BOSS uses Bayesian optimization to build N-dimensional surrogate models for the energy or property landscapes and infer global minima. The models are iteratively refined by sequentially sampling DFT data points that are promising and/or have high information content. In computational structure search, representing heterogenous materials with compact chemical ‘building blocks’ allows us to build in prior knowledge and reduce search dimensionality. The uncertainty-led exploration/exploitation sampling strategy delivers global minima with modest sampling, but also ensures visits to less favorable regions of phase space to gather information on rare events and energy barriers.
|Publication status||Published - 9 Mar 2020|
|MoE publication type||I2 ICT software|
- Machine learning
- Materials Science
- Computational Physics