Summary of Crystal-lsbo: Automated Design Of De Novo Crystals with Latent Space Bayesian Optimization, by Onur Boyar et al.
Crystal-LSBO: Automated Design of De Novo Crystals with Latent Space Bayesian Optimization
by Onur Boyar, Yanheng Gu, Yuji Tanaka, Shunsuke Tonogai, Tomoya Itakura, Ichiro Takeuchi
First submitted to arxiv on: 28 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes Crystal-LSBO, a novel framework for designing crystals from scratch using Latent Space Bayesian Optimization (LSBO). LSBO has been successful in discovering novel objects across various domains when combined with Variational Autoencoders (VAEs). The authors aim to enhance explorability within LSBO frameworks by introducing multiple VAEs that focus on distinct aspects of crystal structure, such as lattice, coordinates, and chemical elements. This approach reduces the complexity of the learning task for each model, enabling LSBO approaches to operate effectively. The paper demonstrates the efficacy of Crystal-LSBO through optimization tasks focused mainly on formation energy values, showcasing a new perspective for de novo crystal discovery. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Crystal researchers are working on a new way to design crystals from scratch using computer algorithms. These algorithms, called Latent Space Bayesian Optimization (LSBO), have been successful in finding new objects by combining them with another tool called Variational Autoencoders (VAEs). The challenge is that the input data for designing crystals is very complex, making it hard to explore and discover new ones. To solve this problem, the researchers created a new framework called Crystal-LSBO that uses multiple VAEs to focus on different aspects of crystal structure. This makes it easier for the algorithms to learn and find new crystals. The paper shows how well this approach works by testing it with different optimization tasks. |
Keywords
* Artificial intelligence * Latent space * Optimization
