DiffractGPT: Atomic Structure Determination from X-ray Diffraction Patterns using Generative Pre-trained Transformer

TL;DR

DiffractGPT is a transformer-based model that accurately predicts atomic crystal structures directly from X-ray diffraction patterns, significantly advancing automated materials discovery.

Contribution

This paper introduces DiffractGPT, a novel transformer model that predicts atomic structures from XRD patterns, integrating chemical information for improved accuracy.

Findings

Incorporating chemical info improves prediction accuracy

Model trained on thousands of structures from JARVIS-DFT

Fast and straightforward training process

Abstract

Crystal structure determination from powder diffraction patterns is a complex challenge in materials science, often requiring extensive expertise and computational resources. This study introduces DiffractGPT, a generative pre-trained transformer model designed to predict atomic structures directly from X-ray diffraction (XRD) patterns. By capturing the intricate relationships between diffraction patterns and crystal structures, DiffractGPT enables fast and accurate inverse design. Trained on thousands of atomic structures and their simulated XRD patterns from the JARVIS-DFT dataset, we evaluate the model across three scenarios: (1) without chemical information, (2) with a list of elements, and (3) with an explicit chemical formula. The results demonstrate that incorporating chemical information significantly enhances prediction accuracy. Additionally, the training process is straightforward and fast, bridging gaps between computational, data science, and experimental communities. This work represents a significant advancement in automating crystal structure determination, offering a robust tool for data-driven materials discovery and design.