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PO_1001864923351625256051095 | "100186492335162525605109503302859590854752186628799250956716942777619842747116961186072838163302979(...TRUNCATED) | 2025-04-17T18:16:17 | DS_mz0vl4zs7bex_0 | 1 | CP2K | DFT-BLYP-D3 | -1,174.642403 | [[-0.027087,-0.029216,0.064737],[-0.002903,0.009286,0.00013],[0.014961,-0.008888,-0.010861],[-0.0003(...TRUNCATED) | [] | null | null | null | null | null | null | 0.076014 | 0.018987 | null | CO_1105341638437573525598788 | "110534163843757352559878807243473112355739626431186053327639471958225372319730634066564334924801746(...TRUNCATED) | "680098069561046844293678705958877091370932330343596588364662655816704175059713044962449764883923307(...TRUNCATED) | [
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true,
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PO_4915459535435477502375573 | "491545953543547750237557344792274676635651631392181186976042497593642833095076366934023327824151710(...TRUNCATED) | 2025-04-16T21:30:02 | DS_mz0vl4zs7bex_0 | 1 | CP2K | DFT-revPBE0-D3 | -3,026.013277 | [[-0.001734,-0.009246,0.034901],[0.015137,-0.007055,-0.009164],[0.010381,-0.004395,0.023778],[-0.018(...TRUNCATED) | [] | null | null | null | null | null | null | 0.092352 | 0.028682 | null | CO_9808304129350836058619950 | "980830412935083605861995023592030583721132462436509862625537732921045843222191522862109500196947696(...TRUNCATED) | "312738999952952491169301637449000820698906107120380415307995364519703688898048423841081080732851094(...TRUNCATED) | [
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true,
true
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0,
0,
0
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"bnnt-h2o_249"
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End of preview. Expand in Data Studio
Cite this dataset
Schran, C., Thiemann, F. L., Rowe, P., Müller, E. A., Marsalek, O., and Michaelides, A. water ice PNAS 2021. ColabFit, 2023. https://doi.org/10.60732/07d278f0
Cite this dataset
Schran, C., Thiemann, F. L., Rowe, P., Müller, E. A., Marsalek, O., and Michaelides, A. water ice PNAS 2021. ColabFit, 2023. https://doi.org/10.60732/07d278f0This dataset has been curated and formatted for the ColabFit Exchange
This dataset is also available on the ColabFit Exchange:
https://materials.colabfit.org/id/DS_mz0vl4zs7bex_0
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https://materials.colabfit.org
Dataset Name
water ice PNAS 2021
Description
Dataset generated using a committee-based active learning strategy to build a training dataset for modeling complex aqueous systems.
Dataset authors
Christoph Schran, Fabian L. Thiemann, Patrick Rowe, Erich A. Müller, Ondrej Marsalek, Angelos Michaelides
Publication
https://doi.org/10.1073/pnas.2110077118
Original data link
https://doi.org/10.5281/zenodo.5235246
License
CC-BY-SA-4.0
Number of unique molecular configurations
1786
Number of atoms
681912
Elements included
B, C, F, H, Mo, N, O, S, Ti
Properties included
energy, atomic forces
Usage
ds.parquet: Aggregated dataset information.co/directory: Configuration rows each include a structure, calculated properties, and metadata.cs/directory : Configuration sets are subsets of configurations grouped by some common characteristic. Ifcs/does not exist, no configurations sets have been defined for this dataset.cs_co_map/directory : The mapping of configurations to configuration sets (if defined).
ColabFit Exchange documentation includes descriptions of content and example code for parsing parquet files:
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