Conditional Generative Models Enable Targeted Exploration of MAX Phase Design Space

TL;DR

This paper demonstrates how a conditioned large language model can generate novel MAX phase structures with targeted properties, validated by DFT calculations, advancing materials discovery.

Contribution

It introduces a conditioned generative model for MAX phases that can efficiently explore and generate stable, compositionally novel structures in targeted regions of phase space.

Findings

Model generates structures consistent with experimental trends.

Conditioning doubles the rate of novel stable structure generation.

Half of the compositionally novel candidates are DFT-validated as stable.

Abstract

MAX phases (M$_{n+1}$AX$_n$), precursors to MXenes, span a vast compositional space, motivating efficient computational screening for synthesisable candidates. We employ CrystaLLM$-\pi$, a large language model fine-tuned on 6,179 double transition-metal MAX phases, and demonstrate its ability to generate out-of-sample structures consistent with known experimental trends. Using a conditioning vector with two dimensions (a statistically derived MXene derivative count and a surrogate for A-site binding energy), the model was able to target MXene-favourable regions of phase space for generation. Specific condition vectors double novel stable structure generation rates versus unconditioned baselines. Of ten compositionally novel candidates, five exhibit DFT-validated stability ($E_{hull} < 0.050$ eV/atom). This work showcases the potential for autoregressive generative models to explore targeted materials' spaces, offering a scalable framework for accelerated discovery in compositionally complex systems.