Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Predictive model for the discovery of sinter-resistant supports for metallic nanoparticle catalysts by interpretable machine learning

Nature Catalysis volume 8, pages 1038–1050 (2025)Cite this article

Subjects

Abstract

The metal–support interaction (MSI) critically influences the performance of supported nanocatalysts and their long-term stability, yet the factors governing MSIs are multifaceted and challenging to sort out. Here we combine first-principles neural network molecular dynamics (NN-MD) simulations with interpretable machine learning (iML) to shed light on the factors determining MSIs for Pt nanoparticles on diverse metal–oxide supports. Our approach reveals the atomic-scale dynamics of sintering mechanisms and identifies key features of oxide supports governing MSI. We find that the surface energy, surface oxygen bond order, surface dipole and work function of the support are dominant in Pt–oxide interactions. Leveraging these insights, we screened promising sinter-resistant supports for Pt nanoparticles from over 10,000 metal–oxide surfaces and validated some cases by Monte Carlo simulations and experiments. This work integrates iML with NN-MD to accelerate the understanding and discovery of stable supported nanocatalysts, and should be broadly applicable to numerous catalytic applications.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

USD 39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Pt NP/support MSI dataset construction via NN-MD simulations.
Fig. 2: iGAM performance in predicting Eadh and interpretation of feature importance and contributions.
Fig. 3: Case studies validating iGAM-derived correlations.
Fig. 4: Two-model approach for high-throughput screening of sintering-resistant Pt-based systems.
Fig. 5: Metropolis Monte Carlo simulations and experimental validation of Pt NPs on oxide supports.

Similar content being viewed by others

Data availability

Data supporting this study’s findings are provided in the article and Supplementary Information. The complete NN-MD trajectory files, associated statistical analysis data (including contact angle measurements and adhesion energy calculations), all statistical results from high-throughput screening, promising sintering-resistant supports, and their corresponding atomic structure files are publicly accessible via the Zenodo repository at https://doi.org/10.5281/zenodo.16878887 (ref. 41). All other relevant raw data are available from the corresponding authors upon reasonable request. Source data are provided with this paper.

Code availability

The software code for LASP and NN potentials used within the article are available on the website http://www.lasphub.com. The source code of iGAM (a.k.a. EBM) can be found at https://github.com/interpretml/interpret/. All well-established iGAM models are available in the GitHub repository (https://github.com/chenggoj/iGAM-MSI).

References

  1. Liu, W. et al. Single-atom dispersed Co–N–C catalyst: structure identification and performance for hydrogenative coupling of nitroarenes. Chem. Sci. 7, 5758–5764 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  2. Gholami, R., Alyani, M. & Smith, K. Deactivation of Pd catalysts by water during low temperature methane oxidation relevant to natural gas vehicle converters. Catalysts 5, 561–594 (2015).

    Article  CAS  Google Scholar 

  3. Yati, I., et al. Effects of sintering-resistance and large metal-support interface of alumina nanorod-stabilized Pt nanoparticle catalysts on the improved high temperature water gas shift reaction activity. Catal. Commun. 56, 11–16 (2014).

    Article  CAS  Google Scholar 

  4. Dai, Y., Lu, P., Cao, Z., Campbell, C. T. & Xia, Y. The physical chemistry and materials science behind sinter-resistant catalysts. Chem. Soc. Rev. 47, 4314–4331 (2018).

    Article  PubMed  CAS  Google Scholar 

  5. van Deelen, T. W., Hernández Mejía, C. & de Jong, K. P. Control of metal–support interactions in heterogeneous catalysts to enhance activity and selectivity. Nat. Catal. 2, 955–970 (2019).

    Article  Google Scholar 

  6. Chen, J. et al. Metal–support interactions for heterogeneous catalysis: mechanisms, characterization techniques and applications. J. Mater. Chem. A 11, 8540–8572 (2023).

    Article  CAS  Google Scholar 

  7. Campbell, C. T. & Sellers, J. R. V. Enthalpies and entropies of adsorption on well-defined oxide surfaces: experimental measurements. Chem. Rev. 113, 4106–4135 (2013).

    Article  PubMed  CAS  Google Scholar 

  8. Ro, I., Resasco, J. & Christopher, P. Approaches for understanding and controlling interfacial effects in oxide-supported metal catalysts. ACS Catal. 8, 7368–7387 (2018).

    Article  CAS  Google Scholar 

  9. Campbell, C. T. The energetics of supported metal nanoparticles: relationships to sintering rates and catalytic activity. Acc. Chem. Res. 46, 1712–1719 (2013).

