AlloyLens: A Visual Analytics Tool for High-throughput Alloy Screening and Inverse Design

TL;DR

AlloyLens is an interactive visual analytics system that helps researchers explore high-dimensional alloy data, understand sensitivities, and make informed decisions in multi-objective alloy design.

Contribution

The paper introduces AlloyLens, a novel visual analytics tool that integrates multiple visualization and interaction techniques for high-throughput alloy screening and inverse design.

Findings

Reveals latent structures in alloy simulation data.

Highlights local impacts of compositional changes.

Supports tradeoff analysis in alloy design.

Abstract

Designing multi-functional alloys requires exploring high-dimensional composition-structure-property spaces, yet current tools are limited to low-dimensional projections and offer limited support for sensitivity or multi-objective tradeoff reasoning. We introduce AlloyLens, an interactive visual analytics system combining a coordinated scatterplot matrix (SPLOM), dynamic parameter sliders, gradient-based sensitivity curves, and nearest neighbor recommendations. This integrated approach reveals latent structure in simulation data, exposes the local impact of compositional changes, and highlights tradeoffs when exact matches are absent. We validate the system through case studies co-developed with domain experts spanning structural, thermal, and electrical alloy design.