Generative Hierarchical Materials Search (2409.06762v1)

Abstract: Generative models trained at scale can now produce text, video, and more recently, scientific data such as crystal structures. In applications of generative approaches to materials science, and in particular to crystal structures, the guidance from the domain expert in the form of high-level instructions can be essential for an automated system to output candidate crystals that are viable for downstream research. In this work, we formulate end-to-end language-to-structure generation as a multi-objective optimization problem, and propose Generative Hierarchical Materials Search (GenMS) for controllable generation of crystal structures. GenMS consists of (1) a LLM that takes high-level natural language as input and generates intermediate textual information about a crystal (e.g., chemical formulae), and (2) a diffusion model that takes intermediate information as input and generates low-level continuous value crystal structures. GenMS additionally uses a graph neural network to predict properties (e.g., formation energy) from the generated crystal structures. During inference, GenMS leverages all three components to conduct a forward tree search over the space of possible structures. Experiments show that GenMS outperforms other alternatives of directly using LLMs to generate structures both in satisfying user request and in generating low-energy structures. We confirm that GenMS is able to generate common crystal structures such as double perovskites, or spinels, solely from natural language input, and hence can form the foundation for more complex structure generation in near future.

• The paper introduces GenMS, a hierarchical generative model combining LLMs, diffusion models, and GNNs to create viable crystal structures from text inputs.
• It demonstrates an over 80% success rate in generating desired crystal structures with low formation energies, validated by DFT.
• The approach streamlines materials discovery and paves the way for future research in multimodal generative models for complex structure generation.

Generative Hierarchical Materials Search

The paper "Generative Hierarchical Materials Search" introduces an advanced methodology for the generation of crystal structures from high-level natural language descriptions. This novel approach leverages the capabilities of large-scale generative models to navigate the intricate landscape of materials science, forming a multi-objective optimization problem. The introduced system, Generative Hierarchical Materials Search (GenMS), amalgamates the strengths of LLMs, diffusion models, and graph neural networks to efficiently generate and validate crystal structures.

Methodology

GenMS operates through a three-step hierarchical process:

1. High-Level LLM: This model interprets user-provided natural language inputs to generate intermediate textual data, such as chemical formulae.
2. Diffusion Model: Utilizing the output of the LLM, the diffusion model generates detailed crystal structures by transmuting the intermediate textual information into continuous-valued structural data.
3. Graph Neural Network (GNN): This network predicts the properties of the generated crystal structures, such as formation energy, to ensure their viability for further research.

GenMS implements a forward tree search algorithm during the inference phase, synthesizing the outputs from the LLM, the diffusion model, and the GNN to iterate through the space of potential crystal structures. This forward search mechanism allows for the identification of optimal structures that align closely with user specifications.

Numerical Results and Claims

The paper presents several strong numerical results, demonstrating the efficacy of GenMS in satisfying user requests and producing low-energy crystal structures:

• Generation Success: GenMS successfully generates crystal structures matching the desired user specifications over 80% of the time for major families such as double perovskites and spinels.
• Energy Efficiency: The structures generated by GenMS were characterized by low formation energies, as validated by Density Functional Theory (DFT) calculations.

Contrary to existing methodologies, such as direct use of pretrained LLMs, GenMS achieves a notably higher success rate in generating valid and viable structures, underscoring the advantage of its hierarchical approach.

Implications and Future Directions

The practical implications of GenMS are significant in the field of materials science. It provides a robust tool for researchers to generate potential crystal structures based purely on verbal descriptions. This capability can markedly streamline the discovery and exploration of novel materials, reducing reliance on predefined databases and facilitating the search for materials with specific properties.

Theoretically, GenMS opens avenues for further research in multimodal generative models, particularly in the application domains where natural language inputs need to be translated into precise, structured outputs. The hierarchical approach employed by GenMS can be extended to other domains, such as molecular generation or drug discovery, where the complexity of the target structures endorses a need for scalable and interpretable generative models.

Looking ahead, future developments in AI could potentially augment GenMS with even more refined control over the generation process. This could include enhanced models that integrate synthesizability constraints or predictive models for other physical properties, enabling end-to-end solutions from conceptual design to practical synthesis.

Conclusion

GenMS sets a precedent in the field of AI-driven materials science by demonstrating how hierarchical generative models can be harnessed to solve complex, multimodal optimization problems. It stands out in its ability to generate crystal structures from high-level descriptions, addressing a critical gap in the current methodologies. The framework's adaptability suggests promising future expansions, potentially revolutionizing how researchers interact with and manipulate the fundamental building blocks of materials.

In summary, GenMS represents a substantial advancement in the intersection of natural language processing and materials science, paving the way for increasingly sophisticated and user-friendly tools for material discovery.