About this Abstract |
Meeting |
1st World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
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Symposium
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First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
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Presentation Title |
Discovery of New Periodic Inorganic Crystals Via GANs |
Author(s) |
Taylor D. Sparks, Michael Alverson |
On-Site Speaker (Planned) |
Taylor D. Sparks |
Abstract Scope |
Moving away from materials screening to true materials discovery will require accurate generative models for both organic and periodic inorganic systems. In this work, we investigate and analyze the performance of various Generative Adversarial Network (GAN) architectures to find an innovative and effective way of generating theoretical crystal structures that are synthesizable and stable. The space group number, atomic positions, and lattice parameters are parsed from the CIFs and used to construct an input tensor for each of the different network architectures. Several different GAN layer configurations are designed and analyzed, including Wasserstein GANs with gradient penalty, in order to identify a model that can adequately recognize and discern symmetry patterns. This work will detail the process and techniques that were used in an attempt to generate never-before-seen crystal structures that are both stable and synthesizable, as well as reveal a plethora of guiding questions for future work. |
Proceedings Inclusion? |
Undecided |