arXiv subjects

Hongshuo Huang

Publications and source records attributed to Hongshuo Huang.

3 recordsLinked to original sources

arXiv2025

DREAMS: Density Functional Theory Based Research Engine for Agentic Materials Simulation

Large language model (LLM) agents can execute long-horizon scientific workflows, but their numerical outputs are difficult to trust: agents lose context, game verification checks, and can produce large volumes of plausible yet invalid results. We introduce the DFT-based Research Engine for Agentic Materials Simulation (DREAMS), a hierarchical multi-agent framework for density functional theory (DFT) built around a multi-tier safety guard. The guard applies deterministic checks wherever explicit criteria exist and scoped LLM judgment elsewhere, evaluating one parameter at a time and tracing every value to its registered source. Verification extends from tool-call time, where fabricated, laundered, or unsourced values are rejected before entering the workflow, to report time, where a judge audits the full provenance graph behind every claim; a shared canvas preserves information integrity across hundreds of steps. DREAMS achieves average errors below 1% on the Sol27LC lattice-constant benchmark, reproduces expert-level adsorption-energy differences on the CO/Pt(111) puzzle, and quantifies functional-driven uncertainty with Bayesian ensemble sampling, confirming the face-centered-cubic (FCC) site preference at the generalized gradient approximation (GGA) level. Compared with its unguarded counterpart, which reached a nearly correct answer while only 81% of its essential steps succeeded, the guarded system verifies every essential step at approximately 13 times the input tokens; verification layers can be disabled individually to balance trustworthiness against cost, and the tuned judge rules transfer across five judge models. DREAMS operates at an enhanced L2 (L2+) automation level and demonstrates capabilities approaching L3 automation, providing a path toward trustworthy, high-throughput autonomous materials simulation.

cs.AI↗

arXiv2024

AlloyBERT: Alloy Property Prediction with Large Language Models

The pursuit of novel alloys tailored to specific requirements poses significant challenges for researchers in the field. This underscores the importance of developing predictive techniques for essential physical properties of alloys based on their chemical composition and processing parameters. This study introduces AlloyBERT, a transformer encoder-based model designed to predict properties such as elastic modulus and yield strength of alloys using textual inputs. Leveraging the pre-trained RoBERTa encoder model as its foundation, AlloyBERT employs self-attention mechanisms to establish meaningful relationships between words, enabling it to interpret human-readable input and predict target alloy properties. By combining a tokenizer trained on our textual data and a RoBERTa encoder pre-trained and fine-tuned for this specific task, we achieved a mean squared error (MSE) of 0.00015 on the Multi Principal Elemental Alloys (MPEA) data set and 0.00611 on the Refractory Alloy Yield Strength (RAYS) dataset. This surpasses the performance of shallow models, which achieved a best-case MSE of 0.00025 and 0.0076 on the MPEA and RAYS datasets respectively. Our results highlight the potential of language models in material science and establish a foundational framework for text-based prediction of alloy properties that does not rely on complex underlying representations, calculations, or simulations.

cond-mat.mtrl-sci↗

arXiv2023

Materials Informatics Transformer: A Language Model for Interpretable Materials Properties Prediction

Recently, the remarkable capabilities of large language models (LLMs) have been illustrated across a variety of research domains such as natural language processing, computer vision, and molecular modeling. We extend this paradigm by utilizing LLMs for material property prediction by introducing our model Materials Informatics Transformer (MatInFormer). Specifically, we introduce a novel approach that involves learning the grammar of crystallography through the tokenization of pertinent space group information. We further illustrate the adaptability of MatInFormer by incorporating task-specific data pertaining to Metal-Organic Frameworks (MOFs). Through attention visualization, we uncover the key features that the model prioritizes during property prediction. The effectiveness of our proposed model is empirically validated across 14 distinct datasets, hereby underscoring its potential for high throughput screening through accurate material property prediction.

cs.LG↗