Update description of GPax

Based on info from Sergei Kalinin

cc @ziatdinovmax

readme.md

@@ -212,7 +212,7 @@ Experimental orchestration software for autonomously controlling software-hardwa
   random exploration, and a multi-objective variant of [PHYSBO](https://github.com/issp-center-dev/PHYSBO).
 - [SLAMD](https://github.com/BAMresearch/WEBSLAMD) - A web app leveraging data-driven design for cementitious materials via a digital lab twin, complemented by a (materials-agnostic) AI optimization feature. Features UI to interactively explore  design spaces. The web app uses Python and Javascript.
 - [Summit](https://github.com/sustainable-processes/summit) is a set of tools for optimizing chemical processes with a wide variety of design of experiments (DoE) and adaptive design methods along with benchmarks.
-- [GPax](https://github.com/ziatdinovmax/gpax) is a Python package for physics-based Gaussian processes (GPs) built on top of NumPyro and JAX that take advantage of prior physical knowledge and different data modalities for active learning and Bayesian optimization.
+- [GPax](https://github.com/ziatdinovmax/gpax) is a Python package for physics-based Gaussian processes (GPs) built on top of NumPyro and JAX that take advantage of prior physical knowledge and different data modalities for active learning and Bayesian optimization. It supports "deep kernel learning", structured probabilistic mean functions, hypothesis learning workflows, multitask, multifidelity, heteroscedastic, and vector BO and emphasizes user friendliness.
 
 #### Proprietary
 - [ChemOS](https://chemos.io/)