Dataset Viewer
chemical_formula_hill
string | chemical_formula_reduced
string | chemical_formula_anonymous
string | atomic_numbers
list | elements
list | elements_ratios
list | nelements
int32 | nsites
int32 | cell
list | positions
list | pbc
list | dimension_types
list | nperiodic_dimensions
int32 | structure_hash
string | multiplicity
int32 | software
string | method
string | adsorption_energy
float64 | atomic_forces
string | atomization_energy
float64 | cauchy_stress
string | cauchy_stress_volume_normalized
bool | electronic_band_gap
float64 | electronic_band_gap_type
string | energy
float64 | formation_energy
float64 | max_force_norm
float64 | mean_force_norm
float64 | property_object_metadata
string | property_object_metadata_id
string | property_object_last_modified
timestamp[ns] | property_object_hash
string | property_object_id
string | configuration_metadata
string | configuration_metadata_id
string | configuration_labels
list | configuration_names
list | configuration_dataset_ids
list | configuration_last_modified
timestamp[ns] | configuration_hash
string | configuration_id
string | dataset_name
string | dataset_authors
list | dataset_description
string | dataset_elements
list | dataset_nelements
int32 | dataset_nproperty_objects
int64 | dataset_nconfigurations
int32 | dataset_nsites
int64 | dataset_adsorption_energy_count
int64 | dataset_atomic_forces_count
int64 | dataset_atomization_energy_count
int64 | dataset_cauchy_stress_count
int64 | dataset_electronic_band_gap_count
int64 | dataset_energy_count
int64 | dataset_energy_mean
float64 | dataset_energy_variance
float64 | dataset_formation_energy_count
int64 | dataset_last_modified
timestamp[ns] | dataset_dimension_types
list | dataset_nperiodic_dimensions
list | dataset_publication_year
string | dataset_total_elements_ratios
list | dataset_license
string | dataset_links
string | dataset_doi
string | dataset_hash
string | dataset_id
string | dataset_extended_id
string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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| [
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| 2025-07-01T21:19:00 | 10012018049889696251710575096958132688787896200190521322953529899132221646726339078687660487284888288839898464321864287387660438004280971116760809407997494 | CO_1001201804988969625171057 | QM-22_n-tetradecane_train | [
"Chen Qu",
"Paul L. Houston",
"Thomas Allison",
"Barry I. Schneider",
"Joel M. Bowman"
]
| The n-tetradecane training split of the QM-22 datasets. QM-22 consists of CHON molecules of 4-15 atoms, developed in counterpoint to the MD17 dataset, run at higher total energies (above 500 K) and with a broader configuration space. | [
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| 2 | 253,646 | 253,646 | 11,160,424 | 0 | 253,646 | 0 | 0 | 0 | 253,646 | -15,008.903193 | 1.466039 | 0 | 2025-07-02T10:26:21 | [
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| Apache-2.0 | {'source-publication': 'https://doi.org/10.1021/acs.jctc.4c00932', 'source-data': 'https://github.com/jmbowma/QM-22', 'other': ['https://doi.org/10.1063/5.0089200']} | null | 9388467828308186822617759717616594863305576282735587252079220484188015252902883645071795412790498198381413285939095135733209223099338533288084804612883610 | DS_gtkq8hnj6tdg_0 | QM-22_n-tetradecane_train__Qu-Houston-Allison-Schneider-Bowman__DS_gtkq8hnj6tdg_0 |
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| The n-tetradecane training split of the QM-22 datasets. QM-22 consists of CHON molecules of 4-15 atoms, developed in counterpoint to the MD17 dataset, run at higher total energies (above 500 K) and with a broader configuration space. | [
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| The n-tetradecane training split of the QM-22 datasets. QM-22 consists of CHON molecules of 4-15 atoms, developed in counterpoint to the MD17 dataset, run at higher total energies (above 500 K) and with a broader configuration space. | [
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| The n-tetradecane training split of the QM-22 datasets. QM-22 consists of CHON molecules of 4-15 atoms, developed in counterpoint to the MD17 dataset, run at higher total energies (above 500 K) and with a broader configuration space. | [
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| The n-tetradecane training split of the QM-22 datasets. QM-22 consists of CHON molecules of 4-15 atoms, developed in counterpoint to the MD17 dataset, run at higher total energies (above 500 K) and with a broader configuration space. | [
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| The n-tetradecane training split of the QM-22 datasets. QM-22 consists of CHON molecules of 4-15 atoms, developed in counterpoint to the MD17 dataset, run at higher total energies (above 500 K) and with a broader configuration space. | [
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| The n-tetradecane training split of the QM-22 datasets. QM-22 consists of CHON molecules of 4-15 atoms, developed in counterpoint to the MD17 dataset, run at higher total energies (above 500 K) and with a broader configuration space. | [
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| The n-tetradecane training split of the QM-22 datasets. QM-22 consists of CHON molecules of 4-15 atoms, developed in counterpoint to the MD17 dataset, run at higher total energies (above 500 K) and with a broader configuration space. | [
