“An emerging concept termed Organoid Intelligence (OI) ...

“An emerging concept termed Organoid Intelligence (OI) combines organoids with artificial intelligence systems to generate learning and memory, with the goals of modeling cognition and enabling biological computing applications.”
https://onlinelibrary.wiley.com/doi/10.1002/adhm.202302745

The U.S. National Science Foundation has invested $14 million in seven interdisciplinary research projects through the Emerging Frontiers in Research and Innovation (EFRI): Biocomputing through EnGINeering Organoid Intelligence program.
https://new.nsf.gov/news/nsf-invests-14m-bioengineered-systems-ethical-biocomputing

1) Integrated Human Brain Organoid Systems for Adaptive Reservoir Computing, University of Michigan
https://nsf.gov/awardsearch/showAward?AWD_ID=2422149

2) Implantation of Dense Associative Memory through CArdiac muscle cell-based Reprogrammable Bio-Oscillatory Neural Networks, University of Notre Dame
https://nsf.gov/awardsearch/showAward?AWD_ID=2422333

3) Reservoir Computer - Intelligent and Evolving Mechano-Reservoir Computing with Living Spheroids on Fibers, Virginia Tech
https://nsf.gov/awardsearch/showAward?AWD_ID=2422340

4) Neuron-Soft Organoid-Computer Interfaces for Long-Term Three-Dimensional Neural Network Computing, Harvard University
https://nsf.gov/awardsearch/showAward?AWD_ID=2422348

5) Spatiotemporal Learning in 3D Neuronal Organoids, University of Maryland
https://nsf.gov/awardsearch/showAward?AWD_ID=2422352

6) Feasibility of 3D Biological Neurocomputers for Intelligent Biomedical Motor Control Systems, University of California, Irvine
https://nsf.gov/awardsearch/showAward?AWD_ID=2422412

7) Teaching non-brain organoids how to think: Programmable organoid intelligence using neuronal networks implemented by gene circuits, Massachusetts Institute of Technology
https://nsf.gov/awardsearch/showAward?AWD_ID=2422282

Graph Networks for Materials Exploration (GNoME) is a deep learning tool developed by Google DeepMind to predict and discover new materials. GNoME uses graph neural networks (GNNs) to predict the stability of new materials.
https://github.com/google-deepmind/materials_discovery

AI tool GNoME finds 2.2 million new crystals, including 380,000 stable materials that could power future technologies
https://deepmind.google/discover/blog/millions-of-new-materials-discovered-with-deep-learning/

Scaling deep learning for materials discovery
https://nature.com/articles/s41586-023-06735-9.pdf

DenseGNN: universal and scalable deeper graph neural networks for high-performance property prediction in crystals and molecules
https://nature.com/articles/s4152