论文标题
从液态水中提取冰期：为什么机器学习水模型概括得很好
Extracting ice phases from liquid water: why a machine-learning water model generalizes so well
论文作者
论文摘要
我们研究了液态水和53条线之间的结构相似性，包括20个已知晶相。我们将这种相似性比较基于当地环境，这些环境由中心原子的一定截止半径内的原子组成。我们揭示了液态水通过使用一般原子描述符直接比较这些阶段的环境，并通过证明仅在液态水上训练的机器学习潜力可以预测密度，晶状体能量和振动的振动特性，从而探索了各种冰期的局部环境。在水棚中发现了对水相行为的灯光发现的当地环境的发现，并合理化了不同阶段之间水模型的可转移性。
We investigate the structural similarities between liquid water and 53 ices, including 20 knowncrystalline phases. We base such similarity comparison on the local environments that consist of atoms within a certain cutoff radius of a central atom. We reveal that liquid water explores the localenvironments of the diverse ice phases, by directly comparing the environments in these phases using general atomic descriptors, and also by demonstrating that a machine-learning potential trained on liquid water alone can predict the densities, the lattice energies, and vibrational properties of theices. The finding that the local environments characterising the different ice phases are found in water sheds light on water phase behaviors, and rationalizes the transferability of water models between different phases.
