# Materials Representation and Transfer Learning for Multi-Property Prediction

arXiv: https://arxiv.org/abs/2106.02225 · HTML: https://mlsanity.com/paper/2106.02225/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 71 references that are in the map.
- First posted on arXiv 4 Jun 2021.

## Builds on (most cited first, 18 of 18)

- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model](https://mlsanity.com/paper/1911.08265/) (2019)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Molecular Graph Convolutions: Moving Beyond Fingerprints](https://mlsanity.com/paper/1603.00856/) (2016)
- [D-VAE: A Variational Autoencoder for Directed Acyclic Graphs](https://mlsanity.com/paper/1904.11088/) (2019)
- [OrbNet: Deep Learning for Quantum Chemistry Using Symmetry-Adapted Atomic-Orbital Features](https://mlsanity.com/paper/2007.08026/) (2020)
- [Predicting materials properties without crystal structure: Deep representation learning from stoichiometry](https://mlsanity.com/paper/1910.00617/) (2019)
- [Large-scale Multi-label Learning with Missing Labels](https://mlsanity.com/paper/1307.5101/) (2013)
- [Disentangled Variational Autoencoder based Multi-Label Classification with Covariance-Aware Multivariate Probit Model](https://mlsanity.com/paper/2007.06126/) (2020)
- [Neural Message Passing for Multi-Label Classification](https://mlsanity.com/paper/1904.08049/) (2019)
- [Out-of-Distribution Detection in Multi-Label Datasets using Latent Space of β-VAE](https://mlsanity.com/paper/2003.08740/) (2020)
- [Graph Dynamical Networks for Unsupervised Learning of Atomic Scale Dynamics in Materials](https://mlsanity.com/paper/1902.06836/) (2019)
- [Deep Hurdle Networks for Zero-Inflated Multi-Target Regression: Application to Multiple Species Abundance Estimation](https://mlsanity.com/paper/2010.16040/) (2020)
- [HOT-VAE: Learning High-Order Label Correlation for Multi-Label Classification via Attention-Based Variational Autoencoders](https://mlsanity.com/paper/2103.06375/) (2021)
- [End-to-End Learning for the Deep Multivariate Probit Model](https://mlsanity.com/paper/1803.08591/) (2018)
- [Hierarchical Visualization of Materials Space with Graph Convolutional Neural Networks](https://mlsanity.com/paper/1807.03404/) (2018)
- [Predicting Elastic Properties of Materials from Electronic Charge Density Using 3D Deep Convolutional Neural Networks](https://mlsanity.com/paper/2003.13425/) (2020)
- [On-the-fly Closed-loop Autonomous Materials Discovery via Bayesian Active Learning](https://mlsanity.com/paper/2006.06141/) (2020)

## Built on by (most cited first, 7 of 7)

- [Automating Crystal-Structure Phase Mapping: Combining Deep Learning with Constraint Reasoning](https://mlsanity.com/paper/2108.09523/) (2021)
- [Formula graph self-attention network for representation-domain independent materials discovery](https://mlsanity.com/paper/2201.05649/) (2022)
- [Curvature-informed multi-task learning for graph networks](https://mlsanity.com/paper/2208.01684/) (2022)
- [Artificial Intelligence in Material Engineering: A review on applications of AI in Material Engineering](https://mlsanity.com/paper/2209.11234/) (2022)
- [A New Workflow for Materials Discovery Bridging the Gap Between Experimental Databases and Graph Neural Networks](https://mlsanity.com/paper/2602.00756/) (2026)
- [Transfer Learning for Deep Learning-based Prediction of Lattice Thermal Conductivity](https://mlsanity.com/paper/2411.18259/) (2024)
- [Emerging Microelectronic Materials by Design: Navigating Combinatorial Design Space with Scarce and Dispersed Data](https://mlsanity.com/paper/2412.17283/) (2024)

## Abstract (arXiv)

The adoption of machine learning in materials science has rapidly transformed materials property prediction. Hurdles limiting full capitalization of recent advancements in machine learning include the limited development of methods to learn the underlying interactions of multiple elements, as well as the relationships among multiple properties, to facilitate property prediction in new composition spaces. To address these issues, we introduce the Hierarchical Correlation Learning for Multi-property Prediction (H-CLMP) framework that seamlessly integrates (i) prediction using only a material's composition, (ii) learning and exploitation of correlations among target properties in multi-target regression, and (iii) leveraging training data from tangential domains via generative transfer learning. The model is demonstrated for prediction of spectral optical absorption of complex metal oxides spanning 69 3-cation metal oxide composition spaces. H-CLMP accurately predicts non-linear composition-property relationships in composition spaces for which no training data is available, which broadens the purview of machine learning to the discovery of materials with exceptional properties. This achievement results from the principled integration of latent embedding learning, property correlation learning, generative transfer learning, and attention models. The best performance is obtained using H-CLMP with Transfer learning (H-CLMP(T)) wherein a generative adversarial network is trained on computational density of states data and deployed in the target domain to augment prediction of optical absorption from composition. H-CLMP(T) aggregates multiple knowledge sources with a framework that is well-suited for multi-target regression across the physical sciences.