arXiv subjects

Yushan Xiao

Publications and source records attributed to Yushan Xiao.

3 recordsLinked to original sources

arXiv2026

Discovering physical mechanisms from experiment-simulation mismatches

Scientific discovery often begins where observation and prediction disagree. As computation and machine learning survey chemical space, experiment-simulation mismatches are exposed at scale, while tracing them to physical mechanisms remains expert-led. Here we present eXplainable DFT (XDFT), a self-evolving agent that turns this process into an executable search. XDFT formalizes candidate mechanisms as executable hypotheses, adjudicates their consequences against experiment and distils trajectories into priors for later searches. This couples a solving loop from mismatch to mechanism with a learning loop through which solving changes the solver. Across 112 source-audited cases in which standard calculations predict a metal whereas experiments find a semiconductor, XDFT resolved 105 with evidence-supported mechanisms within a single-GPU envelope. After 60 cases, the resolving mechanism ranked among the first three hypotheses for 80% of held-out cases, up from 7% under the initial expert prior. XDFT also returned evidence-graded mechanisms for seven expert-curated questions about physical mechanisms. These results establish experiment-simulation mismatches as tractable starting points for scientific agents that discover physical mechanisms while learning how to find the next.

cond-mat.mtrl-sci↗

arXiv2026

Autonomous Multi-objective Alloy Design through Simulation-guided Optimization

Alloy discovery is constrained by vast compositional spaces, competing objectives, and prohibitive experimental costs. Although simulations and machine learning have each accelerated parts of this process, unifying scientific knowledge, scalable search, and experimental confirmation into a data-efficient workflow remains challenging. Here, we present AutoMAT, a hierarchical autonomous framework spanning ideation to experimental validation. Integrating large language models, automated CALPHAD simulations, residual-learning-based correction, and AI-guided optimization, AutoMAT translates design targets into candidate alloys, refines compositions through closed-loop computational search, and validates results experimentally without hand-curated datasets. Targeting lightweight, high-strength alloys, AutoMAT identifies a titanium alloy 8.1% less dense and 13.0% stronger than the aerospace benchmark Ti-185, achieving the highest specific strength among benchmarked systems. In a second case, AutoMAT discovers a high-entropy alloy with 28.2% higher yield strength than the baseline while preserving high ductility. AutoMAT compresses alloy discovery from years to weeks, establishing a generalizable route toward autonomous materials design.

cond-mat.mtrl-sci↗

arXiv2025

MATAI: A Generalist Machine Learning Framework for Property Prediction and Inverse Design of Advanced Alloys

The discovery of advanced metallic alloys is hindered by vast composition spaces, competing property objectives, and real-world constraints on manufacturability. Here we introduce MATAI, a generalist machine learning framework for property prediction and inverse design of as-cast alloys. MATAI integrates a curated alloy database, deep neural network-based property predictors, a constraint-aware optimization engine, and an iterative AI-experiment feedback loop. The framework estimates key mechanical propertie, sincluding density, yield strength, ultimate tensile strength, and elongation, directly from composition, using multi-task learning and physics-informed inductive biases. Alloy design is framed as a constrained optimization problem and solved using a bi-level approach that combines local search with symbolic constraint programming. We demonstrate MATAI's capabilities on the Ti-based alloy system, a canonical class of lightweight structural materials, where it rapidly identifies candidates that simultaneously achieve lower density (<4.45 g/cm3), higher strength (>1000 MPa) and appreciable ductility (>5%) through only seven iterations. Experimental validation confirms that MATAI-designed alloys outperform commercial references such as TC4, highlighting the framework's potential to accelerate the discovery of lightweight, high-performance materials under real-world design constraints.

cond-mat.mtrl-sci↗