    Article  PubMed  CAS  Google Scholar 

  10. Campbell, C. T. & Sellers, J. R. V. Anchored metal nanoparticles: effects of support and size on their energy, sintering resistance and reactivity. Faraday Discuss. 162, 9–20 (2013).

    Article  PubMed  CAS  Google Scholar 

  11. Bartholomew, C. H. Mechanisms of catalyst deactivation. Appl. Catal. A 212, 17–60 (2001).

    Article  CAS  Google Scholar 

  12. O’Connor, N. J., Jonayat, A. S. M., Janik, M. J. & Senftle, T. P. Interaction trends between single metal atoms and oxide supports identified with density functional theory and statistical learning. Nat. Catal. 1, 531–539 (2018).

    Article  Google Scholar 

  13. Leybo, D. et al. Metal-support interactions in metal oxide-supported atomic, cluster and nanoparticle catalysis. Chem. Soc. Rev. 28, 10450–10490 (2024).

    Article  Google Scholar 

  14. Hu, S. & Li, W.-X. Sabatier principle of metal-support interaction for design of ultrastable metal nanocatalysts. Science 374, 1360–1365 (2021).

    Article  PubMed  CAS  Google Scholar 

  15. Liu, J.-C. et al. Metal affinity of support dictates sintering of gold catalysts. J. Am. Chem. Soc. 144, 20601–20609 (2022).

    Article  PubMed  CAS  Google Scholar 

  16. Wang, T. et al. Nature of metal-support interaction for metal catalysts on oxide supports. Science 386, 915–920 (2024).

    Article  PubMed  CAS  Google Scholar 

  17. Esterhuizen, J. A., Goldsmith, B. R. & Linic, S. Interpretable machine learning for knowledge generation in heterogeneous catalysis. Nat. Catal. 5, 175–184 (2022).

    Article  Google Scholar 

  18. Omidvar, N. et al. Interpretable machine learning of chemical bonding at solid surfaces. J. Phys. Chem. Lett. 12, 11476–11487 (2021).

    Article  PubMed  CAS  Google Scholar 

  19. Xin, H., Mou, T., Pillai, H. S., Wang, S.-H. & Huang, Y. Interpretable machine learning for catalytic materials design toward sustainability. Acc. Mater. Res. 5, 22–34 (2024).

    Article  CAS  Google Scholar 

  20. Wang, H. et al. Scientific discovery in the age of artificial intelligence. Nature 620, 47–60 (2023).

    Article  PubMed  CAS  Google Scholar 

  21. Esterhuizen, J. A., Goldsmith, B. R. & Linic, S. Uncovering electronic and geometric descriptors of chemical activity for metal alloys and oxides using unsupervised machine learning. Chem. Catal. 1, 923–940 (2021).

    CAS  Google Scholar 

  22. Goldsmith, B. R., Esterhuizen, J., Liu, J. X., Bartel, C. J. & Sutton, C. Machine learning for heterogeneous catalyst design and discovery. AlChE J. 64, 2311–2323 (2018).

    Article  CAS  Google Scholar 

  23. Kang, P.-L., Shang, C. & Liu, Z.-P. Large-scale atomic simulation via machine learning potentials constructed by global potential energy surface exploration. Acc. Chem. Res. 53, 2119–2129 (2020).

    Article  PubMed  CAS  Google Scholar 

  24. Tran, R. et al. The open catalyst 2022 (OC22) dataset and challenges for oxide electrocatalysts. ACS Catal. 13, 3066–3084 (2023).

    Article  CAS  Google Scholar 

  25. Jain, A. et al. Commentary: the materials project: a materials genome approach to accelerating materials innovation. APL Mater. 1, 011002 (2013).

    Article  Google Scholar 

  26. Hansen, T. W., DeLaRiva, A. T., Challa, S. R. & Datye, A. K. Sintering of catalytic nanoparticles: particle migration or Ostwald ripening? Acc. Chem. Res. 46, 1720–1730 (2013).

    Article  PubMed  CAS  Google Scholar 

  27. Tran, R. et al. Surface energies of elemental crystals. Sci. Data 3, 160080 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Yu, J. et al. Highly stable Pt/CeO2 catalyst with embedding structure toward water-gas shift reaction. J. Am. Chem. Soc. 146, 1071–1080 (2023).

    Article  PubMed  Google Scholar 

  29. Esterhuizen, J. A., Goldsmith, B. R. & Linic, S. Theory-guided machine learning finds geometric structure-property relationships for chemisorption on subsurface alloys. Chem 6, 3100–3117 (2020).

    Article  CAS  Google Scholar 

  30. Lou, Y., Caruana, R., Gehrke, J. & Hooker, G. Accurate intelligible models with pairwise interactions. In Proc. 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ed. Dhillon, I. S.) 623–631 (ACM, 2013).

