# Design of a nickel-base superalloy using a neural network

arXiv: https://arxiv.org/abs/1803.03039 · HTML: https://mlsanity.com/paper/1803.03039/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2018.

## Built on by (most cited first, 3 of 3)

- [Unveil the unseen: Exploit information hidden in noise](https://mlsanity.com/paper/2209.08376/) (2022)
- [Talk Freely, Execute Strictly: Schema-Gated Agentic AI for Flexible and Reproducible Scientific Workflows](https://mlsanity.com/paper/2603.06394/) (2026)
- [Probabilistic selection and design of concrete using machine learning](https://mlsanity.com/paper/2304.11226/) (2023)

## Abstract (arXiv)

A new computational tool has been developed to model, discover, and optimize new alloys that simultaneously satisfy up to eleven physical criteria. An artificial neural network is trained from pre-existing materials data that enables the prediction of individual material properties both as a function of composition and heat treatment routine, which allows it to optimize the material properties to search for the material with properties most likely to exceed a target criteria. We design a new polycrystalline nickel-base superalloy with the optimal combination of cost, density, gamma' phase content and solvus, phase stability, fatigue life, yield stress, ultimate tensile strength, stress rupture, oxidation resistance, and tensile elongation. Experimental data demonstrates that the proposed alloy fulfills the computational predictions, possessing multiple physical properties, particularly oxidation resistance and yield stress, that exceed existing commercially available alloys.