论文标题
轨道混合器：使用原子轨道特征进行分子波形的依赖性预测
Orbital Mixer: Using Atomic Orbital Features for Basis Dependent Prediction of Molecular Wavefunctions
论文作者
论文摘要
在大规模上利用AB的启动数据使能够开发能够非常准确且快速的分子属性预测的机器学习模型。许多先前作品的中央范式集中在仅生成一组固定属性的预测。相反，最近的研究线旨在通过分子波形明确学习电子结构，可以直接得出其他量子化学特性。虽然先前的方法仅作为仅原子构型的函数生成预测，但在这项工作中，我们提出了一种直接针对基础依赖信息以预测分子电子结构的替代方法。我们模型的主干轨道搅拌机在简单，直观且可扩展的结构中使用MLP混合层，并实现了与最先进的ART相比，具有竞争性的哈密顿和分子轨道能量以及系数预测精度。
Leveraging ab initio data at scale has enabled the development of machine learning models capable of extremely accurate and fast molecular property prediction. A central paradigm of many previous works focuses on generating predictions for only a fixed set of properties. Recent lines of research instead aim to explicitly learn the electronic structure via molecular wavefunctions from which other quantum chemical properties can directly be derived. While previous methods generate predictions as a function of only the atomic configuration, in this work we present an alternate approach that directly purposes basis dependent information to predict molecular electronic structure. The backbone of our model, Orbital Mixer, uses MLP Mixer layers within a simple, intuitive, and scalable architecture and achieves competitive Hamiltonian and molecular orbital energy and coefficient prediction accuracies compared to the state-of-the-art.
