Materials Science Optimization Benchmark Dataset for High-dimensional, Multi-objective, Multi-fidelity Optimization of CrabNet Hyperparameters - Baird, Sterling G.; Parikh, Jeet N.; Sparks, Taylor D. (ChemRxiv, 2023)
Benchmarks are crucial for driving progress in scientific disciplines. To be effective, benchmarks should closely mimic real-world tasks while being computationally efficient, allowing for accessibility and repeatability. Developing surrogate models that can be indistinguishable from the ground truth observation within the explored dataset bounds dramatically reduces the computational burden of running benchmarks without sacrificing quality, but this requires a large amount of initial data. In the fields of materials science and chemistry, relevant optimization tasks can be challenging due to their complexity, which includes hierarchical, noisy, multi-fidelity, multi-objective, high-dimensional, and non-linearly correlated variables. Additionally, they may include mixed numerical and categorical variables that are subject to linear and non-linear constraints. Simulating or experimentally verifying such tasks can be difficult, which is why benchmarks are essential. This study aimed to overcome these challenges by generating 173219 quasi-random hyperparameter combinations across 23 hyperparameters and using them to train CrabNet on the Matbench experimental band gap dataset (Computational runtime: 387 RTX-2080-Ti GPU days). The results were stored in a free-tier shared MongoDB Atlas dataset, creating a regression dataset that maps hyperparameter combinations to metrics such as MAE, RMSE, computational runtime, and model size for the CrabNet model trained on the Matbench experimental band gap benchmark task. To simulate the actual simulations, heteroskedastic noise was incorporated into the regression dataset, and bad hyperparameter combinations were excluded. Percentile ranks were computed within each group of identical parameter sets to capture heteroskedastic noise, rather than assuming Gaussian noise as is done in traditional approaches. This approach can be applied to other benchmark datasets, bridging the gap between optimization benchmarks with low computational overhead and realistically complex, real-world optimization scenarios.