# Learning material synthesis-process-structure-property relationship by data fusion: Bayesian Coregionalization N-Dimensional Piecewise Function Learning

arXiv: https://arxiv.org/abs/2311.06228 · HTML: https://mlsanity.com/paper/2311.06228/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 45 references that are in the map.
- First posted on arXiv 10 Nov 2023.

## Builds on (most cited first, 9 of 9)

- [Pyro: Deep Universal Probabilistic Programming](https://mlsanity.com/paper/1810.09538/) (2018)
- [GPflow: A Gaussian process library using TensorFlow](https://mlsanity.com/paper/1610.08733/) (2016)
- [Automatic Construction and Natural-Language Description of Nonparametric Regression Models](https://mlsanity.com/paper/1402.4304/) (2014)
- [Autonomous synthesis of metastable materials](https://mlsanity.com/paper/2101.07385/) (2021)
- [Physics Informed Deep Kernel Learning](https://mlsanity.com/paper/2006.04976/) (2020)
- [Unsupervised Phase Mapping of X-ray Diffraction Data by Nonnegative Matrix Factorization Integrated with Custom Clustering](https://mlsanity.com/paper/1802.07307/) (2018)
- [Sequential Adaptive Design for Jump Regression Estimation](https://mlsanity.com/paper/1904.01648/) (2019)
- [A Fully Natural Gradient Scheme for Improving Inference of the Heterogeneous Multi-Output Gaussian Process Model](https://mlsanity.com/paper/1911.10225/) (2019)
- [Scalable Multi-Agent Lab Framework for Lab Optimization](https://mlsanity.com/paper/2208.09099/) (2022)

## Built on by (most cited first, 1 of 1)

- [Bayesian Co-navigation: Dynamic Designing of the Materials Digital Twins via Active Learning](https://mlsanity.com/paper/2404.12899/) (2024)

## Abstract (arXiv)

Autonomous materials research labs require the ability to combine and learn from diverse data streams. This is especially true for learning material synthesis-process-structure-property relationships, key to accelerating materials optimization and discovery as well as accelerating mechanistic understanding. We present the Synthesis-process-structure-property relAtionship coreGionalized lEarner (SAGE) algorithm. A fully Bayesian algorithm that uses multimodal coregionalization to merge knowledge across data sources to learn synthesis-process-structure-property relationships. SAGE outputs a probabilistic posterior for the relationships including the most likely relationships given the data.