Uncertainty quantification (UQ) has increasing importance in the building of robust high-performance and generalizable materials property prediction models. It can also be used active learning to train better models by focusing on gathering new training data from uncertain regions. There are several categories UQ methods, each considering different types uncertainty sources. Here, we conduct a ...

Since the discovery of graphene and related forms of two-dimensional (2D) atomic layer crystals, numerous studies have reported on the fundamental material aspects, such as the synthesis, the physical properties, and the electrical properties on the transistor level. With the advancement in large-area synthesis methods, system level integration to exploit the unique applications of these materi...

The binding energy of an electron in a material is a fundamental characteristic, which determines a wealth of important chemical and physical properties. For metal-organic frameworks this quantity is hitherto unknown. We present a general approach for determining the vacuum level of porous metal-organic frameworks and apply it to obtain the first ionization energy for six prototype materials in...

The adoption of machine learning in materials science has rapidly transformed property prediction. Hurdles limiting full capitalization recent advancements include the limited development methods to learn underlying interactions multiple elements as well relationships among properties facilitate prediction new composition spaces. To address these issues, we introduce Hierarchical Correlation Le...

The honeycomb lattice possesses a novel energy band structure, which is characterized by two distinct Dirac points in the Brillouin zone, dominating most of the physical properties of the honeycomb structure materials. However, up till now, the origin of the Dirac points is unclear yet. Here, we discover a hidden symmetry on the honeycomb lattice and prove that the existence of Dirac points is ...