Search for: All records

Sequence-controlled copolymers can self-assemble into a wide assortment of complex architectures, with exciting applications in nanofabrication and personalized medicine. However, polymer synthesis is notoriously imprecise, and stochasticity in both chemical synthesis and self-assembly poses a significant challenge to tight control over these systems. While it is increasingly viable to design “protein-like” sequences, specifying each individual monomer in a chain, the effect of variability within those sequences has not been well studied. In this work, we performed nearly 15[thin space (1/6-em)]000 molecular dynamics simulations of sequence-controlled copolymer aggregates with varying level of sequence stochasticity. We utilized unsupervised learning to characterize the resulting morphologies and found that sequence variation leads to relatively smooth and predictable changes in morphology compared to ensembles of identical chains. Furthermore, structural response to sequence variation was accurately modeled using supervised learning, revealing several interesting trends in how specific families of sequences break down as monomer sequences become more variable. Our work presents a way forward in understanding and controlling the effect of sequence variation in sequence-controlled copolymer systems, which can hopefully be used to design advanced copolymer systems for technological applications in the future. 

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. 

Codes and data for "Large language models design sequence-defined macromolecules via evolutionary optimization" Note this repository contains codes and data files for the manuscript. This is a snapshot of the repository, frozen at the time of submission. Codes: LLM codes, other algorithms, postprocessing, visualization Data files: prompts, models, embeddings, LLM responses 

Rigid bodies, made of smaller composite beads, are commonly used to simulate anisotropic particles with molecular dynamics or Monte Carlo methods. To accurately represent the particle shape and to obtain smooth and realistic effective pair interactions between two rigid bodies, each body may need to contain hundreds of spherical beads. Given an interacting pair of particles, traditional molecular dynamics methods calculate all the inter-body distances between the beads of the rigid bodies within a certain distance. For a system containing many anisotropic particles, these distance calculations are computationally costly and limit the attainable system size and simulation time. However, the effective interaction between two rigid particles should only depend on the distance between their center of masses and their relative orientation. Therefore, a function capable of directly mapping the center of mass distance and orientation to the interaction energy between the two rigid bodies would completely bypass inter-bead distance calculations. It is challenging to derive such a general function analytically for almost any non-spherical rigid body. In this study, we have trained neural nets, powerful tools to fit nonlinear functions to complex datasets, to achieve this task. The pair configuration (center of mass distance and relative orientation) is taken as an input, and the energy, forces, and torques between two rigid particles are predicted directly. We show that molecular dynamics simulations of cubes and cylinders performed with forces and torques obtained from the gradients of the energy neural-nets quantitatively match traditional simulations that use composite rigid bodies. Both structural quantities and dynamic measures are in agreement, while achieving up to 23 times speedup over traditional molecular dynamics, depending on hardware and system size. The method presented here can, in principle, be applied to any irregular concave or convex shape with any pair interaction, provided that sufficient training data can be obtained. 

Polymer mechanochemistry offers attractive opportunities for using macroscopic forces to drive molecular-scale chemical transformations, but achieving efficient activation in bulk polymeric materials has remained challenging. Understanding how the structure and topology of polymer networks impact molecular-scale force distributions is critical for addressing this problem. Here we show that in block copolymer elastomers the molecular-scale force distributions and mechanochemical activation yields are strongly impacted by the molecular weight distribution of the polymers. We prepare bidisperse triblock copolymer elastomers with spiropyran mechanophores placed in either the short chains, the long chains, or both and show that the overall mechanochemical activation of the materials is dominated by the short chains. Molecular dynamics simulations reveal that this preferential activation occurs because pinning of the ends of the elastically effective midblocks to the glassy/rubbery interface forces early extension of the short chains. These results suggest that microphase segregation and network strand dispersity play a critical role in determining molecular-scale force distributions and suggest that selective placement of mechanophores in microphase-segregated polymers is a promising design strategy for efficient mechanochemical activation in bulk materials.