# Sparse mixed linear modeling with anchor-based guidance for high-entropy alloy discovery

arXiv: https://arxiv.org/abs/2504.20354 · HTML: https://mlsanity.com/paper/2504.20354/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 29 references that are in the map.
- First posted on arXiv 29 Apr 2025.

## Builds on (most cited first, 5 of 5)

- [Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro](https://mlsanity.com/paper/1912.11554/) (2019)
- [Interpretable models for extrapolation in scientific machine learning](https://mlsanity.com/paper/2212.10283/) (2022)
- [Parsimonious Mixed Models](https://mlsanity.com/paper/1506.04967/) (2015)
- [Exhaustive search for sparse variable selection in linear regression](https://mlsanity.com/paper/1707.02050/) (2017)
- [High Entropy Alloy property predictions using Transformer-based language model](https://mlsanity.com/paper/2411.04861/) (2024)

## Abstract (arXiv)

High-entropy alloys have attracted attention for their exceptional mechanical properties and thermal stability. However, the combinatorial explosion in the number of possible elemental compositions renders traditional trial-and-error experimental approaches highly inefficient for materials discovery. To solve this problem, machine learning techniques have been increasingly employed for property prediction and high-throughput screening. Nevertheless, highly accurate nonlinear models often suffer from a lack of interpretability, which is a major limitation. In this study, we focus on local data structures that emerge from the greedy search behavior inherent to experimental data acquisition. By introducing a linear and low-dimensional mixture regression model, we strike a balance between predictive performance and model interpretability. In addition, we develop an algorithm that simultaneously performs prediction and feature selection by considering multiple candidate descriptors. Through a case study on high-entropy alloys, this study introduces a method that combines anchor-guided clustering and sparse linear modeling to address biased data structures arising from greedy exploration in materials science.