Point-Defect Optical Transitions and Thermal Ionization Energies from Quantum Monte Carlo Methods: Application to F-center Defect in MgO

We present an approach to calculation of point defect optical and thermal ionization energies based on the highly accurate quantum Monte Carlo methods. The use of an inherently many-body theory that directly treats electron correlation offers many improvements over the typically-employed density functional theory Kohn-Sham description. In particular, the use of quantum Monte Carlo methods can help overcome the band gap problem and obviate the need for ad-hoc corrections. We demonstrate our approach to the calculation of the optical and thermal ionization energies of the F-center defect in magnesium oxide, and obtain excellent agreement with experimental and/or other high-accuracy computational results

Leveraging Language Representation for Material Recommendation, Ranking, and Exploration

Data-driven approaches for material discovery and design have been accelerated by emerging efforts in machine learning. While there is enormous progress towards learning the structure to property relationship of materials, methods that allow for general representations of crystals to effectively explore the vast material search space and identify high-performance candidates remain limited. In this work, we introduce a material discovery framework that uses natural language embeddings derived from material science-specific language models as representations of compositional and structural features. The discovery framework consists of a joint scheme that, given a query material, first recalls candidates based on representational similarity, and ranks the candidates based on target properties through multi-task learning. The contextual knowledge encoded in language representations is found to convey information about material properties and structures, enabling both similarity analysis for recall, and multi-task learning to share information for related properties. By applying the discovery framework to thermoelectric materials, we demonstrate diversified recommendations of prototype structures and identify under-studied high-performance material spaces, including halide perovskite, delafossite-like, and spinel-like structures. By leveraging material language representations, our framework provides a generalized means for effective material recommendation, which is task-agnostic and can be applied to various material systems

Insulator-to-Metal Transition in Selenium-Hyperdoped Silicon: Observation and Origin

Hyperdoping has emerged as a promising method for designing semiconductors with unique optical and electronic properties, although such properties currently lack a clear microscopic explanation. Combining computational and experimental evidence, we probe the origin of sub-band gap optical absorption and metallicity in Se-hyperdoped Si. We show that sub-band gap absorption arises from direct defect-to-conduction band transitions rather than free carrier absorption. Density functional theory predicts the Se-induced insulator-to-metal transition arises from merging of defect and conduction bands, at a concentration in excellent agreement with experiment. Quantum Monte Carlo calculations confirm the critical concentration, demonstrate that correlation is important to describing the transition accurately, and suggest that it is a classic impurity-driven Mott transition.Comment: 5 pages, 3 figures (PRL formatted