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Abstract Physics-informed machine learning (PIML), the combination of prior physics knowledge with data-driven machine learning models, has emerged as an effective means of mitigating a shortage of training data, increasing model generalizability, and ensuring physical plausibility of results. In this paper, we survey a wide variety of recent works in PIML and summarize them from three key aspects: 1) motivations of PIML, 2) physics knowledge in PIML, and 3) methods of physics knowledge integration in PIML. We additionally discuss current challenges and corresponding research opportunities in PIML. 

Metal-organic frameworks (MOFs) with tunable structures and unique host-guest chemistry have emerged as promising candidates for conductive materials. However, the tunability of conductivity and porosity in conductive MOFs and their interrelationship still lack a systematic study. Herein, we report the synthesis of a series of 3D copper MOFs (NU-4000 to NU-4003) using a triphenylene-based hexatopic carboxylate linker. By modulating the ratio of mixed solvents, distinct structural topologies and π-π stacking arrangements were achieved, resulting in electrical conductivity ranging from insulators (˂ 10-6 S/cm) to semiconductors (10-8 ~ 102 S/cm). Among them, NU-4003 features continuous π-π stacking and exhibits a conductivity of 1.7 × 10-6 S/cm. To further enhance conductivity, we encapsulated C60, a strong electron acceptor, within the circular channels of NU-4003, resulting in a remarkable conductivity increase to 140 S/cm with approximately 100% pore occupancy. Even at lower C60 loadings that leave 54% of the pore volume remaining accessible, the conductivity remains exceptionally high at 104 S/cm. This represents an eight-order magnitude enhancement and positions NU-4003-C60 as one of the most conductive 3D MOFs reported to date. This work integrates two charge transport pathways (through-space and electron donor and acceptor) into a single MOF host-guest material, achieving a significant enhancement in conductivity. This study demonstrates the potential of combining host-guest chemistry and π-π stacking to design conductive MOFs with permanent porosity maintained, providing a blueprint for the development of next-generation materials for electronic and energy-related applications. 

Artificial Intelligence (AI) is poised to revolutionize numerous aspects of human life, with healthcare among the most critical fields set to benefit from this transformation. Medicine remains one of the most challenging, expensive, and impactful sectors, with challenges such as information retrieval, data organization, diagnostic accuracy, and cost reduction. AI is uniquely suited to address these challenges, ultimately improving the quality of life and reducing healthcare costs for patients worldwide. Despite its potential, the adoption of AI in healthcare has been slower compared to other industries, highlighting the need to understand the specific obstacles hindering its progress. This review identifies the current shortcomings of AI in healthcare and explores its possibilities, realities, and frontiers to provide a roadmap for future advancements. 

Abstract Stereoselective ring-opening polymerization catalysts are used to produce degradable stereoregular poly(lactic acids) with thermal and mechanical properties that are superior to those of atactic polymers. However, the process of discovering highly stereoselective catalysts is still largely empirical. We aim to develop an integrated computational and experimental framework for efficient, predictive catalyst selection and optimization. As a proof of principle, we have developed a Bayesian optimization workflow on a subset of literature results for stereoselective lactide ring-opening polymerization, and using the algorithm, we identify multiple new Al complexes that catalyze either isoselective or heteroselective polymerization. In addition, feature attribution analysis uncovers mechanistically meaningful ligand descriptors, such as percent buried volume (%V_bur) and the highest occupied molecular orbital energy (E_HOMO), that can access quantitative and predictive models for catalyst development.