Building performance simulation (BPS) is critical for understanding building dynamics and behavior, analyzing the performance of the built environment, optimizing energy efficiency, improving demand flexibility, and enhancing building resilience. However, conducting BPS is not trivial. Traditional BPS relies on accurate building energy models, which are primarily physics-based and heavily dependent on detailed building information, expert knowledge, and case-by-case model calibrations, significantly limiting their scalability. With the development of sensing technology and the increased availability of data, there is growing attention and interest in data-driven BPS. However, purely data-driven models often suffer from limited generalization ability and a lack of physical consistency, resulting in poor performance in real-world applications. To address these limitations, recent studies have begun integrating physics priors into data-driven models, a methodology known as physics-informed machine learning (PIML). PIML is an emerging field where its definitions, methodologies, evaluation criteria, application scenarios, and future directions remain open. To bridge those gaps, this study systematically reviews the state-of-the-art PIML for BPS, offering a comprehensive definition of PIML and comparing it to traditional BPS approaches regarding data requirements, modeling effort, performance, and computational cost. We also summarize the commonly used methodologies, validation approaches, application domains, available data sources, open-source packages, and testbeds. In addition, this study provides a general guideline for selecting appropriate PIML models based on BPS applications. Finally, this study identifies key challenges and outlines future research directions, providing a solid foundation and valuable insights to advance R&D of PIML in BPS.

Computational high-throughput screening was carried out to assess a large number of experimentally reported metal-organic frameworks (MOFs) and zeolites for their utility in hexane isomer separation. Through the work, we identified many MOFs and zeolites with high selectivity (SL+M > 10) for the group of n-hexane, 2-methylpentane, and 3-methylpentane (linear and monobranched isomers) versus 2,2-dimethylbutane and 2,3-dimethylbutane (dibranched isomers). This group of selective sorbents includes VICDOC (Fe2(BDP)3), a MOF with triangular pores that is known to exhibit high isomer selectivity and capacity. For three of these structures, the adsorption isotherms for a 10-component mixture of hexane and heptane isomers were calculated. Subsequent simulations of column breakthrough curves showed that the DEYVUA MOF exhibits a longer process cycle time than VICDOC MOF or MRE zeolite, which are previously reported, high-performing materials, illustrating the importance of capacity in designing MOFs for practical applications. Among the identified candidates, we synthesized and characterized a MOF in a new copper form with high predicted adsorbent capacity (qL+M > 1.2 mol/L) and moderately high selectivity (SL+M ≈ 10). Finally, we examined the role of pore shape in hexane isomer separations, especially of triangular-shaped pores. We show through the potential energy surface and three-dimensional siting analyses that linear alkanes do not populate the corners of narrow triangular channels and that structures with nontriangular pores can efficiently separate hexane isomers. Detailed thermodynamic analysis illustrates how differences in the free energy of adsorption contribute to shape-selective separation in nanoporous materials.