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ACS Omega
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December 13, 2021
Uncertainty Prediction for Machine Learning Models of Material Properties
Francesca Tavazza, Brian DeCost, Kamal Choudhary
Physical Review Materials
|
March 14, 2020
Machine learning with force-field inspired descriptors for materials: fast screening and mapping energy landscape
Kamal Choudhary, Brian DeCost, Francesca Tavazza
ACS Macro Letters
|
August 26, 2022
Leveraging Theory for Enhanced Machine Learning
Debra J Audus, Austin McDannald, Brian DeCost
Journal of Chemical Information and Modeling
|
March 1, 2023
AtomVision: A Machine Vision Library for Atomistic Images
Kamal Choudhary, Ramya Gurunathan, Brian DeCost, et al.
The Journal of Chemical Physics
|
August 8, 2021
Aggressively optimizing validation statistics can degrade interpretability of data-driven materials models
Katherine Lei, Howie Joress, Nils Persson, et al.
Nature Communications
|
January 4, 2024
Publisher Correction: Exploiting redundancy in large materials datasets for efficient machine learning with less data
Kangming Li, Daniel Persaud, Kamal Choudhary, et al.
Nature Communications
|
November 10, 2023
Exploiting redundancy in large materials datasets for efficient machine learning with less data
Kangming Li, Daniel Persaud, Kamal Choudhary, et al.
Chemical Science
|
September 12, 2025
Intrinsic direct air capture
Austin McDannald, Daniel W Siderius, Brian DeCost, et al.
ACS Combinatorial Science
|
March 20, 2019
An Inter-Laboratory Study of Zn-Sn-Ti-O Thin Films using High-Throughput Experimental Methods
Jason R Hattrick-Simpers, Andriy Zakutayev, Sara C Barron, et al.
Nature Communications
|
November 25, 2020
On-the-fly closed-loop materials discovery via Bayesian active learning
A Gilad Kusne, Heshan Yu, Changming Wu, et al.
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Search research articles
Search
Showing results (1-10 of 10) with videos related to
Sort By:
Page
of 1
ACS Omega
|
December 13, 2021
Uncertainty Prediction for Machine Learning Models of Material Properties
Francesca Tavazza, Brian DeCost, Kamal Choudhary
Physical Review Materials
|
March 14, 2020
Machine learning with force-field inspired descriptors for materials: fast screening and mapping energy landscape
Kamal Choudhary, Brian DeCost, Francesca Tavazza
ACS Macro Letters
|
August 26, 2022
Leveraging Theory for Enhanced Machine Learning
Debra J Audus, Austin McDannald, Brian DeCost
Journal of Chemical Information and Modeling
|
March 1, 2023
AtomVision: A Machine Vision Library for Atomistic Images
Kamal Choudhary, Ramya Gurunathan, Brian DeCost, et al.
The Journal of Chemical Physics
|
August 8, 2021
Aggressively optimizing validation statistics can degrade interpretability of data-driven materials models
Katherine Lei, Howie Joress, Nils Persson, et al.
Nature Communications
|
January 4, 2024
Publisher Correction: Exploiting redundancy in large materials datasets for efficient machine learning with less data
Kangming Li, Daniel Persaud, Kamal Choudhary, et al.
Nature Communications
|
November 10, 2023
Exploiting redundancy in large materials datasets for efficient machine learning with less data
Kangming Li, Daniel Persaud, Kamal Choudhary, et al.
Chemical Science
|
September 12, 2025
Intrinsic direct air capture
Austin McDannald, Daniel W Siderius, Brian DeCost, et al.
ACS Combinatorial Science
|
March 20, 2019
An Inter-Laboratory Study of Zn-Sn-Ti-O Thin Films using High-Throughput Experimental Methods
Jason R Hattrick-Simpers, Andriy Zakutayev, Sara C Barron, et al.
Nature Communications
|
November 25, 2020
On-the-fly closed-loop materials discovery via Bayesian active learning
A Gilad Kusne, Heshan Yu, Changming Wu, et al.
Page
of 1