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pyiron-workshop · Sep 14, 2023 · 207e37c · 207e37c
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diff --git a/_config.yml b/_config.yml
@@ -1,9 +1,9 @@
 # Book settings
 # Learn more at https://jupyterbook.org/customize/config.html
 
-title: "Workshop: From Electrons to Phase Diagrams"
+title: "Workshop: Data analysis and workflows in Materials science"
 #author: The Jupyter Book Community
-logo: potentials_logo.png
+#logo: potentials_logo.png
 
 # Force re-execution of notebooks on each build.
 # See https://jupyterbook.org/content/execute.html
@@ -19,7 +19,7 @@ latex:
 
 # Information about where the book exists on the web
 repository:
-  url: https://github.com/pyiron/potentials-workshop-2022
+  url: https://github.com/pyiron/DGM_workshop
   path_to_book: book
   branch: main
 

diff --git a/_toc.yml b/_toc.yml
@@ -4,21 +4,6 @@
 format: jb-book
 root: intro
 chapters:
-  - file: introduction/Intro.md
-    sections:
-    - file: introduction/01_Introduction_Pyiron.ipynb
-    - file: introduction/02_Visualizing_Training_Data.ipynb
-    - file: introduction/03_Creating_Training_Data.ipynb
-  - file: potentials/Intro.md
-    sections:
-    - file: potentials/01-EAM/IntroductionPotentialFitting.ipynb
-    - file: potentials/01-EAM/HandsOnPotenitalFitting.ipynb
-    - file: potentials/02-HDNNP/handson.ipynb
-    - file: potentials/03-ACE/pacemaker_example.ipynb
-    - file: validation/validation_LiAl.ipynb
-  - file: phase_diagram/Intro.md
-    sections:
-    - file: phase_diagram/tutorial_1.ipynb
-    - file: phase_diagram/tutorial_2.ipynb
-    - file: phase_diagram/exercise_1.ipynb
-    - file: phase_diagram/exercise_2.ipynb
+  - file: dgm_workshop/01_intro_to_python.ipynb
+  - file: dgm_workshop/02_workflows_in_materials_science.ipynb
+  - file: dgm_workshop/03_customising_your_workflow.ipynb
diff --git a/dgm_workshop/01_intro_to_python.ipynb b/dgm_workshop/01_intro_to_python.ipynb
@@ -1,28 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "43e2cffc-0c95-4958-a243-86942b40da3b",
-   "metadata": {},
-   "source": [
-    "<table border=\"0\">\n",
-    "<tr>\n",
-    "  <td>\n",
-    "    <font style=\"font-family:roboto;color:#455e6c;font-size:30px\"> <b> Data analysis and workflows in Materials science </b> </font> </td>\n",
-    "  <td>\n",
-    "    <img src=\"logo.png\" width=\"40%\" align=\"right\"></td>\n",
-    "</tr>\n",
-    "<tr></tr>\n",
-    "<tr>\n",
-    "  <td> \n",
-    "    <font style=\"font-family:roboto;color:#455e6c;font-size:18px\"> Sarath Menon </font> <br> \n",
-    "    <font style=\"font-family:roboto;color:#455e6c;font-size:12px\"> Max-Planck-Institut für Eisenforschung GmbH <br></font>\n",
-    "    <font style=\"font-family:roboto;color:#455e6c;font-size:12px\"> DGM-Nachwuchsforum | 25.04.2023 </font>\n",
-    "  </td>\n",
-    "</tr>\n",
-    "</table>"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "150c4306-adba-4a06-ac10-ea5b0b13047a",
@@ -1143,7 +1120,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,

diff --git a/dgm_workshop/02_workflows_in_materials_science.ipynb b/dgm_workshop/02_workflows_in_materials_science.ipynb
@@ -1,28 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "bf2ee604-36c7-4406-86ce-82f07eff053a",
-   "metadata": {},
-   "source": [
-    "<table border=\"0\">\n",
-    "<tr>\n",
-    "  <td>\n",
-    "    <font style=\"font-family:roboto;color:#455e6c;font-size:30px\"> <b> Data analysis and workflows in Materials science </b> </font> </td>\n",
-    "  <td>\n",
-    "    <img src=\"logo.png\" width=\"40%\" align=\"right\"></td>\n",
-    "</tr>\n",
-    "<tr></tr>\n",
-    "<tr>\n",
-    "  <td> \n",
-    "    <font style=\"font-family:roboto;color:#455e6c;font-size:18px\"> Sarath Menon </font> <br> \n",
-    "    <font style=\"font-family:roboto;color:#455e6c;font-size:12px\"> Max-Planck-Institut für Eisenforschung GmbH <br></font>\n",
-    "    <font style=\"font-family:roboto;color:#455e6c;font-size:12px\"> DGM-Nachwuchsforum | 25.04.2023 </font>\n",
-    "  </td>\n",
-    "</tr>\n",
-    "</table>"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "42864dbd-93b0-4890-b791-033cda035c1f",
@@ -529,7 +506,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,

