cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.14272/kdbwpyoltiigfp-uhfffaoysa-n.1
title: C12H12O5S2
title: InChI=1S/C12H12O5S2/c1-16-9-3-4-10-8(7-18-19(2,14)15)5-12(13)17-11(10)6-9/h3-6H,7H2,1-2H3

description: This is a physical chemical entity[CHEBI_24431] associated with a molecule[CHEBI_25367].
 The molecule[CHEBI_25367] can be described by the following structural desciptors[cheminf_000085]:
 InChI descriptor[cheminf_000113]: InChI=1S/C12H12O5S2/c1-16-9-3-4-10-8(7-18-19(2,14)15)5-12(13)17-11(10)6-9/h3-6H,7H2,1-2H3, and canonical SMILES descriptor[cheminf_000007]: COc1ccc2c(c1)oc(=O)cc2CSS(=O)(=O)C, and by the IUPAC name[cheminf_000107]: .
 
 The physical chemical entity[CHEBI_24431] has a component solvent[CHEBI_46787] which is described by the canonical SMILES descriptor[cheminf_000007]:
 
 The physical chemical entity[CHEBI_24431] has the following Sample ID as registered in the research data repository chemotion (www.chemotion-repository.net, https://doi.org/10.25504/FAIRsharing.iagXcR): CRS-53452

 The physical chemical entity[CHEBI_24431] can be described by the physical descriptors [CHEMINF_000025]:
 Melting point descriptor[CHEMINF_000256]: 
 Boiling point descriptor[CHEMINF_000257]: 
 Refractive index descriptor[CHEMINF_000253]:

 The physical chemical entity[CHEBI_24431] can be further described by the following assays[OBI:0000070][CHMO:0001133]:
 
 CHMO:0000593 | 1H nuclear magnetic resonance spectroscopy (1H NMR)
 
 CHMO:0000595 | 13C nuclear magnetic resonance spectroscopy (13C NMR)
 
 CHMO:0000792 | ultraviolet-visible-near infrared spectrophotometry (UV-VIS-NIR)
 
 CHMO:0000060 | fluorescence detection (fluorescence (FL) detection)
 
 CHMO:0000470 | mass spectrometry (MS)
 
 The physical chemical entity[CHEBI_24431] was deposited to the Molecule Archive of the Karlsruhe Insitute of Technology (KIT) with the following Sample ID: 

 Used ontologies:
 CHEBI - Chemical Entities of Biological Interest
 CHEMINF - chemical information ontology (information entities about chemical entities)
 CHMO - Chemical Methods Ontology
 OBI - Ontology for Biomedical Investigations
DOI: 10.14272/kdbwpyoltiigfp-uhfffaoysa-n/chmo0000593
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) ()
title: InChI=1S/C12H12O5S2/c1-16-9-3-4-10-8(7-18-19(2,14)15)5-12(13)17-11(10)6-9/h3-6H,7H2,1-2H3

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR)
DOI: 10.14272/kdbwpyoltiigfp-uhfffaoysa-n/chmo0000792
title: ultraviolet-visible-near infrared spectrophotometry (UV-VIS-NIR) ()
title: InChI=1S/C12H12O5S2/c1-16-9-3-4-10-8(7-18-19(2,14)15)5-12(13)17-11(10)6-9/h3-6H,7H2,1-2H3

description: dataset for ultraviolet-visible-near infrared spectrophotometry (UV-VIS-NIR)
DOI: 10.14272/kdbwpyoltiigfp-uhfffaoysa-n/chmo0000060
title: fluorescence detection (fluorescence (FL) detection) ()
title: InChI=1S/C12H12O5S2/c1-16-9-3-4-10-8(7-18-19(2,14)15)5-12(13)17-11(10)6-9/h3-6H,7H2,1-2H3

description: dataset for fluorescence detection (fluorescence (FL) detection)
DOI: 10.14272/kdbwpyoltiigfp-uhfffaoysa-n/chmo0000470
title: mass spectrometry (MS) ()
title: InChI=1S/C12H12O5S2/c1-16-9-3-4-10-8(7-18-19(2,14)15)5-12(13)17-11(10)6-9/h3-6H,7H2,1-2H3

description: dataset for mass spectrometry (MS)
DOI: 10.14272/kdbwpyoltiigfp-uhfffaoysa-n/chmo0000595
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) ()
title: InChI=1S/C12H12O5S2/c1-16-9-3-4-10-8(7-18-19(2,14)15)5-12(13)17-11(10)6-9/h3-6H,7H2,1-2H3

