Liu, Z.-P. & Hu, P. General rules for predicting where a catalytic reaction should occur on metal surfaces: a density functional theory study of C–H and C–O bond breaking/making on flat, stepped, and kinked metal surfaces. J. Am. Chem. Soc. 125, 1958–1967 (2003).
Google Scholar
Alavi, A., Hu, P., Deutsch, T., Silvestrelli, P. L. & Hutter, J. CO oxidation on Pt(111): an ab initio density functional theory study. Phys. Rev. Lett. 80, 3650–3653 (1998).
Google Scholar
Oganov, A. R., Pickard, C. J., Zhu, Q. & Needs, R. J. Structure prediction drives materials discovery. Nat. Rev. Mater. 4, 331–348 (2019).
Google Scholar
Behler, J. & Parrinello, M. Generalized neural-network representation of high-dimensional potential-energy surfaces. Phys. Rev. Lett. 98, 146401 (2007).
Google Scholar
Behler, J. First principles neural network potentials for reactive simulations of large molecular and condensed systems. Angew. Chem. Int. Ed. 56, 12828–12840 (2017).
Google Scholar
Zuo, Y. et al. Performance and cost assessment of machine learning interatomic potentials. J. Phys. Chem. A 124, 731–745 (2020).
Google Scholar
Huang, S.-D., Shang, C., Zhang, X.-J. & Liu, Z.-P. Material discovery by combining stochastic surface walking global optimization with a neural network. Chem. Sci. 8, 6327–6337 (2017).
Google Scholar
Ma, S., Shang, C. & Liu, Z.-P. Heterogeneous catalysis from structure to activity via SSW-NN method. J. Chem. Phys. 151, 050901 (2019).
Google Scholar
Wang, H., Zhang, L., Han, J. & E, W. DeePMD-kit: a deep learning package for many-body potential energy representation and molecular dynamics. Comput. Phys. Commun. 228, 178–184 (2018).
Google Scholar
Zhang, Y., Xia, J. & Jiang, B. Physically motivated recursively embedded atom neural networks: incorporating local completeness and nonlocality. Phys. Rev. Lett. 127, 156002 (2021).
Google Scholar
Batzner, S. et al. E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials. Nat. Commun. 13, 2453 (2022).
Google Scholar
Batatia, I. et al. The design space of E(3)-equivariant atom-centred interatomic potentials. Nat. Mach. Intell. 7, 56–67 (2025).
Google Scholar
Young, T. A., Johnston-Wood, T., Deringer, V. L. & Duarte, F. A transferable active-learning strategy for reactive molecular force fields. Chem. Sci. 12, 10944–10955 (2021).
Google Scholar
Smith, J. S. et al. Automated discovery of a robust interatomic potential for aluminum. Nat. Commun. 12, 1257 (2021).
Google Scholar
Xu, J., Xie, W., Han, Y. & Hu, P. Atomistic insights into the oxidation of flat and stepped platinum surfaces using large-scale machine learning potential-based grand-canonical Monte Carlo. ACS Catal. 12, 14812–14824 (2022).
Google Scholar
Liu, Y. & Guo, H. A Gaussian process based Δ-machine learning approach to reactive potential energy surfaces. J. Phys. Chem. A 127, 8765–8772 (2023).
Google Scholar
Nandi, A., Qu, C., Houston, P. L., Conte, R. & Bowman, J. M. Δ -machine learning for potential energy surfaces: a PIP approach to bring a DFT-based PES to CCSD(T) level of theory. J. Chem. Phys. 154, 051102 (2021).
Google Scholar
Morrow, J. D., Gardner, J. L. A. & Deringer, V. L. How to validate machine-learned interatomic potentials. J. Chem. Phys. 158, 121501 (2023).
Google Scholar
Smith, J. S., Nebgen, B., Lubbers, N., Isayev, O. & Roitberg, A. E. Less is more: sampling chemical space with active learning. J. Chem. Phys. 148, 241733 (2018).
Google Scholar
Schran, C. et al. Machine learning potentials for complex aqueous systems made simple. Proc. Natl Acad. Sci. USA 118, e2110077118 (2021).