  31. Rudin, C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell. 1, 206–215 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Reshef, D. N. et al. Detecting novel associations in large datasets. Science 334, 1518–1524 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. Hemmingson, S. L. & Campbell, C. T. Trends in adhesion energies of metal nanoparticles on oxide surfaces: understanding support effects in catalysis and nanotechnology. ACS Nano 11, 1196–1203 (2017).

    Article  PubMed  CAS  Google Scholar 

  34. Lou, Y., Caruana, R. & Gehrke, J. Intelligible models for classification and regression. In Proc. 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ed. Yang, Q.) 150–158 (ACM, 2012).

  35. Hu, S. & Li, W.-X. Metal–support interaction controlled migration and coalescence of supported particles. Sci. China Technol. Sci. 62, 762–772 (2019).

    Article  Google Scholar 

  36. Limas, N. G. & Manz, T. A. Introducing DDEC6 atomic population analysis: part 4. Efficient parallel computation of net atomic charges, atomic spin moments, bond orders and more. RSC Adv. 8, 2678–2707 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Manz, T. A. Introducing DDEC6 atomic population analysis: part 3. Comprehensive method to compute bond orders. RSC Adv. 7, 45552–45581 (2017).

    Article  CAS  Google Scholar 

  38. Smith, J. et al. Sintering mechanism of Pt/Al2O3 in complex emission gases elucidated via in situ environmental STEM. ACS Mater. Lett. 6, 3301–3311 (2024).

    Article  CAS  Google Scholar 

  39. Huang, S.-D., Shang, C., Kang, P.-L., Zhang, X.-J. & Liu, Z.-P. LASP: fast global potential energy surface exploration. WIREs Comput. Mol. Sci. 9, e1415 (2019).

    Article  CAS  Google Scholar 

  40. Kang, P.-L., Shang, C. & Liu, Z.-P. Recent implementations in LASP 3.0: global neural network potential with multiple elements and better long-range description. Chin. J. Chem. Phys. 34, 583–590 (2021).

    Article  CAS  Google Scholar 

  41. Jiang, C. Predictive model for the discovery of sinter-resistant supports for metallic nanoparticle catalysts by interpretable machine learning. Zenodo https://doi.org/10.5281/zenodo.16878887 (2025).

  42. Shang, C. & Liu, Z.-P. in Quantum Chemistry in the Age of Machine Learning (ed. Dral, P. O.) 313–327 (Elsevier, 2023).

  43. Kresse, G. & Furthmüller, J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys. Rev. B 54, 11169–11186 (1996).

    Article  CAS  Google Scholar 

  44. Kresse, G. & Hafner, J. Ab initio molecular dynamics for liquid metals. Phys. Rev. B 47, 558–561 (1993).

    Article  CAS  Google Scholar 

  45. Kresse, G. & Hafner, J. Ab initio molecular-dynamics simulation of the liquid-metal-amorphous-semiconductor transition in germanium. Phys. Rev. B 49, 14251–14269 (1994).

    Article  CAS  Google Scholar 

  46. Perdew, J. P., Burke, K. & Ernzerhof, M. Generalized gradient approximation made simple. Phys. Rev. Lett. 77, 3865–3868 (1996).

    Article  PubMed  CAS  Google Scholar 

  47. Wang, Y., Kiziltas, A., Blanchard, P. & Walsh, T. R. ContactAngleCalculator: an automated, parametrized and flexible code for contact angle estimation in visual molecular dynamics. J. Chem. Inf. Model. 62, 6302–6308 (2022).

    Article  PubMed  CAS  Google Scholar 

  48. Humphrey, W., Dalke, A. & Schulten, K. VMD: Visual Molecular Dynamics. J. Mol. Graph. 14, 33–38 (1996).

    Article  PubMed  CAS  Google Scholar 

  49. Kresse, G. & Joubert, D. From ultrasoft pseudopotentials to the projector augmented-wave method. Phys. Rev. B 59, 1758–1775 (1999).

    Article  CAS  Google Scholar 

  50. Sun, W. et al. The thermodynamic scale of inorganic crystalline metastability. Sci. Adv. https://doi.org/10.1126/sciadv.1600225 (2016).

  51. Young, S. D., Chen, J., Sun, W., Goldsmith, B. R. & Pilania, G. Thermodynamic stability and anion ordering of perovskite oxynitrides. Chem. Mater. 35, 5975–5987 (2023).

    Article  CAS  Google Scholar 

  52. Wang, V., Xu, N., Liu, J.-C., Tang, G. & Geng, W.-T. VASPKIT: a user-friendly interface facilitating high-throughput computing and analysis using VASP code. Comput. Phys. Commun. 267, 108033 (2021).

