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

TL;DR

MATAI is a comprehensive machine learning framework that accelerates the discovery and design of advanced alloys by predicting properties and optimizing compositions under real-world constraints, demonstrated on Ti-based alloys.

Contribution

The paper introduces MATAI, a novel generalist ML framework combining property prediction, constrained optimization, and iterative feedback for alloy design, with experimental validation.

Findings

MATAI accurately predicts alloy properties from composition.

It rapidly identifies alloy candidates with superior properties.

Experimental results confirm the designed alloys outperform commercial references.

Abstract

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.