Work

RangeBO: Benchmark Data for Specification-Driven Materials Design

Year: 2026

Type: dataset

Abstract: Benchmark datasets for "Range-Aware Bayesian Optimization for Discovering Diverse Designs within Target Property Windows" by Jiang, Wu, Schroeder, and Webb. Code: https://github.com/webbtheosim/target-range-optimization This archive contains the processed datasets for 10 benchmarks: bace, esol, freesolv, lipo, qm9, nanoparticle, propensity, toporg, kmc, and tddft. The synthetic benchmarks, ackley, branin, hartmann, and layeb06, are defined in the code and are not included. Contents of benchmark_data.zip: clean/Model-ready features, labels, and output scalers. mordred/Mordred molecular descriptors before PCA. smiles/Raw molecular data in CSV and Excel formats. config/Target values and tolerance settings. README.txtDataset description and provenance. Data flow: smiles/ -> mordred/ -> clean/ The clean/ folder is sufficient to run the benchmarks. To use the data: git clone https://github.com/webbtheosim/target-range-optimization.gitcd target-range-optimizationunzip benchmark_data.zip -d bo_target/data/

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.21497960

Authors: Jiang, Shengli, Webb, Michael, Schroeder, Charles, Wu, Jason

Institutions: University of South Carolina, Princeton University

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: green

Funder: National Science Foundation

Award: Collaborative Research: DMREF: Machine Learning and Robotics for the Data-Driven Design of Protein-polymer Hybrid Materials

OpenAlex ID: W7171149779

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