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR)
DOI: 10.15468/dl.hz5e4u
title: Occurrence Download

description: A dataset containing 4732 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pinctada margaritifera"
 ]
}

 The dataset includes 4732 records from 150 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002085-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18150/dcaqpa
title: Changes of nanoviscosity during TRAIL-induced apoptosis of HeLa cells

description: Data of changes of diffusion time, nanoviscosity and volume related to article titled "Changes of nanoviscosity during TRAIL-induced apoptosis of HeLa cells", obtained using Fluorescence Correlation Spectroscopy (FCS) and calculated using equations described in article.
DOI: 10.14272/gxalhtzphvtvih-uhfffaoysa-n.1
title: C15H18O3S2
title: InChI=1S/C15H18O3S2/c1-3-4-7-19-20-10-11-8-15(16)18-14-9-12(17-2)5-6-13(11)14/h5-6,8-9H,3-4,7,10H2,1-2H3

description: This is a physical chemical entity[CHEBI_24431] associated with a molecule[CHEBI_25367].
 The molecule[CHEBI_25367] can be described by the following structural desciptors[cheminf_000085]:
 InChI descriptor[cheminf_000113]: InChI=1S/C15H18O3S2/c1-3-4-7-19-20-10-11-8-15(16)18-14-9-12(17-2)5-6-13(11)14/h5-6,8-9H,3-4,7,10H2,1-2H3, and canonical SMILES descriptor[cheminf_000007]: CCCCSSCc1cc(=O)oc2c1ccc(c2)OC, and by the IUPAC name[cheminf_000107]: .
 
 The physical chemical entity[CHEBI_24431] has a component solvent[CHEBI_46787] which is described by the canonical SMILES descriptor[cheminf_000007]:
 
 The physical chemical entity[CHEBI_24431] has the following Sample ID as registered in the research data repository chemotion (www.chemotion-repository.net, https://doi.org/10.25504/FAIRsharing.iagXcR): CRS-53459

 The physical chemical entity[CHEBI_24431] can be described by the physical descriptors [CHEMINF_000025]:
 Melting point descriptor[CHEMINF_000256]: 
 Boiling point descriptor[CHEMINF_000257]: 
 Refractive index descriptor[CHEMINF_000253]:

 The physical chemical entity[CHEBI_24431] can be further described by the following assays[OBI:0000070][CHMO:0001133]:
 
 CHMO:0000593 | 1H nuclear magnetic resonance spectroscopy (1H NMR)
 
 CHMO:0000595 | 13C nuclear magnetic resonance spectroscopy (13C NMR)
 
 CHMO:0000292 | ultraviolet-visible spectrophotometry (UV-VIS)
 
 CHMO:0000060 | fluorescence detection (fluorescence (FL) detection)
 
 CHMO:0000498 | high-resolution mass spectrometry (HRMS)
 
 The physical chemical entity[CHEBI_24431] was deposited to the Molecule Archive of the Karlsruhe Insitute of Technology (KIT) with the following Sample ID: 

 Used ontologies:
 CHEBI - Chemical Entities of Biological Interest
 CHEMINF - chemical information ontology (information entities about chemical entities)
 CHMO - Chemical Methods Ontology
 OBI - Ontology for Biomedical Investigations
DOI: 10.14272/gxalhtzphvtvih-uhfffaoysa-n/chmo0000060
title: fluorescence detection (fluorescence (FL) detection) ()
title: InChI=1S/C15H18O3S2/c1-3-4-7-19-20-10-11-8-15(16)18-14-9-12(17-2)5-6-13(11)14/h5-6,8-9H,3-4,7,10H2,1-2H3

