AdsorbFlow: energy-conditioned flow matching enables fast and realistic adsorbate placement

TL;DR

AdsorbFlow is introduced, a deterministic generative model that learns an energy-conditioned vector field on the rigid-body configuration space of adsorbate translation and rotation via conditional flow matching and establishes that deterministic transport is both faster and more accurate than stochastic denoising for adsorbate placement.

Abstract

Identifying low-energy adsorption geometries on catalytic surfaces is a practical bottleneck for computational heterogeneous catalysis: the difficulty lies not only in the cost of density functional theory (DFT) but in proposing initial placements that relax into the correct energy basins. Conditional denoising diffusion has improved success rates, yet requires $\sim$100 iterative steps per sample. Here we introduce AdsorbFlow, a deterministic generative model that learns an energy-conditioned vector field on the rigid-body configuration space of adsorbate translation and rotation via conditional flow matching. Energy information enters through classifier-free guidance conditioning -- not energy-gradient guidance -- and sampling reduces to integrating an ODE in as few as 5 steps. On OC20-Dense with full DFT single-point verification, AdsorbFlow with an EquiformerV2 backbone achieves 61.4% SR@10 and 34.1% SR@1 -- surpassing AdsorbDiff (31.8% SR@1, 41.0% SR@10) at every evaluation level and AdsorbML (47.7% SR@10) -- while using 20 times fewer generative steps and achieving the lowest anomaly rate among generative methods (6.8%). On 50 out-of-distribution systems, AdsorbFlow retains 58.0% SR@10 with a MLFF-to-DFT gap of only 4~percentage points. These results establish that deterministic transport is both faster and more accurate than stochastic denoising for adsorbate placement.

Figures (1)

• Figure 1: AdsorbFlow overview.Training: A linear interpolant connects the relaxed pose $x_0$ (at $t{=}0$) to noise $x_1$ (at $t{=}1$), and a symmetry-aware E(3)-equivariant network predicts the velocity field. Sampling: Starting from initial noise $x_1$ at $t{=}1$, we integrate the ODE backward to $t{=}0$ using a Heun solver for $K$ steps (default $K{=}5$), applying classifier-free guidance (CFG). The generated placements are then relaxed via MLFF and verified with DFT.