Skip to content
DraconiticResearch
Search Sources API
203,203 studies31 sourcesUpdated 32 min ago
Peer-reviewedCC BYMaterials & Design

Explainable machine learning reveals orbital design rules for reversible H2 adsorption on single-atom-doped TiO2 nanoparticles

  • Mustafa Kurban1
  • Can Polat2
  • Erchin Serpedin2
  • Hasan Kurban1
  1. 1Hamad Bin Khalifa University, College of Science and Engineering, Doha, Qatar
  2. 2Texas A&M University, Department of Electrical and Computer Engineering, College Station, Texas, USA
Open at sourceDOICite

Abstract

Hydrogen is a promising energy carrier for decarbonized technologies, but reversible on-board storage remains challenging because efficient uptake and release under near-ambient conditions require a narrow adsorption-energy window. Here, we combine high-throughput electronic-structure calculations with explainable machine learning to uncover the governing factors of molecular hydrogen adsorption on single-atom-doped TiO 2 nanoparticles. Screening 30 dopants across perpendicular and parallel H 2 adsorption geometries reveals substantial variation in adsorption strength and clear configuration-dependent behavior. From a broad descriptor space, a three-stage feature-selection strategy identifies eight robust predictors, which are further reduced through symbolic regression to an analytical and interpretable model. The resulting descriptor space is governed by H–H activation, dopant–H 2 distance, and -electron rearrangement. Adsorption within the target window is associated with balanced -donation and -back-donation, with Group 4 and 5 transition metals emerging as the most robust candidates across geometries. Combined with Langmuir-based desorption estimates, these results provide a mechanism-based screening rule for reversible hydrogen storage on doped TiO 2 .

Related

  1. Descriptor-guided thermodynamic screening of H2 adsorption on single-atom-doped anatase TiO2 nanoparticles with interpretable machine learning
    Mustafa Kurban, Can Polat, Erchin Serpedin +1
    Scientific ReportsPeer-reviewedCC BYFull text7 citedMaterials & polymersOpenAlex
  2. NephroTox AI: an explainable machine learning framework with uncertainty quantification for drug-induced nephrotoxicity prediction
    Jixing Liu, Jiabing Guo, Henry H. Y. Tong +1
    Journal of CheminformaticsPeer-reviewedCC BY-NC-ND 4.0Theoretical & computational chemistryCrossref
  3. Explainable machine learning reveals a compact, low-dimensional geometric basis underlying sheet and skeletal TPMS architected materials
    Georgios Mermigkis, Chrysovalantis-Michail Liakopoulos, Dimitrios C. Rodopoulos +2
    Scientific ReportsPeer-reviewedCC BY-NC-ND 4.0Full textMaterials & polymersCrossref
  4. Growth driven phase transitions in Zinc Oxide nanoparticles through machine-learning assisted simulations
    Quentin Gromoff, Magali Benoit, Jacek Goniakowski +2
    NanoscalePeer-reviewedPublisher termsFull textMaterials & polymersHAL
  5. Machine-learning-based flow-synthesis of platinum-group-metal high-entropy alloy nanoparticles
    Daiki Takahashi, Hirotaka Ashitani, Shogo Kawaguchi +7
    Communications ChemistryPeer-reviewedCC BY 4.0General chemistryNatureCrossref

Cite

BibTeXRIS
BibTeX
@article{kurban2026explainable,
  title = {{Explainable machine learning reveals orbital design rules for reversible H2 adsorption on single-atom-doped TiO2 nanoparticles}},
  author = {Mustafa Kurban and Can Polat and Erchin Serpedin and Hasan Kurban},
  journal = {Materials \& Design},
  year = {2026},
  month = jun,
  doi = {10.1016/j.matdes.2026.116401},
  url = {https://doi.org/10.1016/j.matdes.2026.116401},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Explainable machine learning reveals orbital design rules for reversible H2 adsorption on single-atom-doped TiO2 nanoparticles
AU  - Mustafa Kurban
AU  - Can Polat
AU  - Erchin Serpedin
AU  - Hasan Kurban
PY  - 2026
DA  - 2026/06/12
JO  - Materials & Design
DO  - 10.1016/j.matdes.2026.116401
UR  - https://doi.org/10.1016/j.matdes.2026.116401
AB  - Hydrogen is a promising energy carrier for decarbonized technologies, but reversible on-board storage remains challenging because efficient uptake and release under near-ambient conditions require a narrow adsorption-energy window. Here, we combine high-throughput electronic-structure calculations with explainable machine learning to uncover the governing factors of molecular hydrogen adsorption on single-atom-doped TiO 2 nanoparticles. Screening 30 dopants across perpendicular and parallel H 2 adsorption geometries reveals substantial variation in adsorption strength and clear configuration-dependent behavior. From a broad descriptor space, a three-stage feature-selection strategy identifies eight robust predictors, which are further reduced through symbolic regression to an analytical and interpretable model. The resulting descriptor space is governed by H–H activation, dopant–H 2 distance, and -electron rearrangement. Adsorption within the target window is associated with balanced -donation and -back-donation, with Group 4 and 5 transition metals emerging as the most robust candidates across geometries. Combined with Langmuir-based desorption estimates, these results provide a mechanism-based screening rule for reversible hydrogen storage on doped TiO 2 .
SN  - 0264-1275
SN  - 1873-4197
ER  -