description: dataset for fluorescence detection (fluorescence (FL) detection)
DOI: 10.14272/gxalhtzphvtvih-uhfffaoysa-n/chmo0000498
title: high-resolution mass spectrometry (HRMS) ()
title: InChI=1S/C15H18O3S2/c1-3-4-7-19-20-10-11-8-15(16)18-14-9-12(17-2)5-6-13(11)14/h5-6,8-9H,3-4,7,10H2,1-2H3

description: dataset for high-resolution mass spectrometry (HRMS)
DOI: 10.14272/gxalhtzphvtvih-uhfffaoysa-n/chmo0000595
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) ()
title: InChI=1S/C15H18O3S2/c1-3-4-7-19-20-10-11-8-15(16)18-14-9-12(17-2)5-6-13(11)14/h5-6,8-9H,3-4,7,10H2,1-2H3

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR)
DOI: 10.14272/gxalhtzphvtvih-uhfffaoysa-n/chmo0000292
title: ultraviolet-visible spectrophotometry (UV-VIS) ()
title: InChI=1S/C15H18O3S2/c1-3-4-7-19-20-10-11-8-15(16)18-14-9-12(17-2)5-6-13(11)14/h5-6,8-9H,3-4,7,10H2,1-2H3

description: dataset for ultraviolet-visible spectrophotometry (UV-VIS)
DOI: 10.14272/gxalhtzphvtvih-uhfffaoysa-n/chmo0000593
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) ()
title: InChI=1S/C15H18O3S2/c1-3-4-7-19-20-10-11-8-15(16)18-14-9-12(17-2)5-6-13(11)14/h5-6,8-9H,3-4,7,10H2,1-2H3

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR)
DOI: 10.14272/bnagzzkqwcnhhr-uhfffaoysa-n.1
title: C17H22BF2N3O2S2
title: InChI=1S/C17H22BF2N3O2S2/c1-9-5-11(3)22-15(9)13(7-26-27-8-14(21)17(24)25)16-10(2)6-12(4)23(16)18(22,19)20/h5-6,14H,7-8,21H2,1-4H3,(H,24,25)

description: This is a physical chemical entity[CHEBI_24431] associated with a molecule[CHEBI_25367].
 The molecule[CHEBI_25367] can be described by the following structural desciptors[cheminf_000085]:
 InChI descriptor[cheminf_000113]: InChI=1S/C17H22BF2N3O2S2/c1-9-5-11(3)22-15(9)13(7-26-27-8-14(21)17(24)25)16-10(2)6-12(4)23(16)18(22,19)20/h5-6,14H,7-8,21H2,1-4H3,(H,24,25), and canonical SMILES descriptor[cheminf_000007]: OC(=O)C(CSSCC1=C2C(=CC(=[N+]2[B-](n2c1c(C)cc2C)(F)F)C)C)N, and by the IUPAC name[cheminf_000107]: .
 
 The physical chemical entity[CHEBI_24431] has a component solvent[CHEBI_46787] which is described by the canonical SMILES descriptor[cheminf_000007]:
 
 The physical chemical entity[CHEBI_24431] has the following Sample ID as registered in the research data repository chemotion (www.chemotion-repository.net, https://doi.org/10.25504/FAIRsharing.iagXcR): CRS-53466

 The physical chemical entity[CHEBI_24431] can be described by the physical descriptors [CHEMINF_000025]:
 Melting point descriptor[CHEMINF_000256]: 
 Boiling point descriptor[CHEMINF_000257]: 
 Refractive index descriptor[CHEMINF_000253]:

 The physical chemical entity[CHEBI_24431] can be further described by the following assays[OBI:0000070][CHMO:0001133]:
 
 CHMO:0000593 | 1H nuclear magnetic resonance spectroscopy (1H NMR)
 
 CHMO:0000595 | 13C nuclear magnetic resonance spectroscopy (13C NMR)
 
 BFO:0000015 | process
 
 CHMO:0000792 | ultraviolet-visible-near infrared spectrophotometry (UV-VIS-NIR)
 
 CHMO:0000060 | fluorescence detection (fluorescence (FL) detection)
 
