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-### Title 
-Test
+# Geometric Machine Learning in Quantum Chemistry 
+
+_Quantum Chemistry_ allows predicting the properties of molecular systems with great precision by invoking and "solving" the laws of quantum mechanics. Unfortunately, it is held by the computational effort which scales poorly with the size of the system under study. Machine Learning is about to revolutionize the field of electronic structure theory by bringing down the computational effort. Principled machine learning approaches respect the roto-translational symmetries of the problem. This is the dominion of _geometric machine learning_. 
+
+The aim of the lecture is to enable you to approach and understand the latest literature in this burgeoning field. It broadly divided into three parts focusing on elements of quantum chemistry, elements of geometric machine learning, and their synthesis. 
+
+## Style 
+The lecture is a slow-paced blackboard lecture. There are no lecture notes and you are expected to take your own notes (not just take a picture at the end of class). In the final part of the term, you will read some of the latest works in the field, and we will discuss those in an inverted classroom setting. 
+
+Exercises are mostly computational (running experiments with pySCF and other packages, plus some python coding) and partly pen-and-paper. 
+
+## Contents 
+### Part 1: Quantum Chemistry 
+* Introduction, Born-Oppenheimer approximation
+* Multi electron wave function, Hartree product, Slater determinants
+* Hartree-Fock approximation
+* Basis functions and numerics
+* Density functional theory
+* Exchange and correlation holes and functionals
+### Part 2: Geometric Machine Learning
+* Graph convolutional neural networks, attention, message passing neural networks
+* Irreducible representations of SO(3) and spherical harmonics
+* Tensor field equivariant neural networks
+### Part 3: Synthesis 
+* KineticNet (Inverted classroom) 
+* M-OFDFT (Inverted classroom) 
+
+### Prerequisites
+One lecture in quantum mechanics, basic notions of machine learning. 
+
+### Formalities
+
+Time and Place: To allow students to reach Neuenheimer Feld in time for the next lecture, we start at 9h00 (not 9h15). The lecture is on Thursdays in the "Kleine Hörsaal" (second floor) of Philosophenweg 12. 
+
+The lecture accounts for 6CP and can be elected as part of the Computational Physics Specialization. 
+
+Registration is not required. Simply come to the first lecture on April 18th.