ABSTRACT
AI has been widely applied in scientific scenarios, such as robots performing chemical synthetic actions to free researchers from monotonous experimental procedures. However, there exists a gap between human-readable natural language descriptions and machine-executable instructions, of which the former are typically in numerous chemical articles, and the latter are currently compiled manually by experts. We apply the latest technology of pre-trained models and achieve automatic transcription between descriptions and instructions. We design a concise and comprehensive schema of instructions and construct an open-source human-annotated dataset consisting of 3950 description-instruction pairs, with 9.2 operations in each instruction on average. We further propose knowledgeable pre-trained transcription models enhanced by multi-grained chemical knowledge. The performance of recent popular models and products showing great capability in automatic writing (e.g., ChatGPT) has also been explored. Experiments prove that our system improves the instruction compilation efficiency of researchers by at least 42%, and can generate fluent academic paragraphs of synthetic descriptions when given instructions, showing the great potential of pre-trained models in improving human productivity.
ABSTRACT
Controlled polymerization of cumulenic monomers represents a promising yet underdeveloped strategy toward well-defined alkyne polymers. Here we report a stereoelectronic effect-inspired approach using simple vinylidenecyclopropanes (VDCPs) as butatriene homologues in controlled radical ring-opening polymerizations. While being thermally stable, VDCPs mimic butatrienes via conjugation of the cyclopropane ring. This leads to exclusive terminal-selective propagation that affords a highly structurally regular alkyne-based backbone, featuring complete ring-opening and no backbiting regardless of polymerization conditions.
