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Benchmarking local geometry optimization algorithms for computational materials discovery
David Greten1, Konstantin S Jakob1, Karsten Reuter1
1Fritz Haber Institute of the Max Planck Society, Berlin, Germany.
The Journal of Chemical Physics
|August 11, 2026
Summary
Choosing the right optimization algorithm is crucial for accurately predicting new crystalline materials. This study shows how different algorithms impact structure relaxation in computational materials discovery, guiding future research.
Area of Science:
- Materials Science
- Computational Chemistry
- Crystallography
Background:
- Computational materials discovery relies on accurate structure relaxation to predict material properties.
- Efficient and reliable optimization algorithms are essential for successful structure relaxation.
Purpose of the Study:
- To investigate the impact of various optimization algorithms on inorganic crystal structure relaxation.
- To evaluate the performance of different optimizers when using machine-learned interatomic potentials.
Main Methods:
- Utilized general-purpose machine-learned interatomic potentials for structure relaxation simulations.
- Compared the effectiveness of different optimization algorithms in predicting equilibrium crystal structures.
Main Results:
- Optimizer selection significantly influences the efficiency and reliability of crystal structure relaxation.
- The choice of algorithm is critical for large-scale computational materials science workflows, especially in element-substitution-based discovery.
Conclusions:
- Highlights the importance of selecting appropriate optimization algorithms for computational materials discovery.
- Provides guidance for researchers to choose optimal algorithms for predicting new crystalline materials efficiently and reliably.
