diff --git a/slides/siam-may24.md b/slides/siam-may24.md index 11935cfc..215b917b 100644 --- a/slides/siam-may24.md +++ b/slides/siam-may24.md @@ -9,6 +9,8 @@ class: title, middle ## Crystal-GFN ### Sampling crystals with desirable properties and constraints +Presenting: Alex Hernández-García (he/il/él) + Mila AI4Science: Alex Hernandez-Garcia, Alexandre Duval, Alexandra Volokhova, Yoshua Bengio, Divya Sharma, Pierre Luc Carrier, Yasmine Benabed, Michał Koziarski, Victor Schmidt, Pierre-Paul De Breuck .turquoise[SIAM Conference on Mathematical Aspects of Materials Science ([MS24](https://www.siam.org/conferences/cm/conference/ms24)), May 22th 2024] @@ -173,7 +175,11 @@ Instead of optimising the atom positions by learning from a small data set, we d --- ## GFlowNets as the generative framework -### 3 key ingredients +### A brief introduction + +-- + +#### 3 key ingredients -- @@ -681,6 +687,7 @@ Bengio et al. [Flow network based generative models for non-iterative diverse ca .context[Compositional generation of crystals in the space of crystallographic properties.] +
.center[![:scale 100%](../assets/images/slides/crystals/crystalgfn_init.png)] --- @@ -691,6 +698,7 @@ count: false .context[Compositional generation of crystals in the space of crystallographic properties.] +
.center[![:scale 100%](../assets/images/slides/crystals/crystalgfn_sg.png)] --- @@ -701,6 +709,7 @@ count: false .context[Compositional generation of crystals in the space of crystallographic properties.] +
.center[![:scale 100%](../assets/images/slides/crystals/crystalgfn_sg_output.png)] --- @@ -711,6 +720,7 @@ count: false .context[Compositional generation of crystals in the space of crystallographic properties.] +
.center[![:scale 100%](../assets/images/slides/crystals/crystalgfn_comp.png)] --- @@ -721,6 +731,7 @@ count: false .context[Compositional generation of crystals in the space of crystallographic properties.] +
.center[![:scale 100%](../assets/images/slides/crystals/crystalgfn_comp_output.png)] --- @@ -731,6 +742,7 @@ count: false .context[Compositional generation of crystals in the space of crystallographic properties.] +
.center[![:scale 100%](../assets/images/slides/crystals/crystalgfn_lp.png)] --- @@ -741,6 +753,7 @@ count: false .context[Compositional generation of crystals in the space of crystallographic properties.] +
.center[![:scale 100%](../assets/images/slides/crystals/crystalgfn_lp_output.png)] --- @@ -751,6 +764,7 @@ count: false .context[Compositional generation of crystals in the space of crystallographic properties.] +
.center[![:scale 100%](../assets/images/slides/crystals/crystalgfn_all.png)] --- @@ -761,6 +775,7 @@ count: false .context[Compositional generation of crystals in the space of crystallographic properties.] +
.center[![:scale 100%](../assets/images/slides/crystals/crystalgfn_all.png)] .conclusion[Crystal-GFN binds multiple spaces representing crystallographic and material properties, setting intra- and inter-space hard constraints in the generation process.] @@ -801,10 +816,6 @@ We have tested the following properties: - .highlight1[Electronic band gap] [eV] (squared distance to a target value, 1.34 eV), via a pre-trained machine learning model. - .highlight1[Unit cell density] [g/cm3], calculated _exactly_ from the GFN outputs. --- - -.highlight1[Coming soon]: pre-trained machine learning model to predict the ionic conductivity [S/cm]. - --- ## Crystal-GFlowNet