Search for: All records

Creators/Authors contains: "Baird, Sterling G."

« Prev Next »

Total Resources

11

Resource Type
Conference Paper

0

Conference Proceeding

0

Dataset

0

Journal Article

11

Workshop Report

0

Availability
Full Text / Resource Available

5

Citation Only

6

Save Results
Excel (limit 2000)
CSV (limit 5000)
XML (limit 5000)

Have feedback or suggestions for a way to improve these results?
!

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

Generative adversarial networks and diffusion models in material discovery

https://doi.org/10.1039/D3DD00137G

Alverson, Michael ; Baird, Sterling G. ; Murdock, Ryan ; Ho, ; Johnson, Jeremy ; Sparks, Taylor D. ( January 2024 , Digital Discovery)

Diffusion Models outperform Generative Adversarial Networks (GANs) and Wasserstein GANs in material discovery.

more » « less
Free, publicly-accessible full text available January 17, 2025
Materials science optimization benchmark dataset for multi-objective, multi-fidelity optimization of hard-sphere packing simulations

https://doi.org/10.1016/j.dib.2023.109487

Baird, Sterling G. ; Issa, Ramsey ; Sparks, Taylor D. ( October 2023 , Data in Brief)

Free, publicly-accessible full text available October 1, 2024
Review of low-cost self-driving laboratories in chemistry and materials science: the “frugal twin” concept

https://doi.org/10.1039/D3DD00223C

Lo, Stanley ; Baird, Sterling G. ; Schrier, Joshua ; Blaiszik, Ben ; Carson, Nessa ; Foster, Ian ; Aguilar-Granda, Andrés ; Kalinin, Sergei V. ; Maruyama, Benji ; Politi, Maria ; et al ( January 2024 , Digital Discovery)

Low-cost self-driving labs (SDLs) offer faster prototyping, low-risk hands-on experience, and a test bed for sophisticated experimental planning software which helps us develop state-of-the-art SDLs.

more » « less
Free, publicly-accessible full text available January 1, 2025
Discovering chemically novel, high-temperature superconductors

https://doi.org/10.1016/j.commatsci.2023.112358

Seegmiller, Colton C. ; Baird, Sterling G. ; Sayeed, Hasan M. ; Sparks, Taylor D. ( September 2023 , Computational Materials Science)

Free, publicly-accessible full text available September 1, 2024
Building a “Hello World” for self-driving labs: The Closed-loop Spectroscopy Lab Light-mixing demo

https://doi.org/10.1016/j.xpro.2023.102329

Baird, Sterling G. ; Sparks, Taylor D. ( June 2023 , STAR Protocols)

Free, publicly-accessible full text available June 1, 2024
Compactness matters: Improving Bayesian optimization efficiency of materials formulations through invariant search spaces

https://doi.org/10.1016/j.commatsci.2023.112134

Baird, Sterling G. ; Hall, Jason R. ; Sparks, Taylor D. ( May 2023 , Computational Materials Science)

Free, publicly-accessible full text available May 1, 2024
What is a minimal working example for a self-driving laboratory?

https://doi.org/10.1016/j.matt.2022.11.007

Baird, Sterling G. ; Sparks, Taylor D. ( December 2022 , Matter)

Full Text Available
High-dimensional Bayesian optimization of 23 hyperparameters over 100 iterations for an attention-based network to predict materials property: A case study on CrabNet using Ax platform and SAASBO

https://doi.org/10.1016/j.commatsci.2022.111505

Baird, Sterling G. ; Liu, Marianne ; Sparks, Taylor D. ( August 2022 , Computational Materials Science)

Full Text Available
High-throughput calculation of atomic planar density for compounds

https://doi.org/10.1107/S1600576722001492

Baird, Sterling G. ; Sparks, Taylor D. ( April 2022 , Journal of Applied Crystallography)

A large collection of element-wise planar densities for compounds obtained from the Materials Project is calculated using brute force computational geometry methods, where the planar density is given by the total fractional area of atoms intersecting a supercell's crystallographic plane divided by the area of the supercell's crystallographic plane. It is demonstrated that the element-wise maximum lattice plane densities can be useful as machine learning features. The methods described here are implemented in an open-source Mathematica package hosted at https://github.com/sgbaird/LatticePlane.
more » « less
Full Text Available
xtal2png: A Python package for representing crystalstructure as PNG files

https://doi.org/10.21105/joss.04528

Baird, Sterling G. ; Jablonka, Kevin M. ; Alverson, Michael D. ; Sayeed, Hasan M. ; Khan, Mohammed Faris ; Seegmiller, Colton ; Smit, Berend ; Sparks, Taylor D. ( August 2022 , Journal of Open Source Software)

Full Text Available

« Prev Next »