论文标题
以原子为中心表示和通信的机器学习方案的统一理论
Unified theory of atom-centered representations and message-passing machine-learning schemes
论文作者
论文摘要
将分子和晶体结构与它们的显微特性相关联的数据驱动方案共享了对其原子成分排列的简洁有效描述。许多类型的模型都依赖于以原子为中心环境的描述，这些环境与原子特性或对广泛的宏观数量的原子贡献相关。可以从以原子为中心的密度相关性（ACDC）来理解此类中的框架，这些密度相关性（ACDC）被用作靶标的身体订购的，对称性适应的膨胀的基础。其他几个方案，这些方案收集了有关使用“通信”想法之间相邻原子之间关系的信息，不能直接映射到以单个原子为中心的相关性。我们将ACDC框架概括为包括以上的信息，生成表示形式的表示形式，这些表示提供了完整的线性基础来回归原子坐标的对称函数，并提供了一个连贯的基础，以系统化我们对以原子为中心和消息传播的理解，不变的和不变的机器机器。
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, that 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), that 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 provides a coherent foundation to systematize our understanding of both atom-centered and message-passing, invariant and equivariant machine-learning schemes.