 The physical chemical entity[CHEBI_24431] was deposited to the Molecule Archive of the Karlsruhe Insitute of Technology (KIT) with the following Sample ID: 

 Used ontologies:
 CHEBI - Chemical Entities of Biological Interest
 CHEMINF - chemical information ontology (information entities about chemical entities)
 CHMO - Chemical Methods Ontology
 OBI - Ontology for Biomedical Investigations
DOI: 10.14272/bnagzzkqwcnhhr-uhfffaoysa-n/chmo0000593
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) ()
title: InChI=1S/C17H22BF2N3O2S2/c1-9-5-11(3)22-15(9)13(7-26-27-8-14(21)17(24)25)16-10(2)6-12(4)23(16)18(22,19)20/h5-6,14H,7-8,21H2,1-4H3,(H,24,25)

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR)
DOI: 10.14272/bnagzzkqwcnhhr-uhfffaoysa-n/bfo0000015.2
title: process ()
title: InChI=1S/C17H22BF2N3O2S2/c1-9-5-11(3)22-15(9)13(7-26-27-8-14(21)17(24)25)16-10(2)6-12(4)23(16)18(22,19)20/h5-6,14H,7-8,21H2,1-4H3,(H,24,25)

description: dataset for process
DOI: 10.14272/bnagzzkqwcnhhr-uhfffaoysa-n/chmo0000595
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) ()
title: InChI=1S/C17H22BF2N3O2S2/c1-9-5-11(3)22-15(9)13(7-26-27-8-14(21)17(24)25)16-10(2)6-12(4)23(16)18(22,19)20/h5-6,14H,7-8,21H2,1-4H3,(H,24,25)

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR)
DOI: 10.14272/bnagzzkqwcnhhr-uhfffaoysa-n/chmo0000792
title: ultraviolet-visible-near infrared spectrophotometry (UV-VIS-NIR) ()
title: InChI=1S/C17H22BF2N3O2S2/c1-9-5-11(3)22-15(9)13(7-26-27-8-14(21)17(24)25)16-10(2)6-12(4)23(16)18(22,19)20/h5-6,14H,7-8,21H2,1-4H3,(H,24,25)

description: dataset for ultraviolet-visible-near infrared spectrophotometry (UV-VIS-NIR)
DOI: 10.14272/bnagzzkqwcnhhr-uhfffaoysa-n/chmo0000060
title: fluorescence detection (fluorescence (FL) detection) ()
title: InChI=1S/C17H22BF2N3O2S2/c1-9-5-11(3)22-15(9)13(7-26-27-8-14(21)17(24)25)16-10(2)6-12(4)23(16)18(22,19)20/h5-6,14H,7-8,21H2,1-4H3,(H,24,25)

description: dataset for fluorescence detection (fluorescence (FL) detection)
DOI: 10.15468/dl.n38wrr
title: Occurrence Download

description: A dataset containing 15362 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Censuses of breeding birds: Line transect and Area based",
 "Country is Sweden"
 ]
}

 The dataset includes 15362 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002087-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cnr4d5
title: Occurrence Download

description: A dataset containing 22366 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Xeno-canto - Bird sounds from around the world",
 "Country is Sweden"
 ]
}

 The dataset includes 22366 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002088-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.30453233
title: data

description: Lonely but Not Alone: How Anthropomorphic Virtual Learning Assistants Foster Positive Emotions through Social Presence
DOI: 10.6084/m9.figshare.30453233.v1
title: data

description: Lonely but Not Alone: How Anthropomorphic Virtual Learning Assistants Foster Positive Emotions through Social Presence
DOI: 10.2909/374953e4-3ed5-4841-a050-cc5135a0d177
title: Surface of the marine protected areas (2012-2023)

description: This indicator measures marine protected area (MPA) coverage at the EU and Member State levels over time. It considers MPAs reported as both Natura 2000 sites and nationally designated protected areas.
DOI: 10.5281/zenodo.17432338
title: Atrial fibrillation models and simulations personalized to substrate-specific data

description: Dataset originated on the PERSUADE PROJECT (ERA4Health 2023 CARDINNOV)

related to the deliverable "D3.1 Synthetic augmented database. Data and workflow for fitting fast simulations to the substrate and patient-specific data."

