[Paper Review] MeLM, a generative pretrained language modeling framework that solves forward and inverse mechanics problems

Markus J. Buehler|arXiv (Cornell University)|Jun 30, 2023

Machine Learning in Materials Science4 citations

TL;DR

MeLM is a generative, autoregressive language modeling framework that leverages self-attention mechanisms to solve forward and inverse mechanics problems across diverse materials and scales. By modeling multi-particle systems through learned interaction potentials, it achieves high performance in predicting mechanical behavior and designing novel hierarchical structures, including bio-inspired honeycombs, carbon nanotubes, and proteins.

ABSTRACT

We report a flexible multi-modal mechanics language model, MeLM, applied to solve various nonlinear forward and inverse problems, that can deal with a set of instructions, numbers and microstructure data. The framework is applied to various examples including bio-inspired hierarchical honeycomb design, carbon nanotube mechanics, and protein unfolding. In spite of the flexible nature of the model-which allows us to easily incorporate diverse materials, scales, and mechanical features-it performs well across disparate forward and inverse tasks. Based on an autoregressive attention-model, MeLM effectively represents a large multi-particle system consisting of hundreds of millions of neurons, where the interaction potentials are discovered through graph-forming self-attention mechanisms that are then used to identify relationships from emergent structures, while taking advantage of synergies discovered in the training data. We show that the model can solve complex degenerate mechanics design problems and determine novel material architectures across a range of hierarchical levels, providing an avenue for materials discovery and analysis. Looking beyond the demonstrations reported in this paper, we discuss other opportunities in applied mechanics and general considerations about the use of large language models in modeling, design, and analysis that can span a broad spectrum of material properties from mechanical, thermal, optical, to electronic.

Motivation & Objective

• To develop a flexible, multi-modal framework capable of solving complex forward and inverse mechanics problems across diverse materials and length scales.
• To enable the discovery of novel material architectures through end-to-end learning of mechanical behavior from heterogeneous data inputs including instructions, numbers, and microstructure data.
• To explore the application of large language models in mechanics, extending beyond text to include structural, mechanical, and multi-physics properties.
• To demonstrate the model's ability to handle degenerate design problems and identify emergent structural relationships in complex systems.
• To establish a foundation for using generative AI in materials discovery and multi-scale mechanical analysis, spanning mechanical, thermal, optical, and electronic properties.

Proposed method

• Adapts an autoregressive transformer-based architecture to model mechanical systems as sequences of particles and interactions.
• Employs graph-forming self-attention mechanisms to discover interaction potentials from multi-particle systems, enabling relational reasoning across particles.
• Integrates multi-modal inputs—textual instructions, numerical parameters, and microstructural data—into a unified generative modeling framework.
• Trains the model on diverse mechanics problems to learn emergent structural and mechanical relationships from data without explicit physical equations.
• Uses autoregressive generation to predict mechanical responses (forward problems) and inverse designs (e.g., target stiffness or deformation) from desired outputs.
• Leverages synergies in training data to generalize across hierarchical levels and material types, including biomaterials and nanomaterials.

Experimental results

Research questions

• RQ1 Can a generative language model effectively solve forward mechanics problems involving complex, nonlinear material responses?
• RQ2 Can the same framework perform inverse design tasks, such as identifying material architectures that achieve target mechanical properties?
• RQ3 How well can a single model generalize across disparate materials, scales, and mechanical features using only multi-modal input data?
• RQ4 To what extent can self-attention mechanisms in a transformer architecture discover meaningful interaction potentials in multi-particle systems?
• RQ5 Can such a model identify novel, high-performance material architectures in degenerate design spaces without explicit physical modeling?

Key findings

• MeLM successfully solves forward mechanics problems in bio-inspired hierarchical honeycombs, accurately predicting deformation and load-bearing responses.
• The model generates novel carbon nanotube architectures with tailored mechanical properties, demonstrating effective inverse design capabilities.
• MeLM predicts protein unfolding pathways with high fidelity, indicating its ability to model complex biomolecular mechanics.
• The framework generalizes across multiple material types and hierarchical levels, including nano- and micro-scale systems.
• Self-attention mechanisms effectively learn interaction potentials from data, enabling the model to infer structural relationships without prior physical laws.
• MeLM achieves strong performance on degenerate design problems, identifying multiple viable material architectures for a given mechanical target.