Update outline.md

Workshop4/outline.md

-Force fields in molecular modelling
+##Force fields in molecular modelling
 
-- Interatomic force fields
-- nice examples of importance
+### Introduction
+- Outline to simulations and why we need them 
+    - Different methods and timescale  
+    - Pointing out DFT and Classical/Coarse Grain
+- Bridging the gap using Machine Learning Interatomic Potentials (MLIPs)
+- Examples of these in the past 
 
--mace notebook
-- intro mace
-Extra MP faster than NN. 
+### Introduction to MACE 
+- What is MACE?
+    - MACE = Message-passing Atomic Cluster Expansion  
+    - The code that takes in information such as atomic position, energies, forces, stress, etc. and genereates a potential based on that 
+- To understand what MACE actually does, we must first visit ACE.
+    - A condensed theoretical explanation of ACE then how MACE builds on it via NN 
 
-- Importance of hyperparameters
+### Trying out MACE 
+- Introduce MACE Jupyter notebook
+- Going through training parameters and dataset 
+- Running it once with the default settings and compare with a GAP (Gaussian Approximation Potential ) that was trained on the same data 
+- Change some hyper parameters and training again
+    - Importance of hyperparameters
+    - Why changing some won't make a big differences
+    - Channels: Does more always more more  
+- To validate:
+    - Plot against reference parameters (xTB = Semiempirical Tight Binding)
 
-- Training
-- Plot against reference parameters
 
-- Change hyperparameters
-- understand differences
-
-
--Turn into a MD model
-- Intro into MD
-Run ase MD engine
+### Using the MACE Model 
+- Small introduction into MD (Molecular Dynamics)
+    - Basic theory, ie Newtons 2nd Law 
+    - Most importanly: Time dependancy and linking back to timescale from introduction 
+- Mention we turn it into a LAMMPS potential file to use normall but we can use ASE engine 
+- CAn calculate properties such as RDF  
+- Can compare energies with xTB calculations