 

R+D Partners of PERSUADE project: 

Hospital Universitario Puerta del Mar, Fundación para la Gestión de la Investigación Biomédica Cádiz (FCADIZ)

Commlab, Universitat de Valencia

Cardiology Dept., Clinic Emergency Hospital Bucharest, University of Medicine and Pharmacy (SCUB)

Department of Cardiology and Department of Experimental Cardiology. Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands (AUMC)

Politecnico di Milano, Department of Chemistry, Materials and Chemical Engineering “Giulio Natta” (POLIMI)

 

Spanish project reference: PCI2024-153442

 

#####################

 

METADATA

 

Title:   Atrial fibrillation models and simulations personalized to substrate-specific data

Date:     24/10/2025

Authors: 

Duna de Luis Moura (Commlab, Universitat de Valencia)

Miguel Rodrigo (Commlab, Universitat de Valencia)

Juan Fernández-Armenta (Hospital Universitario Puerta del Mar, Fundación para la Gestión de la Investigación Biomédica Cádiz (FCADIZ))

Etelvino Silva (Hospital Universitario Puerta del Mar, Fundación para la Gestión de la Investigación Biomédica Cádiz (FCADIZ))

Radu Vatasescu (Cardiology Dept., Clinic Emergency Hospital Bucharest, University of Medicine and Pharmacy (SCUB))

Gabriela-Elena Marascu (Cardiology Dept., Clinic Emergency Hospital Bucharest, University of Medicine and Pharmacy (SCUB))
DOI: 10.5281/zenodo.17432339
title: Atrial fibrillation models and simulations personalized to substrate-specific data

description: Dataset originated on the PERSUADE PROJECT (ERA4Health 2023 CARDINNOV)

related to the deliverable "D3.1 Synthetic augmented database. Data and workflow for fitting fast simulations to the substrate and patient-specific data."

 

R+D Partners of PERSUADE project: 

Hospital Universitario Puerta del Mar, Fundación para la Gestión de la Investigación Biomédica Cádiz (FCADIZ)

Commlab, Universitat de Valencia

Cardiology Dept., Clinic Emergency Hospital Bucharest, University of Medicine and Pharmacy (SCUB)

Department of Cardiology and Department of Experimental Cardiology. Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands (AUMC)

Politecnico di Milano, Department of Chemistry, Materials and Chemical Engineering “Giulio Natta” (POLIMI)

 

Spanish project reference: PCI2024-153442

 

#####################

 

METADATA

 

Title:   Atrial fibrillation models and simulations personalized to substrate-specific data

Date:     24/10/2025

Authors: 

Duna de Luis Moura (Commlab, Universitat de Valencia)

Miguel Rodrigo (Commlab, Universitat de Valencia)

Juan Fernández-Armenta (Hospital Universitario Puerta del Mar, Fundación para la Gestión de la Investigación Biomédica Cádiz (FCADIZ))

Etelvino Silva (Hospital Universitario Puerta del Mar, Fundación para la Gestión de la Investigación Biomédica Cádiz (FCADIZ))

Radu Vatasescu (Cardiology Dept., Clinic Emergency Hospital Bucharest, University of Medicine and Pharmacy (SCUB))

Gabriela-Elena Marascu (Cardiology Dept., Clinic Emergency Hospital Bucharest, University of Medicine and Pharmacy (SCUB))
DOI: 10.25345/c5rx93s51
title: MassIVE MSV000099605 - XIAMENUNIVERSITY-QIUYAN-2-Macrocarpal A (ykz0204)
title: Macrocarpal A, a key bioactive constituent isolated from Myrtaceae species like Eucalyptus, exhibits potent anti-inflammatory and antimicrobial effects, holding promise for pharmaceutical development and natural product applications.
DOI: 10.15468/dl.gn6x8j
title: Occurrence Download

description: A dataset containing 19046 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is INSDC Sequences",
 "Country is Sweden"
 ]
}

