Atomic structure generation from reconstructing structural fingerprints
- URL: http://arxiv.org/abs/2207.13227v1
- Date: Wed, 27 Jul 2022 00:42:59 GMT
- Title: Atomic structure generation from reconstructing structural fingerprints
- Authors: Victor Fung, Shuyi Jia, Jiaxin Zhang, Sirui Bi, Junqi Yin, P. Ganesh
- Abstract summary: We present an end-to-end structure generation approach using atom-centered symmetry functions as the representation and conditional variational autoencoders as the generative model.
We are able to successfully generate novel and valid atomic structures of sub-nanometer Pt nanoparticles as a proof of concept.
- Score: 1.2128971613239876
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Data-driven machine learning methods have the potential to dramatically
accelerate the rate of materials design over conventional human-guided
approaches. These methods would help identify or, in the case of generative
models, even create novel crystal structures of materials with a set of
specified functional properties to then be synthesized or isolated in the
laboratory. For crystal structure generation, a key bottleneck lies in
developing suitable atomic structure fingerprints or representations for the
machine learning model, analogous to the graph-based or SMILES representations
used in molecular generation. However, finding data-efficient representations
that are invariant to translations, rotations, and permutations, while
remaining invertible to the Cartesian atomic coordinates remains an ongoing
challenge. Here, we propose an alternative approach to this problem by taking
existing non-invertible representations with the desired invariances and
developing an algorithm to reconstruct the atomic coordinates through
gradient-based optimization using automatic differentiation. This can then be
coupled to a generative machine learning model which generates new materials
within the representation space, rather than in the data-inefficient Cartesian
space. In this work, we implement this end-to-end structure generation approach
using atom-centered symmetry functions as the representation and conditional
variational autoencoders as the generative model. We are able to successfully
generate novel and valid atomic structures of sub-nanometer Pt nanoparticles as
a proof of concept. Furthermore, this method can be readily extended to any
suitable structural representation, thereby providing a powerful, generalizable
framework towards structure-based generation.
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