# Machine-learned metrics for predicting the likelihood of success in materials discovery

arXiv: https://arxiv.org/abs/1911.11201 · HTML: https://mlsanity.com/paper/1911.11201/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2019.

## Built on by (most cited first, 6 of 6)

- [Fast Bayesian Optimization of Needle-in-a-Haystack Problems using Zooming Memory-Based Initialization (ZoMBI)](https://mlsanity.com/paper/2208.13771/) (2022)
- [ET-AL: Entropy-Targeted Active Learning for Bias Mitigation in Materials Data](https://mlsanity.com/paper/2211.07881/) (2022)
- [Robust SleepNets](https://mlsanity.com/paper/2102.12555/) (2021)
- [ATRAS: Adversarially Trained Robust Architecture Search](https://mlsanity.com/paper/2106.06917/) (2021)
- [Artificial Intelligence in Material Engineering: A review on applications of AI in Material Engineering](https://mlsanity.com/paper/2209.11234/) (2022)
- [A Critical Examination of Active Learning Workflows in Materials Science](https://mlsanity.com/paper/2601.05946/) (2026)

## Abstract (arXiv)

Materials discovery is often compared to the challenge of finding a needle in a haystack. While much work has focused on accurately predicting the properties of candidate materials with machine learning (ML), which amounts to evaluating whether a given candidate is a piece of straw or a needle, less attention has been paid to a critical question: Are we searching in the right haystack? We refer to the haystack as the design space for a particular materials discovery problem (i.e. the set of possible candidate materials to synthesize), and thus frame this question as one of design space selection. In this paper, we introduce two metrics, the Predicted Fraction of Improved Candidates (PFIC), and the Cumulative Maximum Likelihood of Improvement (CMLI), which we demonstrate can identify discovery-rich and discovery-poor design spaces, respectively. Using CMLI and PFIC together to identify optimal design spaces can significantly accelerate ML-driven materials discovery.