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.
翻译:我们提出证据,证明学习了密度功能理论(“DFT” ) 的军力场已经可以用于地面催化剂的发现。我们的关键发现是,在经过评估的系统中,利用在50 ⁇ 以上使用RPBE功能(RPBE 功能)的已知潜在产量结构的力量放松到使用RPBE功能而放松的力量,尽管预测的力量与地面真理大不相同。这令人惊讶地意味着,在具有挑战性的催化系统(如在公开催化器2020数据集中发现的系统)中,学习的潜力可以取代DFT。此外,我们还表明,在具有与目标DFT能源相同的小型微粒子的地方训练的军力场也能在超过50 ⁇ 的情况下找到较低或类似的能源结构。“Easy潜能”比在真正能源和力量方面经过培训的标准模型(这种模型进一步加速计算)的步骤要少。它的成功表明,一个关键点是:即使在模型存在高力误差时,所学的潜力也可以定位能源微型。此外,结构优化的主要要求是,所学到的潜力是正确的微型状态。由于所学的潜力是迅速和规模的系统,结果是开放的。