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A collaborative team from the Institute of Statistical Mathematics and Panasonic Holdings Corporation has developed a groundbreaking machine learning algorithm named ShotgunCSP, capable of rapidly and accurately predicting crystal structures based on material compositions. In benchmark tests, ShotgunCSP achieved world-class performance, significantly outperforming traditional approaches.

Crystal structure prediction (CSP) aims to determine stable or metastable crystal forms of compounds under specific conditions. Traditionally, this process depends on first-principles energy calculations and iterative energy minimization—an approach that has long been a central challenge in materials science.

While recent advances in computational power and generative AI have improved this field, predicting crystal structures for large or complex molecular systems still requires vast computational resources. This has left a critical gap in the ability to efficiently explore large structural phase spaces.

The research team found that machine learning can effectively predict the symmetry patterns—such as space groups and Wyckoff positions—associated with stable structures. This insight allowed them to drastically reduce the search space, avoiding the need for iterative energy calculations. Even in complex systems, ShotgunCSP was able to identify stable crystal structures with exceptional speed and accuracy.

Published in npj Computational Materials, this work showcases a transformative shift in how researchers approach CSP.

Crystals are highly ordered solids central to industries including semiconductors, pharmaceuticals, and energy storage. Since a material’s properties depend heavily on its crystal structure, predictive models like ShotgunCSP offer significant value in speeding up materials development and reducing experimental trial-and-error.

Traditionally, CSP methods combine optimization algorithms—like genetic algorithms—with first-principles energy calculations, such as those based on density functional theory (DFT). These conventional approaches involve evaluating and relaxing thousands of candidate structures, which is computationally intensive—especially for systems with more than 30 atoms per unit cell.

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Recent benchmark studies have shown that existing CSP methods correctly predict less than half of all tested crystal systems, underlining the need for more efficient and accurate solutions.

To overcome this, the ShotgunCSP method begins by training a machine learning-based energy predictor to approximate DFT energy values. Using transfer learning, the team was able to achieve high accuracy with relatively little training data. A novel structure generator then produces plausible candidate structures, guided by predicted symmetry constraints.

These candidates are filtered using the energy predictor, drastically narrowing down the possibilities. Only the most promising candidates are then refined with DFT calculations to determine the final stable structure. The algorithm’s name, ShotgunCSP, reflects its wide yet targeted approach—scanning broadly but analyzing only the most likely "hits."

A crucial innovation in ShotgunCSP is its ability to predict structural symmetries using machine learning, enabling it to focus computational efforts efficiently. This makes it possible to tackle larger and more complex systems than was previously feasible, marking a major advancement in crystal structure prediction and materials discovery.