Large language models design sequence-defined macromolecules via evolutionary optimization

We demonstrate the ability of a large language model to perform evolutionary optimization for materials discovery. Anthropic's Claude 3.5 model outperforms an active learning scheme with handcrafted surrogate models and an evolutionary algorithm in selecting monomer sequences to produce targeted morphologies in macromolecular self-assembly. Utilizing pre-trained language models can potentially reduce the need for hyperparameter tuning while offering new capabilities such as self-reflection. The model performs this task effectively with or without context about the task itself, but domain-specific context sometimes results in faster convergence to good solutions. Furthermore, when this context is withheld, the model infers an approximate notion of the task (e.g., calling it a protein folding problem). This work provides evidence of Claude 3.5's ability to act as an evolutionary optimizer, a recently discovered emergent behavior of large language models, and demonstrates a practical use case in the study and design of soft materials.