arXiv:2502.02748v4 Announce Type: replace Abstract: Predicting properties of crystals from their structures is a fundamental yet challenging task in materials science. Unlike molecules, crystal structures exhibit infinite periodic arrangements of atoms, requiring methods capable of capturing both local and global information effectively. However, current works fall short of capturing long-range interactions within periodic structures. To address this, we leverage \emph{reciprocal space}, the natural domain for periodic crystals, and construct a Fourier series representation from fractional coo
The increasing availability of computational resources and advancements in AI/ML are enabling new approaches to complex scientific problems like materials prediction, making this development timely for the field.
Accurate prediction of crystal properties can significantly accelerate materials discovery, leading to breakthroughs in diverse fields from electronics to energy, reducing discovery costs and timelines.
The proposed ReciNet model alters how long-range interactions in periodic crystalline structures are modeled, potentially improving the reliability and efficiency of AI-driven materials design and accelerating the discovery pipeline.
- · Materials scientists
- · Pharmaceutical industry
- · Electronics manufacturers
- · AI/ML research labs
- · Traditional high-throughput screening methods
- · Companies reliant on slow, empirical materials discovery
Improved AI models for predicting material properties become more common, leading to faster initial screening of potential new materials.
The accelerated discovery of new materials with specific desired properties could lead to novel industrial applications and products across various sectors.
A competitive landscape emerges where nations and corporations with superior AI-driven materials design capabilities gain significant economic and strategic advantages.
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