We present evidence that learned density functional theory (``DFT'') force fields are ready for ground state catalyst discovery. Our key finding is that relaxation using forces from a learned potential yields structures with similar or lower energy to those relaxed using the RPBE functional in over 50\% of evaluated systems, despite the fact that the predicted forces differ significantly from the ground truth. This has the surprising implication that learned potentials may be ready for replacing DFT in challenging catalytic systems such as those found in the Open Catalyst 2020 dataset. Furthermore, we show that a force field trained on a locally harmonic energy surface with the same minima as a target DFT energy is also able to find lower or similar energy structures in over 50\% of cases. This ``Easy Potential'' converges in fewer steps than a standard model trained on true energies and forces, which further accelerates calculations. Its success illustrates a key point: learned potentials can locate energy minima even when the model has high force errors. The main requirement for structure optimisation is simply that the learned potential has the correct minima. Since learned potentials are fast and scale linearly with system size, our results open the possibility of quickly finding ground states for large systems.

中文翻译：

---

习得的力场已准备好用于基态催化剂的发现

我们提供的证据表明，学习的密度泛函理论（“DFT”）力场已准备好用于基态催化剂的发现。我们的主要发现是，在超过 50% 的评估系统中，使用来自学习势能的力进行松弛产生的结构与使用 RPBE 函数松弛的结构具有相似或更低的能量，尽管预测的力与实际情况有显着差异。这具有令人惊讶的暗示，即在具有挑战性的催化系统（例如 Open Catalyst 2020 数据集中发现的那些）中，学习潜力可能已准备好替代 DFT。此外，我们表明，在与目标 DFT 能量具有相同最小值的局部谐波能量表面上训练的力场也能够在超过 50% 的情况下找到更低或相似的能量结构。这种“容易的潜力” 与在真实能量和力上训练的标准模型相比，收敛的步骤更少，这进一步加快了计算速度。它的成功说明了一个关键点：即使模型有很大的力误差，学习的电位也可以定位能量最小值。结构优化的主要要求只是学习潜力具有正确的最小值。由于学习的潜力是快速的并且与系统大小成线性关系，我们的结果为快速找到大型系统的基态提供了可能性。