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Brian L. DeCost · SciRah

Researcher profile · Semantic Scholar

Brian L. DeCost

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Brian L. DeCost — researcher profile on SciRah

h-index: 24
Works: 55
Citations: 5,335

Most-cited papers

Recent advances and applications of deep learning methods in materials science
Kamal Choudhary, Brian L. DeCost, Chi Chen, Anubhav Jain +9 more
2021npj Computational Materials1,119 citations Open access
Semantic Scholar
Atomistic Line Graph Neural Network for improved materials property predictions
Kamal Choudhary, Brian L. DeCost
2021npj Computational Materials893 citations Open access
Semantic Scholar
JARVIS: An Integrated Infrastructure for Data-driven Materials Design
Kamal Choudhary, K. Garrity, Andrew C. E. Reid, Brian L. DeCost +25 more
2020415 citations Open access
Semantic Scholar
Autonomous experimentation systems for materials development: A community perspective
E. Stach, Brian L. DeCost, A. Kusne, J. Hattrick-Simpers +17 more
2021382 citations Open access
Semantic Scholar
Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks
Felipe Oviedo, Ze-Kun Ren, Shijing Sun, C. Settens +8 more
2018npj Computational Materials325 citations Open access
Semantic Scholar
Accelerated Development of Perovskite-Inspired Materials via High-Throughput Synthesis and Machine-Learning Diagnosis
Shijing Sun, Noor Titan Putri Hartono, Ze-Kun Ren, Felipe Oviedo +14 more
2018Joule286 citations Open access
Semantic Scholar
The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design
Kamal Choudhary, K. Garrity, Andrew C. E. Reid, Brian L. DeCost +24 more
2020npj Computational Materials281 citations Open access
Semantic Scholar
High Throughput Quantitative Metallography for Complex Microstructures Using Deep Learning: A Case Study in Ultrahigh Carbon Steel
Brian L. DeCost, Bo Lei, T. Francis, E. Holm
2018Microscopy and Microanalysis186 citations Open access
Semantic Scholar
A critical examination of robustness and generalizability of machine learning prediction of materials properties
Kangming Li, Brian L. DeCost, Kamal Choudhary, Michael Greenwood +1 more
2022npj Computational Materials164 citations Open access
Semantic Scholar
Machine learning with force-field inspired descriptors for materials: fast screening and mapping energy landscape
Kamal Choudhary, Brian L. DeCost, F. Tavazza
2018PHYSICAL REVIEW MATERIALS154 citations Open access
Semantic Scholar
Exploiting redundancy in large materials datasets for efficient machine learning with less data
Kangming Li, D. Persaud, Kamal Choudhary, Brian L. DeCost +2 more
2023Nature Communications123 citations Open access
Semantic Scholar
Computer Vision and Machine Learning for Autonomous Characterization of AM Powder Feedstocks
Brian L. DeCost, Harshvardhan Jain, A. Rollett, E. Holm
2016JOM123 citations Open access
Semantic Scholar
Scientific AI in Materials Science: a Path to a Sustainable and Scalable Paradigm
Brian L. DeCost, J. Hattrick-Simpers, Z. Trautt, A. Kusne +2 more
2020Machine Learning: Science and Technology104 citations Open access
Semantic Scholar
Probing out-of-distribution generalization in machine learning for materials
Kangming Li, Andre Niyongabo Rubungo, Xiangyun Lei, D. Persaud +4 more
2024Communications Materials103 citations Open access
Semantic Scholar
Uncertainty Prediction for Machine Learning Models of Material Properties
F. Tavazza, Brian L. DeCost, Kamal Choudhary
2021ACS Omega96 citations Open access
Semantic Scholar
Structure-aware graph neural network based deep transfer learning framework for enhanced predictive analytics on diverse materials datasets
Vishu Gupta, Kamal Choudhary, Brian L. DeCost, F. Tavazza +4 more
2024npj Computational Materials76 citations Open access
