Emojification (#189)

Gives our README a shiny touch by adding emojis

README.md

@@ -30,15 +30,15 @@ The Bayesian Back End (**BayBE**) provides a general-purpose toolbox for Bayesia
 of Experiments, focusing on additions that enable real-world experimental campaigns.
 
 Besides functionality to perform a typical recommend-measure loop, BayBE's highlights are:
-- Custom parameter encodings: Improve your campaign with domain knowledge
-- Built-in chemical encodings: Improve your campaign with chemical knowledge
-- Single and multiple targets with min, max and match objectives
-- Custom surrogate models: For specialized problems or active learning
-- Hybrid (mixed continuous and discrete) spaces
-- Transfer learning: Mix data from multiple campaigns and accelerate optimization
-- Comprehensive backtest, simulation and imputation utilities: Benchmark and find your best settings
-- Fully typed and hypothesis-tested: Robust code base
-- All objects are fully de-/serializable: Useful for storing results in databases or use in wrappers like APIs
+- ✨ Custom parameter encodings: Improve your campaign with domain knowledge
+- 🧪 Built-in chemical encodings: Improve your campaign with chemical knowledge
+- 🎯 Single and multiple targets with min, max and match objectives
+- ⚙️ Custom surrogate models: For specialized problems or active learning
+- 🎭 Hybrid (mixed continuous and discrete) spaces
+- 🚀 Transfer learning: Mix data from multiple campaigns and accelerate optimization
+- 📈 Comprehensive backtest, simulation and imputation utilities: Benchmark and find your best settings
+- 📝 Fully typed and hypothesis-tested: Robust code base
+- 🔄 All objects are fully de-/serializable: Useful for storing results in databases or use in wrappers like APIs
 
 
 ## Quick Start