ML) techniques