Semantic Scholar
On-the-fly closed-loop materials discovery via Bayesian active learning
A. Gilad Kusne, He-Shan Yu, Chang-Ming Wu, Huairuo Zhang +12 more
2020Nature Communications62 citations Open access
Semantic Scholar
Why big data and compute are not necessarily the path to big materials science
Naohiro Fujinuma, Brian L. DeCost, J. Hattrick-Simpers, S. Lofland
2022Communications Materials46 citations Open access
Semantic Scholar
Accelerating defect predictions in semiconductors using graph neural networks
Md Habibur Rahman, Prince Gollapalli, P. Manganaris, S. K. Yadav +4 more
2023APL Machine Learning41 citations Open access
Semantic Scholar
Why is EXAFS for complex concentrated alloys so hard? Challenges and opportunities for measuring ordering with X-ray absorption spectroscopy
H. Joress, B. Ravel, E. Anber, Jonathan D. Hollenbach +4 more
2023Matter40 citations Open access
Semantic Scholar
Recent progress in the JARVIS infrastructure for next-generation data-driven materials design
Daniel Wines, R. Gurunathan, K. Garrity, Brian L. DeCost +3 more
2023Applied Physics Reviews39 citations Open access
Semantic Scholar
A High-Throughput Structural and Electrochemical Study of Metallic Glass Formation in Ni–Ti–Al
H. Joress, Brian L. DeCost, Suchismita Sarker, T. Braun +6 more
2019ACS Combinatorial Science39 citations Open access
Semantic Scholar
Unified Graph Neural Network Force-field for the Periodic Table for Solids
Kamal Choudhary, Brian L. DeCost, Lily Major, K. Butler +2 more
2022Digital Discovery26 citations Open access
Semantic Scholar
AutoEIS: Automated Bayesian Model Selection and Analysis for Electrochemical Impedance Spectroscopy
Runze Zhang, D. Sur, Robert W. Black, Kangming Li +6 more
2023Journal of the Electrochemical Society25 citations Open access
Semantic Scholar
An Experimental High-Throughput to High-Fidelity Study Towards Discovering Al–Cr Containing Corrosion-Resistant Compositionally Complex Alloys
D. Sur, Emily Holcombe, W. Blades, E. Anber +8 more
2023High Entropy Alloys & Materials21 citations Open access
Semantic Scholar
Leveraging theory for enhanced machine learning
Debra J. Audus, Austin McDannald, Brian L. DeCost
2022ACS Macro Letters21 citations Open access
Semantic Scholar
Development of an Automated Millifluidic Platform and Data-Analysis Pipeline for Rapid Electrochemical Corrosion Measurements: A Ph Study on Zn-Ni
H. Joress, Brian L. DeCost, Najlaa Hassan, T. Braun +2 more
2022Social Science Research Network19 citations Open access
Semantic Scholar
Towards Automated Design of Corrosion Resistant Alloy Coatings with an Autonomous Scanning Droplet Cell
Brian L. DeCost, H. Joress, Suchismita Sarker, Apurva Mehta +1 more
2022JOM18 citations Open access
Semantic Scholar
Efficient first principles based modeling via machine learning: from simple representations to high entropy materials
Kangming Li, Kamal Choudhary, Brian L. DeCost, Michael Greenwood +1 more
2024Journal of Materials Chemistry A16 citations Open access
Semantic Scholar
AtomVision: A Machine Vision Library for Atomistic Images
Kamal Choudhary, R. Gurunathan, Brian L. DeCost, A. Biacchi
2022Journal of Chemical Information and Modeling14 citations Open access
Semantic Scholar
Aggressively optimizing validation statistics can degrade interpretability of data-driven materials models
Katherine Lei, H. Joress, Nils Persson, J. Hattrick-Simpers +1 more
2021Journal of Chemical Physics14 citations Open access
Semantic Scholar
An Inter-Laboratory Study of Zn-Sn-Ti-O Thin Films using High-Throughput Experimental Methods.
J. Hattrick-Simpers, A. Zakutayev, S. Barron, Z. Trautt +10 more
2019ACS Combinatorial Science14 citations Open access
Semantic Scholar
An Open Combinatorial Diffraction Dataset Including Consensus Human and Machine Learning Labels with Quantified Uncertainty for Training New Machine Learning Models
