Harsha Satya Vardhan Vasamsetti supervised by Prof. Deva Priyakumar U received his Master of Science in Computer Science and Engineering (CSE) by Research. Here’s a summary of his research work on MatGPT: Transformer-Based Generation of Inorganic Materials with Targeted Properties

Functional materials with tailored properties are essential for advancements in crucial areas such as energy storage and semiconductor technologies. Despite progress in generative models, producing materials for specific target properties remains challenging because of their vast chemical space and complexities introduced by atomic structures and material properties.

In this work, an approach to the generative design of inorganic materials is introduced, employing a string-based invertible representation called the Simplified Line-Input Crystal Encoding System (SLICES). This representation adheres to crystallographic invariances, facilitating accurate modeling and reversible generation of crystal structures. A transformer decoder model with approximately 100 million parameters is integrated with a property-guided generation mechanism based on scaled property-token embeddings, following the continuous value encoding methodology. For the first time, special tokens corresponding to material properties (formation energy, band gap, bulk modulus, and crystal system) are introduced as control codes, and the embedding of each token is scaled by the desired property value, allowing fine-grained and continuous control over the generated structures. Unlike prior approaches such as MatterGPT, which concatenated property embeddings with SLICES tokens, or SLICES-PLUS, which required architectural modifications and an enhanced crystal representation for symmetry control, the present work achieves both property and crystal system conditioning within a single unified framework.

Trained on the Alex-20 dataset (280,033 structures) for formation energy and band gap, and on the Materials Project dataset (13,024 structures) for bulk modulus, the model consistently achieved validity exceeding 90% across single-property generation experiments, with competitive uniqueness and novelty. The framework was extended to dual-property conditioning, demonstrating simultaneous targeting of formation energy with bulk modulus and formation energy with band gap across broad grids of target combinations. Crystal system conditioning was further introduced as a new capability, achieving match rates of up to 93.44% for triclinic systems and 57.06% for cubic systems when co-conditioned with formation energy, and generalizing consistently across band gap and bulk modulus conditioning as well. A multi-step validation strategy incorporating structural and compositional plausibility checks, predictive property assessments via the MEGNet model followed by Density Functional Theory (DFT) calculations confirmed the physical realism of the generated structures. The results show that MatGPT generates valid, unique, and novel inorganic crystal structures while enabling controlled generation toward prescribed formation energy, band gap, bulk modulus, and crystal system targets. Representative DFT relaxations further indicate that the generated structures can retain their structural integrity after optimization, supporting
the physical plausibility of the proposed materials. These results establish scaled property-token conditioning as a simple and unified strategy for controllable inverse design of inorganic crystals.

In addition, an agentic workflow for materials science is presented, integrating the Qwen language model with domain-specific computational tools through a modular Model Context Protocol (MCP) architecture. The framework augments the language model with tools for structure relaxation, thermodynamic calculations, and retrieval-augmented generation, and is evaluated on the MaScQA benchmark for materials science question answering. Together, the generative MatGPT framework and the agentic verification system establish foundations for end-to-end AIdriven materials discovery pipelines.

August 2026