ML/MM with GROMACS and Metatomic

Author: PicoCentauri

docs/src/software/gromacs.sec

@@ -15,3 +15,4 @@ comprehensive suite for MD workflows. Its open-source nature and active communit
 continuous development and support.
 
 - examples/water-pulsed/water-pulsed
+- examples/ml-mm/ml-mm

docs/src/topics/index.rst

@@ -0,0 +1,92 @@
+Recipes grouped by topic
+========================
+
+You can navigate through the various recipes grouped
+in thematic areas, including classes of simulation problems
+and of modeling techniques. Recipes may be listed in 
+more than one area, when relevant.
+
+.. toctree::
+    :maxdepth: 1
+    :hidden:
+
+    sampling
+    analysis
+    ml-models
+    universal
+    nqes
+
+
+:doc:`sampling`
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. card-carousel:: 3
+
+
+        .. card:: Atomistic Water Model for Molecular Dynamics
+            :link: ../examples/water-model/water-model
+            :link-type: doc
+            :text-align: center
+            :shadow: md
+
+            .. image:: ../examples/water-model/images/thumb/sphx_glr_water-model_thumb.png
+                :alt: In this example, we demonstrate how to construct a metatensor atomistic model for flexible three and four-point water model, with parameters optimized for use together with quantum-nuclear-effects-aware path integral simulations (cf. Habershon et al., JCP (2009)). The model also demonstrates the use of torch-pme, a Torch library for long-range interactions, and uses the resulting model to perform demonstrative molecular dynamics simulations.
+                :class: gallery-img
+
+                
+:doc:`analysis`
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. card-carousel:: 3
+
+
+        .. card:: Water orientation in a pulsed electric field
+            :link: ../examples/water-pulsed/water-pulsed
+            :link-type: doc
+            :text-align: center
+            :shadow: md
+
+            .. image:: ../examples/water-pulsed/images/thumb/sphx_glr_water-pulsed_thumb.png
+                :alt: Energy dissipation in water is very fast and more efficient than in many other liquids. This behavior is commonly attributed to the intermolecular interactions associated with hydrogen bonding. This effect has been studied intensively by experiments, ab initio, and classical simulations in the work by Elgabarty et al.. Here, we will re run some of the classical force field molecular dynamics (MD) simulations of the paper using the GROMACS package to compute the timeseries of the dipole moments as well as the energy.
+                :class: gallery-img
+
+                
+:doc:`ml-models`
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. card-carousel:: 3
+
+
+        .. card:: ML–MM Simulations with GROMACS and Metatomic
+            :link: ../examples/ml-mm/ml-mm
+            :link-type: doc
+            :text-align: center
+            :shadow: md
+
+            .. image:: ../examples/ml-mm/images/thumb/sphx_glr_ml-mm_thumb.png
+                :alt: In this tutorial we will simulate alanine dipeptide in water using a machine learning potential for the solute, while the solvent is treated with a classical force field. This setup is commonly referred to as an ML/MM simulation and follows very similar ideas to QM/MM.
+                :class: gallery-img
+
+                
+:doc:`universal`
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. card-carousel:: 3
+
+
+        .. card:: ML–MM Simulations with GROMACS and Metatomic
+            :link: ../examples/ml-mm/ml-mm
+            :link-type: doc
+            :text-align: center
+            :shadow: md
+
+            .. image:: ../examples/ml-mm/images/thumb/sphx_glr_ml-mm_thumb.png
+                :alt: In this tutorial we will simulate alanine dipeptide in water using a machine learning potential for the solute, while the solvent is treated with a classical force field. This setup is commonly referred to as an ML/MM simulation and follows very similar ideas to QM/MM.
+                :class: gallery-img
+
+                
+:doc:`nqes`
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. card-carousel:: 3
+

docs/src/topics/ml-models.sec

@@ -18,3 +18,4 @@ data.
 - examples/flashmd/flashmd-demo
 - examples/shiftml/shiftml-example
 - examples/hamiltonian-qm7/hamiltonian-qm7
+- examples/ml-mm/ml-mm

docs/src/topics/universal.sec

@@ -11,3 +11,4 @@ structural space.
 - examples/pet-mad-uq/pet-mad-uq
 - examples/flashmd/flashmd-demo
 - examples/eon-pet-neb/eon-pet-neb
+- examples/ml-mm/ml-mm

examples/ml-mm/.gitignore

@@ -0,0 +1,10 @@
+\#*
+*edr
+*.tpr
+*.cpt
+*.trr
+*.pt
+*.lock
+*.npz
+mdout.mdp
+confout.gro

examples/ml-mm/README.rst

@@ -0,0 +1,5 @@
+ML-MM Simulations with GROMACS and Metatomic
+============================================
+
+Simulate alanine dipeptide in water using a machine learning potential for the
+solute (ML-MM simulation) with GROMACS and the Metatomic interface.