 The dataset includes 19046 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002090-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/13516242
title: World Weather Repository ( Daily Updating )
DOI: 10.15468/dl.2aq746
title: Occurrence Download

description: A dataset containing 35828 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Vascular Plant Herbarium, Oslo (O) UiO",
 "Country is Sweden"
 ]
}

 The dataset includes 35828 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002089-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57967/hf/6813
title: CID_SID_IUPAC_PAIN
DOI: 10.5281/zenodo.17453242
title: Data‑Driven Targetome Discovery and Database Requirements: Insights from the Therapeutic Target Database

description:  (1) counts of distinct target classes with at least one marketed target, (2) approved-drug counts per class, and (3) the top 200 de-duplicated drug-driven target co-occurrence pairs
DOI: 10.5281/zenodo.17453241
title: Data‑Driven Targetome Discovery and Database Requirements: Insights from the Therapeutic Target Database

description:  (1) counts of distinct target classes with at least one marketed target, (2) approved-drug counts per class, and (3) the top 200 de-duplicated drug-driven target co-occurrence pairs
DOI: 10.15468/dl.fxavm8
title: Occurrence Download

description: A dataset containing 112 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is one of (Persicaria viscosa, Persicaria viscosa, Persicaria viscosa)",
 "Country is Japan"
 ]
}

 The dataset includes 112 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002092-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17433998
title: Quantum-mechanical datasets for "Advancing Density Functional Tight-Binding method for Large Organic Molecules through Equivariant Neural Networks"

description: Here, you can access the quantum-mechanical datasets generated to train the equivariant many-body delta tight-binding potentials within the EquiDTB framework.

ABSTRACT

Semi-empirical quantum-mechanical (QM) methods have become valuable tools for studying complex (bio)molecular systems due to their balance between computational efficiency and accuracy. A key aspect of these methods is their parameterization, which not only governs the reliability of the results but also provides an opportunity to enhance their overall performance. In our previous work [J. Phys. Chem. Lett. 11, 16 (2021)], we advanced the \ADD{third-order} semi-empirical density functional tight-binding (DFTB3) method for computing multiple properties of small molecules by developing the machine learning (ML) potential NN$_{\rm rep}$ to bridge the gap between DFTB3 electronic components and those of the hybrid DFT-PBE0 functional. To overcome the limitations of NN$_{\rm rep}$, we introduce the EquiDTB framework, which leverages physics-inspired equivariant neural networks (NN) to parameterize scalable and transferable many-body $\Delta_{\rm TB}$ potentials, replacing the standard pairwise DFTB repulsive potential. This advancement extends the applicability of our ML-corrected DFTB approach to larger molecules and non-covalent systems (including only C, N, O, and H atoms), going beyond the chemical space represented in the training QM datasets. The enhanced performance of EquiDTB over the standard TB methods is demonstrated by the accurate computation of the atomic forces of S66x8 molecular dimers, as well as their interaction energies. Moreover, EquiDTB can be effectively employed to explore the potential energy surfaces of large and flexible drug-like molecules---for example, to determine the minimum energy path between isomers, analyze structural transitions during dynamical simulations, compute vibrational modes, and investigate energetic rankings. The performance for single molecules slightly decreases when the DFTB electronic energy is reduced to first-order but remains superior to standard TB methods. Our work thus demonstrates that an optimal integration of an equivariant NN with QM datasets can advance DFTB method while maintaining high efficiency, paving the way for reliable (bio)molecular simulations.

PREPRINT

https://chemrxiv.org/engage/chemrxiv/article-details/68095e1d927d1c2e667c750a

CODE and MODELS:

The  EquiDTB models and additional scripts for running the validation benchmarks in this work are available at https://github.com/lmedranos/EquiDTB.

FILES:

npz_dtb3: datatset to train the EquiDTB3 model, see README file.

extxyz_qm7x_dtb1_reftb1: datatset to train the EquiDTB1 model, using reference atomic energies computed with DFTB1.

qm7x_dtb1_reftb3: datatset to train the EquiDTB1 model, using reference atomic energies computed with DFTB3.

EquiDTB3_MD_trajs: MD trajectories of large molecules for the validation of EquiDTB3 model.