Accelerating computational discovery of porous solids through improved navigation of energy-structure-function maps

Structural Design and Energy and Environmental Applications of Hydrogen-Bonded Organic Frameworks: A Systematic Review

Hydrogen-bonded organic frameworks (HOFs) are emerging porous materials that show high structural flexibility, mild synthetic conditions, good solution processability, easy healing and regeneration, and good recyclability. Although these properties give them many potential multifunctional applications, their frameworks are unstable due to the presence of only weak and reversible hydrogen bonds. In this work, the development history and synthesis methods of HOFs are reviewed, and categorize their structural design concepts and strategies to improve their stability. More importantly, due to the significant potential of the latest HOF-related research for addressing energy and environmental issues, this work discusses the latest advances in the methods of energy storage and conversion, energy substance generation and isolation, environmental detection and isolation, degradation and transformation, and biological applications. Furthermore, a discussion of the coupling orientation of HOF in the cross-cutting fields of energy and environment is presented for the first time. Finally, current challenges, opportunities, and strategies for the development of HOFs to advance their energy and environmental applications are discussed.

Deep Learning-Based Chemical Similarity for Accelerated Organic Light-Emitting Diode Materials Discovery

Thermally activated delayed fluorescence (TADF) material has attracted great attention as a promising metal-free organic light-emitting diode material with a high theoretical efficiency. To accelerate the discovery of novel TADF materials, computer-aided material design strategies have been developed. However, they have clear limitations due to the accessibility of only a few computationally tractable properties. Here, we propose TADF-likeness, a quantitative score to evaluate the TADF potential of molecules based on a data-driven concept of chemical similarity to existing TADF molecules. We used a deep autoencoder to characterize the common features of existing TADF molecules with common chemical descriptors. The score was highly correlated with the four essential electronic properties of TADF molecules and had a high success rate in large-scale virtual screening of millions of molecules to identify promising candidates at almost no cost, validating its feasibility for accelerating TADF discovery. The concept of TADF-likeness can be extended to other fields of materials discovery.

Inferring energy-composition relationships with Bayesian optimization enhances exploration of inorganic materials

Computational exploration of the compositional spaces of materials can provide guidance for synthetic research and thus accelerate the discovery of novel materials. Most approaches employ high-throughput sampling and focus on reducing the time for energy evaluation for individual compositions, often at the cost of accuracy. Here, we present an alternative approach focusing on effective sampling of the compositional space. The learning algorithm PhaseBO optimizes the stoichiometry of the potential target material while improving the probability of and accelerating its discovery without compromising the accuracy of energy evaluation.

Multifunctional Porous Hydrogen-Bonded Organic Frameworks: Current Status and Future Perspectives

Hydrogen-bonded organic frameworks (HOFs), self-assembled from organic or metalated organic building blocks (also termed as tectons) by hydrogen bonding, π-π stacking, and other intermolecular interactions, have become an emerging class of multifunctional porous materials. So far, a library of HOFs with high porosity has been synthesized based on versatile tectons and supramolecular synthons. Benefiting from the flexibility and reversibility of H-bonds, HOFs feature high structural flexibility, mild synthetic reaction, excellent solution processability, facile healing, easy regeneration, and good recyclability. However, the flexible and reversible nature of H-bonds makes most HOFs suffer from poor structural designability and low framework stability. In this Outlook, we first describe the development and structural features of HOFs and summarize the design principles of HOFs and strategies to enhance their stability. Second, we highlight the state-of-the-art development of HOFs for diverse applications, including gas storage and separation, heterogeneous catalysis, biological applications, sensing, proton conduction, and other applications. Finally, current challenges and future perspectives are discussed.

Computational modeling to assist in the discovery of supramolecular materials

Computational modeling is increasingly used to assist in the discovery of supramolecular materials. Supramolecular materials are typically primarily built from organic components that are self-assembled through noncovalent bonding and have potential applications, including in selective binding, sorption, molecular separations, catalysis, optoelectronics, sensing, and as molecular machines. In this review, the key areas where computational prediction can assist in the discovery of supramolecular materials, including in structure prediction, property prediction, and the prediction of how to synthesize a hypothetical material are discussed, before exploring the potential impact of artificial intelligence techniques on the field. Throughout, the importance of close integration with experimental materials discovery programs will be highlighted. A series of case studies from the author's work across some different supramolecular material classes will be discussed, before finishing with a discussion of the outlook for the field.