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Liu et al. J. Mater. Inf. 2025, 5, 27 I http://dx.doi.org/10.20517/jmi.2024.105 Page 5 of 18

ceed with the initialized parameters incorporating upstream knowledge. Our FT DP is constructed by this
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pre-training-fine-tuning methodology based on the publicly available pre-trained upstream DPA-2 model. All
fine-tuning and model validation in this work were performed using DeePMD-kit software in version 3.0.0
beta 3 [30] .

Double-to-single workflow for TS optimization
To investigate the catalytic reaction mechanism from a theoretical point of view, a crucial task is the optimiza-
tion of TS structures of each elementary reaction for acquiring the activation energy. The climbing-image
nudged elastic band (CI-NEB) [54,55] and the dimer method [56,57] are the two most popular TS optimization
methods in heterogeneous catalysis simulation, representing two types of TS optimization methods respec-
tively: double-ended methods that start from combining optimized initial state (IS) and final state (FS), and
single-endedmethodsthatarebasedononlyonestate, usuallyaguessedTS.Itiswell-knownthattheefficiency
of single-ended methods highly relies on the quality of the input structure, but the optimization will converge
quickly when the optimization reaches the quadratic region around TS. On the contrary, double-ended meth-
ods have no reliance on any guessed TS structure, but they tend to have convergence problems. In our work,
we combine these two types of methods together as a workflow, named double-to-single (D2S), implemented
intheatomicsimulationenvironment(ASE)package [58] (asillustratedinFigure1A)tomakegooduseofthem
for accelerating the TS optimization process. The D2S workflow uses CI-NEB first to generate a rough reac-
tion pathway with a relatively loose convergence criterion (usually 1.0 eV/Å for maximum of atomic forces),
and then a single-ended method, such as the dimer, or a better choice, Sella algorithm based on iterative Hes-
sian diagonalization and partitioned rational function optimization (P-RFO) [59,60] , is utilized by starting from
the maximum point of the NEB pathway for strict TS optimization with target convergence criterion (usually
0.05 eV/Å for maximum of atomic forces). The free energy corrections, including zero-point energy (ZPE)
and thermal corrections (translational, rotational and vibrational) for gas-phase molecules, as well as ZPE and
vibrational contribution for adsorbates, are also computed using ASE. All these codes are open-sourced in
the ATST-Tools suite [61] , which supports using ABACUS and our FT DP model as a property calculator. All
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structures in this part were visualized using ASE.

Genetic algorithm for global optimization
We employed the genetic algorithm (GA) implemented in ASE [62] , using FT DP as the energy-evaluation
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calculator. In our global optimization process, the initial and subsequent populations each consist of 80 mem-
bers, with each population exploring 80 new mutated candidates. If convergence is found difficult with this
default setting, the population size and number of mutated candidates will be increased to 100. The mutation
of candidates involves three operators implemented in the ASE-GA module: mirror (mirroring half atoms in
a randomly oriented cutting plane), rattle (perturbing a part of atoms with a small random displacement), and
permutation (switching the positions of a subset of atoms randomly), each with the same probability of 1/3.
The stopping criterion in GA is regarded as met when the best candidate does not change for ten generations
and the current generation number is at least 20. Following this, at least ten lowest-energy candidates are re-
fined with PBE-DFT single-point calculations to identify the most stable structure. When using GA to obtain
low-energy surface reconstructions, conventional implementation typically operates on an entire surface with
constraints only on the z-axis [63,64] . This approach is unnecessary for predicting surface reconstructions that
only involve local environments such as edge structures. In this work, we adopted a local setting implemented
in ASE, specifying the involved atoms and where they are generated [Figure 1B]. All structures in this part
were visualized using VESTA [65] .

Ab initio atomistic thermodynamics
To evaluate the stability of reconstructed surfaces with different compositions, the ab initio atomistic thermo-
dynamics theory developed by Reuter and Scheffler [66,67] was used in this work. In this way, the surface energy