From 314f2d565b2df1d72f53f36d2836692b677a1197 Mon Sep 17 00:00:00 2001 From: Alex Date: Wed, 13 Dec 2023 16:37:22 -0600 Subject: [PATCH] Fixes --- slides/crystal-gfn-ai4mat23.md | 17 +++++++++-------- 1 file changed, 9 insertions(+), 8 deletions(-) diff --git a/slides/crystal-gfn-ai4mat23.md b/slides/crystal-gfn-ai4mat23.md index 9307b396..d598c09c 100644 --- a/slides/crystal-gfn-ai4mat23.md +++ b/slides/crystal-gfn-ai4mat23.md @@ -46,7 +46,7 @@ Here, we are concerned mainly with _inorganic crystals_, where the constituents -- -A crystal structure is characterized by its .highlight1[unit cell], a small imaginary box containing atoms in a specific spatial arrangement with certain symmetry. The unit cell repeats iself periodically in all directions. +A crystal structure is characterized by its .highlight1[unit cell], a small imaginary box containing atoms in a specific spatial arrangement with certain symmetry. The unit cell repeats itself periodically in all directions. --- @@ -56,17 +56,17 @@ Many solid state materials are crystal structures and they are a core component -- -Accelerating .highlight1[material discovery is key in the climate crisis] .cite[IPCC Sixth Assessment Report, 2022]: +Accelerating .highlight1[material discovery is key in the climate crisis]. From the IPCC Sixth Assessment Report, 2022: * Improving material efficiency can reduce 0.93 ($\pm$ 0.23) GtCO₂-eq per year. * Fuel switching can reduce 2.1 ($\pm$ 0.52) GtCO₂-eq per year, only in the industry sector. * Carbon capture and storage can reduce 0.54 ($\pm$ 0.27) GtCO₂-eq per year in the energy sector. -.smaller[.footnote[† Global anthropogenic emissions in 2019 were estimated in 59 ($\pm$ 6.6) GtCO₂-eq. The budget from 2020 to limit warming to 1.5°C is estimated in 510 ($\pm$ 180) GtCO₂-eq.]] +.smaller[.footnote[Global anthropogenic emissions in 2019 were estimated in 59 ($\pm$6.6) GtCO₂-eq. The budget from 2020 to limit warming to 1.5°C is estimated in 510 ($\pm$180) GtCO₂-eq.]] -- However, .highlight1[material modelling is very challenging]: -* Limited data: only about 200 K known inorganic materials, but potentially $10^{180}$ possible stable materials (for reference: more than a billion molecules are known) +* Limited data: only about 200k known inorganic materials, but potentially $10^{180}$ possible stable materials (for reference: more than a billion molecules are known) * Sparsity: .highlight2[stable materials] only exist in a low-dimensional subspace of all possible 3D arrangements. --- @@ -119,7 +119,7 @@ Example: .highlight2[MatterGen]: An evolution of CDVAE that performs diffusion n Instead of optimising the atom positions by learning from a small data set, we draw .highlight1[inspiration from theoretical crystallography to sample crystals in a lower-dimensional space of crystal structure parameters]. .left-column[ -.center[![:scale 60%](../assets/images/slides/crystals/crystal_systems_table.png)] +.center[![:scale 65%](../assets/images/slides/crystals/crystal_systems_table.png)] ] .right-column[ .center[![:scale 30%](../assets/images/slides/crystals/unit_cell.png)] @@ -273,7 +273,7 @@ count: false ## Results -.context[10,000 crystals randomly sampled.] +.context[10,000 crystals sampled from a randomly initialised, untrained Crystal-GFN.] .center[![:scale 80%](../assets/images/slides/crystals/distributions_fe_val_rand.png)] @@ -296,8 +296,9 @@ count: false - 5 out of 8 crystal-lattice systems in the top-100. - All 5 point symmetries in the top-100. - All 12 elements found in the 10,000 samples. - - 10 out of 12 elements in the top-100 + - 10 out of 12 elements in the top-100. - 80 out of 113 space groups (70 %) found in the 10,000 samples + - 19 out of 113 spacce groups in the top-100. --- @@ -315,7 +316,7 @@ class: title, middle * Discovering new crystal structures with desirable properties can help mitigate the climate crisis. * There are infinitely many conceivable crystals. Only a few are stable. Only a few stable crystals have interesting properties. This is a hard problem. * Crystal-GFN introduces .highlight1[physicochemical and structural constraints], reducing the search space. - * Crystal-GFN was .highlight1[trained in 12 hours in a CPU-only machine]. + * Crystal-GFN was trained in 12 hours in a CPU-only machine. * Our results show that we can generate .highlight1[diverse, high scoring samples with the desired constraints]. * The .highlight1[framework can be flexibly extended] with more constraints, crystal structure descriptors (atomic positions) and other properties.