Google Scholar
Shi, Y.-F., Kang, P.-L., Shang, C. & Liu, Z.-P. Methanol synthesis from CO2/CO mixture on Cu–Zn catalysts from microkinetics-guided machine learning pathway search. J. Am. Chem. Soc. 144, 13401–13414 (2022).
Google Scholar
Han, Y., Xu, J., Xie, W., Wang, Z. & Hu, P. Comprehensive study of oxygen vacancies on the catalytic performance of ZnO for CO/H2 activation using machine learning-accelerated first-principles simulations. ACS Catal. 13, 5104–5113 (2023).
Google Scholar
Han, Y., Xu, J., Xie, W., Wang, Z. & Hu, P. Unravelling the impact of metal dopants and oxygen vacancies on syngas conversion over oxides: a machine learning-accelerated study of CO activation on Cr-doped ZnO surfaces. ACS Catal. 13, 15074–15086 (2023).
Google Scholar
Deringer, V. L., Caro, M. A. & Csányi, G. Machine learning interatomic potentials as emerging tools for materials science. Adv. Mater. 31, 1902765 (2019).
Google Scholar
Luo, L.-H., Huang, S.-D., Shang, C. & Liu, Z.-P. Resolving activation entropy of CO oxidation under the solid–gas and solid–liquid conditions from machine learning simulation. ACS Catal. 12, 6265–6275 (2022).
Google Scholar
Xie, W., Xu, J., Ding, Y. & Hu, P. Quantitative studies of the key aspects in selective acetylene hydrogenation on Pd(111) by microkinetic modeling with coverage effects and molecular dynamics. ACS Catal. 11, 4094–4106 (2021).
Google Scholar
Xie, W., Xu, J., Chen, J., Wang, H. & Hu, P. Achieving theory–experiment parity for activity and selectivity in heterogeneous catalysis using microkinetic modeling. Acc. Chem. Res. 55, 1237–1248 (2022).
Google Scholar
Deng, B. et al. Systematic softening in universal machine learning interatomic potentials. NPJ Comput. Mater. 11, 9 (2025).
Google Scholar
Zeng, J., Cao, L., Xu, M., Zhu, T. & Zhang, J. Z. H. Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation. Nat. Commun. 11, 5713 (2020).
Google Scholar
Schaaf, L. L., Fako, E., De, S., Schäfer, A. & Csányi, G. Accurate energy barriers for catalytic reaction pathways: an automatic training protocol for machine learning force fields. NPJ Comput. Mater. 9, 180 (2023).
Google Scholar
Bernstein, N., Csányi, G. & Deringer, V. L. De novo exploration and self-guided learning of potential-energy surfaces. NPJ Comput. Mater. 5, 99 (2019).
Google Scholar
Morrow, J. D. & Deringer, V. L. Indirect learning and physically guided validation of interatomic potential models. J. Chem. Phys. 157, 104105 (2022).
Google Scholar
Zhang, S. et al. Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential. Nat. Chem. 16, 727–734 (2024).
Google Scholar
Chiang, Y. & Benner, P. mace-universal (Revision e5ebd9b). Hugging Face https://doi.org/10.57967/hf/1202 (2023).
Liao, Y.-L., Wood, B. M., Das, A. & Smidt, T. EquiformerV2: improved equivariant transformer for scaling to higher-degree representations. In Twelfth International Conference on Learning Representations (2024).
Chen, C. & Ong, S. P. A universal graph deep learning interatomic potential for the periodic table. Nat. Comput. Sci. 2, 718–728 (2022).
Google Scholar
Hauschild, A. et al. Normal-incidence X-ray standing-wave determination of the adsorption geometry of PTCDA on Ag(111): comparison of the ordered room-temperature and disordered low-temperature phases. Phys. Rev. B 81, 125432 (2010).
Google Scholar
Ruiz, V. G., Liu, W., Zojer, E., Scheffler, M. & Tkatchenko, A. Density-functional theory with screened van der Waals Interactions for the modeling of hybrid inorganic–organic systems. Phys. Rev. Lett. 108, 146103 (2012).