diff --git a/dgm_workshop/03_customising_your_workflow.ipynb b/dgm_workshop/03_customising_your_workflow.ipynb
@@ -1,28 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "18185071-d4ab-4a29-8808-9a4dc3765cb8",
-   "metadata": {},
-   "source": [
-    "<table border=\"0\">\n",
-    "<tr>\n",
-    "  <td>\n",
-    "    <font style=\"font-family:roboto;color:#455e6c;font-size:30px\"> <b> Data analysis and workflows in Materials science </b> </font> </td>\n",
-    "  <td>\n",
-    "    <img src=\"logo.png\" width=\"40%\" align=\"right\"></td>\n",
-    "</tr>\n",
-    "<tr></tr>\n",
-    "<tr>\n",
-    "  <td> \n",
-    "    <font style=\"font-family:roboto;color:#455e6c;font-size:18px\"> Sarath Menon </font> <br> \n",
-    "    <font style=\"font-family:roboto;color:#455e6c;font-size:12px\"> Max-Planck-Institut für Eisenforschung GmbH <br></font>\n",
-    "    <font style=\"font-family:roboto;color:#455e6c;font-size:12px\"> DGM-Nachwuchsforum | 25.04.2023 </font>\n",
-    "  </td>\n",
-    "</tr>\n",
-    "</table>"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "05cbaad4-3eb7-49fb-b16a-a4a57b080900",
@@ -682,7 +659,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.11.5"
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diff --git a/intro.md b/intro.md
@@ -1,13 +1,11 @@
-# <font style="color:#B71C1C" face="Helvetica" > From Electrons to Phase Diagrams 2022 </font>
+# <font style="font-family:roboto;color:#455e6c"> Data analysis and workflows in Materials science </font>  
 
-Phase diagrams are of ubiquitous importance for materials design. Current materials design workflows in industry and academia employ CALPHAD-computed phase diagrams that to a large extent rely on assessed experimental data.
+S. Menon  
+Max-Planck-Institut für Eisenforschung GmbH  
 
-Today the computation of large numbers of DFT data are becoming a routine task, due to efficient DFT codes, efficient workflow management and powerful high-performance computing. Together with progress in interatomic potentials, in particular the development of machine learning potentials as well as efficient implementations and parameterization codes, this means that interatomic potentials with near-DFT accuracy are now available. When combined with efficient sampling for the computation of free energies, it is therefore possible to estimate phase diagrams directly from DFT data and to supplement and assess experimental input.
-
-At the three-day workshop we will provide tutorials and hands-on classes that cover the complete chain from high-throughput electronic structure calculations to the computation of phase diagrams. Day 1 will focus on automated workflows for the generation of DFT data. On day 2 we will discuss the parameterization and validation of interatomic potentials from DFT reference data. Day 3 will then introduce the methods and tools for the computation of thermodynamic properties and phase diagrams.
-
-<iframe width="560" height="315" src="https://www.youtube.com/embed/8NP3DpUkg6U" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
+Presented as part of [DGM-Nachwuchsforum 2023](https://dgm.de/de/netzwerk/nachwuchs/veranstaltungen/dgm-nachwuchsforum-2023) on 25.04.2023  
 
+A paradigm shift in the field of materials science towards data-driven approaches and digitalisation goes hand in hand with the generation of vast amounts of experimental and simulation data. The analysis and effective use of this data is critical to enhancing our understanding of materials and accelerating materials research. Python has emerged as a programming language of choice for this task in materials science due to its flexibility and ease of use. The tutorial will start with an introduction to python through jupyter notebooks. Furthermore, the participant will gain insight into performing typical simulations in materials science, followed by tools and methods for efficient post-processing and analysis of data. We employ pyiron, an integrated development environment for computational materials science, as a representative software in the tutorial. Overall, it will provide early career researchers tools to streamline their scientific workflows and manage data efficiently.
 
 ```{tableofcontents}
 ```