习得力场为发现基态催化剂做好准备
Learned Force Fields Are Ready For Ground State Catalyst Discovery
摘要：
我们提出的证据表明，学习密度泛函理论（"DFT“）力场准备基态催化剂的发现。我们的关键发现是，松弛使用的力量从一个学习的潜力产生类似或更低的能量结构，以那些放松使用RPBE功能在超过50%的评估系统，尽管预测的力量显着不同的地面真相。这具有令人惊讶的含义，即学习的潜力可能已经准备好在具有挑战性的催化系统中取代DFT，例如在Open Catalyst 2020数据集中发现的那些。此外，我们表明，在局部谐波能量表面上训练的力场具有与目标DFT能量相同的最小值，在超过50%的情况下也能够找到更低或相似的能量结构。这种“易势”比训练真实能量和力的标准模型收敛的步骤更少，这进一步加快了计算速度。它的成功说明了一个关键点：即使模型具有高力误差，学习势也可以定位能量最小值。结构优化的主要要求是简单地说，学习潜力有正确的最小值。由于学习的潜力是快速和规模与系统的大小呈线性关系，我们的研究结果打开了快速找到大型系统的基态的可能性。
英文摘要：
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.
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