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Large Language Models (LLMs) are emerging as powerful AI tools capable of transforming how building information is collected, processed, analyzed, and applied across diverse research areas. Their capabilities can help building operators, facility managers and other stakeholders such as designers, architects and engineers by providing actionable insights for decision-making across planning, construction, operations, and maintenance of buildings and facilities. This paper explores ten key questions concerning the role of LLMs in shaping sustainable, intelligent, and human-centric buildings. From fundamental definitions to advanced applications, we examine how LLMs facilitate decision-making across the life cycle of buildings and energy systems. LLMs can enhance life cycle assessments (LCA), building energy simulations, and real-time data integration, empowering more efficient and adaptive human-AI environments. They can also contribute to streamlining regulatory compliance, improving post-occupancy evaluations, and fostering more inclusive and participatory design processes. Additionally, this paper addresses the ethical challenges posed by LLMs, such as bias, data privacy, and environmental impacts, and explores their potentials in advancing intelligent digital twins (DT) for ongoing building operations and maintenance. Built upon our applied research using LLMs and the review of tools, datasets, and research gaps, we provide a forward-looking perspective on how LLMs can drive innovation, collaboration, and productivity in the built environment while supporting ethical and effective implementation. 

The ring polymer molecular dynamics (RPMD) rate theory is an efficient and accurate method for estimating rate coefficients of chemical reactions affected by nuclear quantum effects. The commonly used RPMD treatment of gas-phase bimolecular reactions adopts two dividing surfaces, one at the transition state and another in the reactant asymptote, where the translational partition function is separable from other partition functions and can be readily obtained. With some exceptions, however, this strategy is difficult to implement for processes on surfaces or in solutions, because reactants are often strongly coupled with the extended medium (surface or solvent) and, thus, non-separable. Under such circumstances, the RPMD rate theory with a single dividing surface (SDS) is better suited. However, most of its implementations adopted Cartesian forms of the reaction coordinate, which may not be ideal for describing complex reactions. Here, we present an SDS-based RPMD implementation, which is able to tackle the aforementioned challenges. This approach is demonstrated in four representative reactions, including the gas-phase H + H2 exchange reaction, gas-phase CH3NC isomerization, H recombinative desorption from Pt(111), and NO desorption from Pd(111). This implementation, which is applicable to both uni- and bi-molecular reactions, offers a unified treatment of gas-phase and surface reaction rate calculations on the same footing. 

Zeroth-order methods are extensively used in machine learning applications where gradients are infeasible or expensive to compute, such as black-box attacks, reinforcement learning, and language model fine-tuning. Existing optimization theory focuses on convergence to an arbitrary stationary point, but less is known about the implicit regularization that provides a fine-grained characterization of which particular solutions are reached. This paper shows that zeroth-order optimization with the standard two-point estimator favors solutions with small trace of Hessian, a measure widely used to distinguish between sharp and flat minima. The authors provide convergence rates of zeroth-order optimization to approximate flat minima for convex and sufficiently smooth functions, defining flat minima as minimizers that achieve the smallest trace of Hessian among all optimal solutions. Experiments on binary classification tasks with convex losses and language model fine-tuning support the theoretical findings. 

Although the dynamics of collisions between a molecule and a solid surface are ultimately quantum mechanical, decohering effects owing to the large number of interacting degrees of freedom typically obscure the wavelike nature of these events. However, a partial decoupling of internal molecular motion from external degrees of freedom can reveal striking interference effects despite significant momentum exchange between the molecule and the bath of surface vibrations. We report state-prepared and state-resolved measurements of methane scattering from a room-temperature gold surface that demonstrate total destructive interference between molecular states related by a reflection symmetry operation. High-contrast interference effects prevail for all processes investigated, including vibrationally excited and vibrationally inelastic collisions. The results demonstrate the distinctly quantum mechanical effect of discrete symmetries in molecular collision dynamics.