J. Hattrick-Simpers, Brian L. DeCost, A. Kusne, H. Joress +9 more
2021Integrating Materials and Manufacturing Innovation12 citations Open access
Semantic Scholar
Author Correction: Atomistic Line Graph Neural Network for improved materials property predictions
Kamal Choudhary, Brian L. DeCost
2022npj Computational Materials9 citations Open access
Semantic Scholar
Physics in the Machine: Integrating Physical Knowledge in Autonomous Phase-Mapping
Aaron Gilad Kusne, Austin McDannald, Brian L. DeCost, C. Oses +4 more
2021Frontiers of Physics9 citations Open access
Semantic Scholar
On-the-fly Closed-loop Autonomous Materials Discovery via Bayesian Active Learning
A. Kusne, Heshan Yu, Chang-Ming Wu, Huairuo Zhang +12 more
20208 citations
Semantic Scholar
Reproducible sorbent materials foundry for carbon capture at scale
Austin McDannald, H. Joress, Brian L. DeCost, Avery E. Baumann +8 more
2022Cell Reports Physical Science7 citations Open access
Semantic Scholar
A reproducibility study of atomistic line graph neural networks for materials property prediction
Kangming Li, Brian L. DeCost, Kamal Choudhary, J. Hattrick-Simpers
2024Digital Discovery6 citations Open access
Semantic Scholar
Fast classification of small X-ray diffraction datasets using data augmentation and deep neural networks
Felipe Oviedo, Ze-Kun Ren, Shijing Sun, C. Settens +8 more
20186 citations
Semantic Scholar
Learning material synthesis–process–structure–property relationship by data fusion: Bayesian co-regionalization N-dimensional piecewise function learning
A. Kusne, Austin McDannald, Brian L. DeCost
2024Digital Discovery5 citations Open access
Semantic Scholar
When Active Learning Fails, Uncalibrated Out of Distribution Uncertainty Quantification Might Be the Problem
Ashley S. Dale, Kangming Li, Brian L. DeCost, Hao Wan +3 more
2025arXiv.org4 citations
Semantic Scholar
Intrinsic direct air capture
Austin McDannald, D. Siderius, Brian L. DeCost, Kamal Choudhary +1 more
2025Chemical Science4 citations Open access
Semantic Scholar
A call for caution in the era of AI-accelerated materials science
Kangming Li, Edward Kim, Yao Fehlis, D. Persaud +3 more
2023Matter3 citations Open access
Semantic Scholar
Leveraging Domain Adaptation for Accurate Machine Learning Predictions of New Halide Perovskites
Dipannoy Das Gupta, Zachary J. L. Bare, Suxuen Yew, Santosh Adhikari +4 more
20242 citations
Semantic Scholar
Learning material synthesis-process-structure-property relationship by data fusion: Bayesian Coregionalization N-Dimensional Piecewise Function Learning
Aaron Gilad Kusne, Austin McDannald, Brian L. DeCost
2023arXiv.org2 citations
Semantic Scholar
Approaches for Uncertainty Quantification of AI-predicted Material Properties: A Comparison
F. Tavazza, Kamal Choudhary, Brian L. DeCost
2023arXiv.org1 citations
Semantic Scholar
Comment on “A simple constrained machine learning model for predicting high-pressure-hydrogen-compressor materials” by Hattrick-Simpers, et al., Molecular Systems Design & Engineering, 2018, 3, 509
J. Hattrick-Simpers, Brian L. DeCost
20201 citations
Semantic Scholar
Fast classification of small X-ray diffraction datasets using data augmentation and deep neural networks
Felipe Oviedo, Ze-Kun Ren, Shijing Sun, C. Settens +8 more
2018arXiv.org1 citations
Semantic Scholar
Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning
Wonseok Jeong, F. Tavazza, Brian L. DeCost
20260 citations
Semantic Scholar
Materials Acceleration Platform for Electrochemistry: a Platform for Autonomous Electrochemistry
D. Persaud, Mike Werezak, Mark Xu, Melyne Zhou +8 more
2026npj Materials Degradation0 citations Open access
Semantic Scholar

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