# A General Approach for Determining Applicability Domain of Machine Learning Models

arXiv: https://arxiv.org/abs/2406.05143 · HTML: https://mlsanity.com/paper/2406.05143/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 52 references that are in the map.
- First posted on arXiv 28 May 2024.

## Builds on (most cited first, 11 of 11)

- [A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges](https://mlsanity.com/paper/2011.06225/) (2020)
- [Generalized Out-of-Distribution Detection: A Survey](https://mlsanity.com/paper/2110.11334/) (2021)
- [Parametric UMAP embeddings for representation and semi-supervised learning](https://mlsanity.com/paper/2009.12981/) (2020)
- [Extracting Accurate Materials Data from Research Papers with Conversational Language Models and Prompt Engineering](https://mlsanity.com/paper/2303.05352/) (2023)
- [Deeply Uncertain: Comparing Methods of Uncertainty Quantification in Deep Learning Algorithms](https://mlsanity.com/paper/2004.10710/) (2020)
- [Machine learning modeling of superconducting critical temperature](https://mlsanity.com/paper/1709.02727/) (2017)
- [Evading the curse of dimensionality in nonparametric density estimation with simplified vine copulas](https://mlsanity.com/paper/1503.03305/) (2015)
- [High-Dimensional Materials and Process Optimization using Data-driven Experimental Design with Well-Calibrated Uncertainty Estimates](https://mlsanity.com/paper/1704.07423/) (2017)
- [ADAPT : Awesome Domain Adaptation Python Toolbox](https://mlsanity.com/paper/2107.03049/) (2021)
- [Clarifying Trust of Materials Property Predictions using Neural Networks with Distribution-Specific Uncertainty Quantification](https://mlsanity.com/paper/2302.02595/) (2023)
- [Outlier-Based Domain of Applicability Identification for Materials Property Prediction Models](https://mlsanity.com/paper/2302.06454/) (2023)

## Built on by (most cited first, 5 of 5)

- [Known Unknowns: Out-of-Distribution Property Prediction in Materials and Molecules](https://mlsanity.com/paper/2502.05970/) (2025)
- [A Critical Examination of Active Learning Workflows in Materials Science](https://mlsanity.com/paper/2601.05946/) (2026)
- [Inferential Mechanics Part 1: Causal Mechanistic Theories of Machine Learning in Chemical Biology with Implications](https://mlsanity.com/paper/2602.23303/) (2026)
- [Uncertainty-aware reinforcement learning for chemical language models](https://mlsanity.com/paper/2606.24990/) (2026)
- [Physics-residual machine learning predicts oxygen-evolution catalyst activity beyond the training range from sparse polarization measurements](https://mlsanity.com/paper/2609.23549/) (2026)

## Abstract (arXiv)

Knowledge of the domain of applicability of a machine learning model is essential to ensuring accurate and reliable model predictions. In this work, we develop a new and general approach of assessing model domain and demonstrate that our approach provides accurate and meaningful domain designation across multiple model types and material property data sets. Our approach assesses the distance between data in feature space using kernel density estimation, where this distance provides an effective tool for domain determination. We show that chemical groups considered unrelated based on chemical knowledge exhibit significant dissimilarities by our measure. We also show that high measures of dissimilarity are associated with poor model performance (i.e., high residual magnitudes) and poor estimates of model uncertainty (i.e., unreliable uncertainty estimation). Automated tools are provided to enable researchers to establish acceptable dissimilarity thresholds to identify whether new predictions of their own machine learning models are in-domain versus out-of-domain.