# Probabilistic design of a molybdenum-base alloy using a neural network

arXiv: https://arxiv.org/abs/1803.00879 · HTML: https://mlsanity.com/paper/1803.00879/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Mar 2018.

## Built on by (most cited first, 2 of 2)

- [Unveil the unseen: Exploit information hidden in noise](https://mlsanity.com/paper/2209.08376/) (2022)
- [Probabilistic selection and design of concrete using machine learning](https://mlsanity.com/paper/2304.11226/) (2023)

## Abstract (arXiv)

An artificial intelligence tool is exploited to discover and characterize a new molybdenum-base alloy that is the most likely to simultaneously satisfy targets of cost, phase stability, precipitate content, yield stress, and hardness. Experimental testing demonstrates that the proposed alloy fulfils the computational predictions, and furthermore the physical properties exceed those of other commercially available Mo-base alloys for forging-die applications.