Nigam Jigyasa, Pozdnyakov Sergey, Fraux Guillaume, Ceriotti Michele
Laboratory of Computational Science and Modeling, Institute of Materials, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland.
J Chem Phys. 2022 May 28;156(20):204115. doi: 10.1063/5.0087042.
Data-driven schemes that associate molecular and crystal structures with their microscopic properties share the need for a concise, effective description of the arrangement of their atomic constituents. Many types of models rely on descriptions of atom-centered environments, which are associated with an atomic property or with an atomic contribution to an extensive macroscopic quantity. Frameworks in this class can be understood in terms of atom-centered density correlations (ACDC), which are used as a basis for a body-ordered, symmetry-adapted expansion of the targets. Several other schemes that gather information on the relationship between neighboring atoms using "message-passing" ideas cannot be directly mapped to correlations centered around a single atom. We generalize the ACDC framework to include multi-centered information, generating representations that provide a complete linear basis to regress symmetric functions of atomic coordinates, and provide a coherent foundation to systematize our understanding of both atom-centered and message-passing and invariant and equivariant machine-learning schemes.
将分子和晶体结构与其微观性质相关联的数据驱动方案都需要对其原子组成的排列进行简洁、有效的描述。许多类型的模型依赖于对以原子为中心的环境的描述，这些环境与原子性质或原子对广泛宏观量的贡献相关联。这类框架可以用原子中心密度相关性（ACDC）来理解，ACDC被用作目标的体序、对称适应展开的基础。其他一些使用“消息传递”思想收集相邻原子之间关系信息的方案不能直接映射到以单个原子为中心的相关性上。我们将ACDC框架推广到包括多中心信息，生成能够提供完整线性基以回归原子坐标对称函数的表示，并为系统化我们对以原子为中心和消息传递以及不变和等变机器学习方案的理解提供一个连贯的基础。