Google Scholar
Eren, B. et al. Activation of Cu(111) surface by decomposition into nanoclusters driven by CO adsorption. Science 351, 475–478 (2016).
Google Scholar
Xu, L. & Mavrikakis, M. Adsorbate-induced adatom formation on lithium, iron, cobalt, ruthenium, and rhenium surfaces. JACS Au 3, 2216–2225 (2023).
Google Scholar
Xie, W. & Hu, P. Influence of surface defects on activity and selectivity: a quantitative study of structure sensitivity of Pd catalysts for acetylene hydrogenation. Catal. Sci. Technol. 11, 5212–5222 (2021).
Google Scholar
Wu, J., Chen, D., Chen, J. & Wang, H. Structural and composition evolution of palladium catalyst for CO oxidation under steady-state reaction conditions. J. Phys. Chem. C 127, 6262–6270 (2023).
Google Scholar
Li, X.-T., Chen, L., Shang, C. & Liu, Z.-P. In situ surface structures of PdAg catalyst and their influence on acetylene semihydrogenation revealed by machine learning and experiment. J. Am. Chem. Soc. 143, 6281–6292 (2021).
Google Scholar
Baker, J. & Chan, F. The location of transition states: a comparison of Cartesian, Z-matrix, and natural internal coordinates. J. Comput. Chem. 17, 888–904 (1996).
Google Scholar
Cordero, B. et al. Covalent radii revisited. Dalton Trans. https://doi.org/10.1039/B801115J (2008).
Bartók, A. P., Kondor, R. & Csányi, G. On representing chemical environments. Phys. Rev. B 87, 184115 (2013).
Google Scholar
Behler, J. Atom-centered symmetry functions for constructing high-dimensional neural network potentials. J. Chem. Phys. 134, 074106 (2011).
Google Scholar
Thompson, A. P., Swiler, L. P., Trott, C. R., Foiles, S. M. & Tucker, G. J. Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials. J. Comput. Phys. 285, 316–330 (2015).
Google Scholar
Mahoney, M. W. & Drineas, P. CUR matrix decompositions for improved data analysis. Proc. Natl Acad. Sci. USA 106, 697–702 (2009).
Google Scholar
Harris, C. R. et al. Array programming with NumPy. Nature 585, 357–362 (2020).
Google Scholar
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).
Google Scholar
Perdew, J. P. et al. Restoring the density-gradient expansion for exchange in solids and surfaces. Phys. Rev. Lett. 100, 136406 (2008).
Google Scholar
Kresse, G. & Joubert, D. From ultrasoft pseudopotentials to the projector augmented-wave method. Phys. Rev. B 59, 1758–1775 (1999).
Google Scholar
Grimme, S., Antony, J., Ehrlich, S. & Krieg, H. A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H–Pu. J. Chem. Phys. 132, 154104 (2010).
Google Scholar
Thompson, A. P. et al. LAMMPS—a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales. Comput. Phys. Commun. 271, 108171 (2022).
Google Scholar
Steinhardt, P. J., Nelson, D. R. & Ronchetti, M. Bond-orientational order in liquids and glasses. Phys. Rev. B 28, 784–805 (1983).
Google Scholar
Menon, S., Leines, G. & Rogal, J. pyscal: a Python module for structural analysis of atomic environments. J. Open Source Softw. 4, 1824 (2019).
Google Scholar
Larsen, A. H. et al. The atomic simulation environment—a Python library for working with atoms. J. Phys. Condens. Matter 29, 273002 (2017).
Google Scholar
Wang, H.-F. & Liu, Z.-P. Comprehensive mechanism and structure-sensitivity of ethanol oxidation on platinum: new transition-state searching method for resolving the complex reaction network. J. Am. Chem. Soc. 130, 10996–11004 (2008).
Google Scholar
Henkelman, G., Uberuaga, B. P. & Jónsson, H. A climbing image nudged elastic band method for finding saddle points and minimum energy paths. J. Chem. Phys. 113, 9901–9904 (2000).
Google Scholar
Xie, W. Electronic structure calculations. Figshare https://doi.org/10.6084/m9.figshare.29484686.v1 (2025).
