- updated the README to point to the new tutorial

README.md

@@ -107,11 +107,15 @@ The library provides a growing number of methods specific to compositional (simp
 
    Critically, unlike the O(N^2) distance-based graph generation methods, this approach **scales linearly** with the resulting number of nodes. Because of that, it is extremely efficient even in high-dimensional spaces, where the number of edges goes into trillions and beyond. Nimplex can **both generate and find neighbors** for around **2M points per second in 9-dimensional space** on a modern CPU. 
 
-   As explored in the manuscript, such representations, even of different dimensions, can can then be used to efficeintly encode complex problem spaces where some prior assumptions and knowledge are available. In the Example #2 from manuscript, inspired by problem of joining titanium with stainless steel in [10.1016/j.addma.2022.102649](https://doi.org/10.1016/j.addma.2022.102649) using 3-component spaces, one encode 3 separate paths where some components are shared in predetermined fashion. This to efficiently encode the problem space in form of a structure graph (left panel below and then use it to construct a single **simplex graph complex** (right panel below) as a single consistent structure.
+   As explored in the manuscript, such representations, even of different dimensions, can can then be used to efficeintly encode complex problem spaces where some prior assumptions and knowledge are available. In the Example #2 from manuscript, inspired by problem of joining titanium with stainless steel in [10.1016/j.addma.2022.102649](https://doi.org/10.1016/j.addma.2022.102649) using 3-component spaces, one encode 3 separate paths where some components are shared in predetermined fashion. This to efficiently encode the problem space in form of a structure graph (left panel below) and then use it to construct a single **simplex graph complex** (right panel below) as a single consistent structure.
 
    <img src="https://raw.githubusercontent.com/amkrajewski/nimplex/dbcd9a6e1edd21aebec5e3ec964593ea1a13d23a/assets/Fig4.png" alt="Simplex Graph Complex" width="1000" style="display: block; margin-left: auto; margin-right: auto;"/>
 
-Several other methods are in testing and will likely be added in the future releases. If you have any suggestions, please open an issue on GitHub as we are always soliciting new ideas and use cases based on real-world problems in the 
+   With such graph representation, one can very easily deploy any scientific library for graph exploration, constrained and biased by models operating in the elemental space mapping `nimplex` provides. A neat and concise demonstration of this is provided in the [`02.AdditiveManufacturingPathPlanning.ipynb`](examples/02.AdditiveManufacturingPathPlanning.ipynb) under `examples` directory, where thermodynamic phase stability models constrain a 4-component (tetrahedral) design space existing in 7-component chemical space and property model related to yield strength (RMSAD) is used to bias designed paths towards objectives like property maximization or gradient minimization with extremely concise code simply modifying the weights on unidirectional edges in the graph. For instance, the figure below (approximately) depicts the shortest path through a subset of tetrahedron formed by solid solution phases, later stretched in space proportionally to RMDAS gradient magnitude.
+
+   <img src="https://raw.githubusercontent.com/amkrajewski/nimplex/main/assets/Fig5.png" alt="Gradient Magnitude Stretched Graph with Shortest Path" width="1000" style="display: block; margin-left: auto; margin-right: auto;"/>
+
+> Several other methods are in testing and will likely be added in the future releases. If you have any suggestions, please open an issue on GitHub as we are always soliciting new ideas and use cases based on real-world problems in the 
 scientific computing community.
 
 ## Usage in Nim