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Creators/Authors contains: "Baird, Sterling G"

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  1. ; ; ; (, Matter)
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  2. ; ; (, Data in Brief)
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    Diffusion Models outperform Generative Adversarial Networks (GANs) and Wasserstein GANs in material discovery. 
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  4. ; (, STAR Protocols)
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  5. ; ; ; (, Computational Materials Science)
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  6. ; ; (, Computational Materials Science)
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  7. ; ; ; ; ; ; ; ; ; ; et al (, 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. 
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  8. ; (, Matter)
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  9. ; ; (, Computational Materials Science)
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  10. ; (, 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. 
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