We present an investigation into diffusion models for molecular generation,
with the aim of better understanding how their predictions compare to the
results of physics-based calculations. The investigation into these models is
driven by their potential to significantly accelerate electronic structure
calculations using machine learning, without requiring expensive
first-principles datasets for training interatomic potentials. We find that the
inference process of a popular diffusion model for de novo molecular generation
is divided into an exploration phase, where the model chooses the atomic
species, and a relaxation phase, where it adjusts the atomic coordinates to
find a low-energy geometry. As training proceeds, we show that the model
initially learns about the first-order structure of the potential energy
surface, and then later learns about higher-order structure. We also find that
the relaxation phase of the diffusion model can be re-purposed to sample the
Boltzmann distribution over conformations and to carry out structure
relaxations. For structure relaxations, the model finds geometries with ~10x
lower energy than those produced by a classical force field for small organic
molecules. Initializing a density functional theory (DFT) relaxation at the
diffusion-produced structures yields a >2x speedup to the DFT relaxation when
compared to initializing at structures relaxed with a classical force field.

通过对扩散模型进行调查，我们希望能更好地理解它们的预测与基于物理计算的结果之间的比较，以利用机器学习来显著加速电子结构计算，而无需昂贵的原理数据集进行训练。我们发现一种流行的扩散模型用于全新分子生成的推理过程可以分为探索阶段和弛豫阶段，其中模型选择原子种类的同时调整原子坐标以找到低能几何形状。随着训练的进行，我们发现该模型最初学习的是位能曲面的一阶结构，然后逐渐学习高阶结构。我们还发现扩散模型的弛豫阶段可以用于对构象进行玻尔兹曼分布采样，并进行结构弛豫。对于结构弛豫，该模型对小型有机分子的能量较经典力场产生的结构低约 10 倍。在密度泛函理论 (DFT) 弛豫中，从扩散生成的结构开始与从经典力场弛豫的结构开始相比，可以使 DFT 弛豫速度提高超过 2 倍。