Accelerating the Discovery of Abiotic Vesicles with AI-Guided Automated Experimentation.

The first protocells are speculated to have arisen from the self-assembly of simple abiotic carboxylic acids, alcohols, and other amphiphiles into vesicles. To study the complex process of vesicle formation, we combined laboratory automation with AI-guided experimentation to accelerate the discovery of specific compositions and underlying principles governing vesicle formation. Using a low-cost commercial liquid handling robot, we automated experimental procedures, enabling high-throughput testing of various reaction conditions for mixtures of seven (7) amphiphiles.

No difference in early functional outcome between the direct anterior approach and posterior approach in patients following total hip arthroplasty.

Background: Objective and subjective outcomes in the direct anterior approach (DAA) and posterior approach (PA) in total hip arthroplasty (THA) were assessed in this study, using the Oxford Hip Score (OHS) as primary outcome. Pain, 3 objective performance-based tests, surgical time, blood loss and length of stay were assessed as secondary outcomes.

Methods: Patients with primary end-stage osteoarthritis were prospectively enrolled by shared decision making for the DAA (32 patients) or PA (26 patients).

Large Language Models for Inorganic Synthesis Predictions.

We evaluate the effectiveness of pretrained and fine-tuned large language models (LLMs) for predicting the synthesizability of inorganic compounds and the selection of precursors needed to perform inorganic synthesis. The predictions of fine-tuned LLMs are comparable to─and sometimes better than─recent bespoke machine learning models for these tasks but require only minimal user expertise, cost, and time to develop. Therefore, this strategy can serve both as an effective and strong baseline for future machine learning studies of various chemical applications and as a practical tool for experimental chemists.

Active Learning of Ligands That Enhance Perovskite Nanocrystal Luminescence.

Ligands play a critical role in the optical properties and chemical stability of colloidal nanocrystals (NCs), but identifying ligands that can enhance NC properties is daunting, given the high dimensionality of chemical space. Here, we use machine learning (ML) and robotic screening to accelerate the discovery of ligands that enhance the photoluminescence quantum yield (PLQY) of CsPbBr perovskite NCs. We developed a ML model designed to predict the relative PL enhancement of perovskite NCs when coordinated with a ligand selected from a pool of 29,904 candidate molecules.