About this Abstract |
Meeting |
2021 TMS Annual Meeting & Exhibition
|
Symposium
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AI/Data Informatics: Applications and Uncertainty Quantification at Atomistics and Mesoscales
|
Presentation Title |
Coupling Machine Learning and Global Structure Optimization in GASP 2.0 |
Author(s) |
Stephen Raymond Xie, Shreyas Honrao, Venkata Surya Chaitanya Kolloru, Richard G Hennig |
On-Site Speaker (Planned) |
Stephen Raymond Xie |
Abstract Scope |
We present the second iteration of the Genetic Algorithm for Structure Prediction (GASP), which adds support for predicting structures on substrates as well as acceleration with machine-learned surrogate models. GASP-Python, first released in 2016, is a grand-canonical evolutionary algorithm for global structure optimization. Here, we demonstrate the effectiveness of coupling GASP with a surrogate model for formation energy, which we fit and improve on-the-fly as the search progresses. As the algorithm produces candidate structures through genetic operations like crossover, the surrogate model is used to predict their ground-state formation energies. By eliminating candidates belonging to previously-explored, high-energy basins of attraction, this machine-learning approach reduces the number of expensive energy evaluations required to explore the energy landscape. We also compare different choices of representations used to encode the relevant physical information into machine-readable inputs. Finally, we demonstrate the approach on bulk and low-dimensional material systems. |
Proceedings Inclusion? |
Planned: |
Keywords |
Computational Materials Science & Engineering, Machine Learning, Modeling and Simulation |