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| The n-tetradecane training split of the QM-22 datasets. QM-22 consists of CHON molecules of 4-15 atoms, developed in counterpoint to the MD17 dataset, run at higher total energies (above 500 K) and with a broader configuration space. | [
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| The n-tetradecane training split of the QM-22 datasets. QM-22 consists of CHON molecules of 4-15 atoms, developed in counterpoint to the MD17 dataset, run at higher total energies (above 500 K) and with a broader configuration space. | [
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| The n-tetradecane training split of the QM-22 datasets. QM-22 consists of CHON molecules of 4-15 atoms, developed in counterpoint to the MD17 dataset, run at higher total energies (above 500 K) and with a broader configuration space. | [
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| The n-tetradecane training split of the QM-22 datasets. QM-22 consists of CHON molecules of 4-15 atoms, developed in counterpoint to the MD17 dataset, run at higher total energies (above 500 K) and with a broader configuration space. | [
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| The n-tetradecane training split of the QM-22 datasets. QM-22 consists of CHON molecules of 4-15 atoms, developed in counterpoint to the MD17 dataset, run at higher total energies (above 500 K) and with a broader configuration space. | [
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| The n-tetradecane training split of the QM-22 datasets. QM-22 consists of CHON molecules of 4-15 atoms, developed in counterpoint to the MD17 dataset, run at higher total energies (above 500 K) and with a broader configuration space. | [
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| The n-tetradecane training split of the QM-22 datasets. QM-22 consists of CHON molecules of 4-15 atoms, developed in counterpoint to the MD17 dataset, run at higher total energies (above 500 K) and with a broader configuration space. | [
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| The n-tetradecane training split of the QM-22 datasets. QM-22 consists of CHON molecules of 4-15 atoms, developed in counterpoint to the MD17 dataset, run at higher total energies (above 500 K) and with a broader configuration space. | [
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| The n-tetradecane training split of the QM-22 datasets. QM-22 consists of CHON molecules of 4-15 atoms, developed in counterpoint to the MD17 dataset, run at higher total energies (above 500 K) and with a broader configuration space. | [
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| The n-tetradecane training split of the QM-22 datasets. QM-22 consists of CHON molecules of 4-15 atoms, developed in counterpoint to the MD17 dataset, run at higher total energies (above 500 K) and with a broader configuration space. | [
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| The n-tetradecane training split of the QM-22 datasets. QM-22 consists of CHON molecules of 4-15 atoms, developed in counterpoint to the MD17 dataset, run at higher total energies (above 500 K) and with a broader configuration space. | [
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| The n-tetradecane training split of the QM-22 datasets. QM-22 consists of CHON molecules of 4-15 atoms, developed in counterpoint to the MD17 dataset, run at higher total energies (above 500 K) and with a broader configuration space. | [
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| 2025-07-01T21:19:00 | 10496645080051730135440816450395731602887758364682665630271251501315295647071334022249212839072857033078781938326486907114802791031251577102613117718124490 | CO_1049664508005173013544081 | QM-22_n-tetradecane_train | [
"Chen Qu",
"Paul L. Houston",
"Thomas Allison",
"Barry I. Schneider",
"Joel M. Bowman"
]
| The n-tetradecane training split of the QM-22 datasets. QM-22 consists of CHON molecules of 4-15 atoms, developed in counterpoint to the MD17 dataset, run at higher total energies (above 500 K) and with a broader configuration space. | [
"C",
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| 2 | 253,646 | 253,646 | 11,160,424 | 0 | 253,646 | 0 | 0 | 0 | 253,646 | -15,008.903193 | 1.466039 | 0 | 2025-07-02T10:26:21 | [
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End of preview. Expand
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Cite this dataset
Qu, C., Houston, P. L., Allison, T., Schneider, B. I., and Bowman, J. M. QM-22 n-tetradecane train. ColabFit, 2025. https://doi.org/None
View on the ColabFit Exchange
https://materials.colabfit.org/id/DS_gtkq8hnj6tdg_0
Dataset Name
QM-22 n-tetradecane train
Description
The n-tetradecane training split of the QM-22 datasets. QM-22 consists of CHON molecules of 4-15 atoms, developed in counterpoint to the MD17 dataset, run at higher total energies (above 500 K) and with a broader configuration space.
Additional details stored in dataset columns prepended with "dataset_".
Dataset authors
Chen Qu, Paul L. Houston, Thomas Allison, Barry I. Schneider, Joel M. Bowman
Publication
https://doi.org/10.1021/acs.jctc.4c00932
Original data link
https://github.com/jmbowma/QM-22
License
Apache-2.0
Number of unique molecular configurations
253646
Number of atoms
11160424
Elements included
C, H
Properties included
energy, atomic forces
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