Hybrid metal nanoparticles, consisting of a nano-crystalline metal core and a protecting shell of organic ligand molecules, have applications in diverse areas such as biolabeling, catalysis, nanomedicine, and solar energy. Despite a rapidly growing database of experimentally determined atom-precise nanoparticle structures and their properties, there has been no successful, systematic way to predict the atomistic structure of the metal-ligand interface. Here, we devise and validate a general method to predict the structure of the metal-ligand interface of ligand-stabilized gold and silver nanoparticles, based on information about local chemical environments of atoms in experimental data. In addition to predicting realistic interface structures, our method is useful for investigations on the steric effects at the metal-ligand interface, as well as for predicting isomers and intermediate structures induced by thermal dynamics or interactions with the environment. Our method is applicable to other hybrid nanomaterials once a suitable set of reference structures is available.

Knee Osteoarthritis (KOA), a leading cause of disability worldwide, is challenging to detect early due to subtle radiographic indicators. Diverse, extensive datasets are needed but are challenging to compile because of privacy, data collection limitations, and the progressive nature of KOA. However, a model capable of projecting genuine radiographs into different OA stages could augment data pools, enhance algorithm training, and offer pre-emptive prognostic insights. In this study, we developed a Cycle-Consistent Adversarial Network (CycleGAN) to generate synthetic past and future stages of KOA on any genuine radiograph. The model's effectiveness was validated through its impact on a KOA specialized Convolutional Neural Network (CNN). Transformations towards synthetic future disease states resulted in 83.76% of none-to-doubtful stage images being classified as moderate-to-severe stages, while retroactive transformations led to 75.61% of severe-stage images being classified as none-to-doubtful stages. Similarly, transformations from mild stages achieved 76.00% correct classification towards future stages and 69.00% for past stages. The CycleGAN demonstrated an exceptional ability to expand the knee joint space and eliminate bone-outgrowths (osteophytes), key radiographic indicators of disease progression. These results signify a promising potential for enhancing diagnostic models, data augmentation, and educational and prognostic uses. Nevertheless, further refinement, validation, and a broader evaluation process encompassing both CNN-based assessments and expert medical feedback are emphasized for future research and development.