    Article  CAS  Google Scholar 

  53. Ong, S. P. et al. The materials application programming interface (API): a simple, flexible and efficient API for materials data based on representational state transfer (REST) principles. Comput. Mater. Sci. 97, 209–215 (2015).

    Article  Google Scholar 

  54. Hong, Q.-J., Ushakov, S. V., van de Walle, A. & Navrotsky, A. Melting temperature prediction using a graph neural network model: from ancient minerals to new materials. Proc. Natl Acad. Sci. USA 119, e2209630119 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  55. Zandkarimi, B., Poths, P. & Alexandrova, A. N. When fluxionality beats size selection: acceleration of Ostwald ripening of sub-nano clusters. Angew. Chem. 133, 12080–12089 (2021).

    Article  Google Scholar 

  56. Zhai, H. & Alexandrova, A. N. Ensemble-average representation of Pt clusters in conditions of catalysis accessed through GPU accelerated deep neural network fitting global optimization. J. Chem. Theory Comput. 12, 6213–6226 (2016).

    Article  PubMed  CAS  Google Scholar 

  57. Lide, D. R. (ed.) in CRC Handbook of Chemistry and Physics 88th edn 12–124 (CRC Press, 2008).

Download references

Acknowledgements

This material is based on work supported by NSF DMREF #2116646 (B.R.G. and S.L.). Experimental work was supported by the US DOE Office of Basic Energy Sciences, Division of Chemical Sciences (DE-SC0021008; S.L.).

Author information

Authors and Affiliations

  1. Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA

    Chenggong Jiang, Bill Yan, Bryan R. Goldsmith & Suljo Linic

  2. Catalysis Science and Technology Institute, University of Michigan, Ann Arbor, MI, USA

    Chenggong Jiang, Bill Yan, Bryan R. Goldsmith & Suljo Linic

Authors
  1. Chenggong Jiang
    View author publications

    Search author on:PubMed Google Scholar

  2. Bill Yan
    View author publications

    Search author on:PubMed Google Scholar

  3. Bryan R. Goldsmith
    View author publications

    Search author on:PubMed Google Scholar

  4. Suljo Linic
    View author publications

    Search author on:PubMed Google Scholar

Contributions

C.J. wrote the original draft, and performed all calculations and analysis. B.Y. performed all experiments. B.R.G. is a PhD co-adviser to C.J., and he supervised the project, analysed the data and contributed to writing. S.L. is a PhD co-adviser to C.J. and PhD adviser to B.Y., and he supervised the project, analysed the data and contributed to writing.

Corresponding authors

Correspondence to Bryan R. Goldsmith or Suljo Linic.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Catalysis thanks the anonymous reviewers for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information (download PDF )

Supplementary Methods, Figs. 1–37 and Tables 1–5.

Supplementary Video 1 (download MP4 )

NN-MD simulation for 3 nm Pt NP on Ce2O3 (100)-Ce terminated surface.

Supplementary Video 2 (download MP4 )

NN-MD simulation for 3 nm Pt NP on CeO2 (110) surface.

Supplementary Video 3 (download MP4 )

NN-MD simulation for 3 nm Pt NP on anatase-TiO2 (101) surface.

Supplementary Video 4 (download MP4 )

NN-MD simulation for one 3 nm Pt NP surrounded by four 1 nm Pt NPs on Ce2O3 (100)-Ce terminated surface.

Supplementary Video 5 (download MP4 )

NN-MD simulation for one 3 nm Pt NP surrounded by four 1 nm Pt NPs on CeO2 (110) surface.

Supplementary Video 6 (download MP4 )

NN-MD simulation for one 3 nm Pt NP surrounded by four 1 nm Pt NPs on anatase-TiO2 (101) surface.

Supplementary Data 1 (download XLSX )

High-throughput screening results summary.

Supplementary Data 2 (download XLSX )

Promising sinter-resistant supports materials summary.

Source data

Source Data Fig. 1 (download XLSX )

Statistical source data.

Source Data Fig. 2 (download XLSX )

Statistical source data.

Source Data Fig. 3 (download XLSX )

Statistical source data.

Source Data Fig. 4 (download XLSX )

Statistical source data.

Source Data Fig. 5 (download XLSX )

Statistical source data.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jiang, C., Yan, B., Goldsmith, B.R. et al. Predictive model for the discovery of sinter-resistant supports for metallic nanoparticle catalysts by interpretable machine learning. Nat Catal 8, 1038–1050 (2025). https://doi.org/10.1038/s41929-025-01417-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Version of record:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41929-025-01417-3

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing