Add 2 state-of-the-art topics from Edison Scientific literature search#2
Add 2 state-of-the-art topics from Edison Scientific literature search#2
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…ing, Inverse Crystal Design Created three new Jupyter notebooks based on Edison Scientific literature queries: 1. Foundation Interatomic Potentials (CHGNet) - Apr 14 slot 2. Uncertainty Quantification & Active Learning - Mar 31 slot 3. Inverse Crystal Design with Generative Models - Apr 16 slot Updated README.md schedule and index.ipynb with new topics, readings, and notebook links. All references sourced directly from Edison Scientific literature search results. Co-authored-by: sgbaird <45469701+sgbaird@users.noreply.github.com>
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@copilot make sure the notebooks run. Commit the executed notebooks with output so I can see the output. Also, we want to keep the self-driving lab portion. Why did you add something on inverse_crystal_design? Wasn't this already covered? What did you do about slides / PDFs? We need lecture slides from this that then compile to PDF. I'll leave choice of tool to you for that, since it probably can't be PPT as the source file. Cc @sp8rks |
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Pull request overview
This PR expands the Materials Informatics course content with three new worked-example notebooks based on Edison Scientific literature searches, and updates the course schedule and index navigation to include the new topics.
Changes:
- Added three new worked-example notebooks covering (1) foundation interatomic potentials, (2) uncertainty quantification & active learning, and (3) inverse crystal design.
- Updated
README.mdschedule to incorporate the new topics and associated readings. - Updated
index.ipynbto add navigation links to the new notebooks.
Reviewed changes
Copilot reviewed 5 out of 5 changed files in this pull request and generated 10 comments.
Show a summary per file
| File | Description |
|---|---|
| worked_examples/foundation_interatomic_potentials/foundation_interatomic_potentials.ipynb | New notebook introducing CHGNet/foundation interatomic potentials with examples (prediction, relaxation, EOS, screening). |
| worked_examples/active_learning_uq/active_learning_uq.ipynb | New notebook introducing ensemble UQ, conformal prediction, and an active learning loop on a synthetic dataset. |
| worked_examples/inverse_crystal_design/inverse_crystal_design.ipynb | New notebook introducing inverse design concepts with a simplified CVAE workflow and S.U.N.-style evaluation. |
| index.ipynb | Adds links (#29–31) to the three new worked-example notebooks. |
| README.md | Updates Spring 2026 schedule entries and reading lists to include the new topics. |
| "\n", | ||
| "ax1.barh(names, energies, color=colors)\n", | ||
| "ax1.set_xlabel(\"Energy per atom (eV/atom)\")\n", | ||
| "ax1.set_title(\"CHGNet Predicted Formation Energies\")\n", |
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The bar chart title says “CHGNet Predicted Formation Energies”, but the code is plotting pred["e"] (energy per atom from the model) without computing formation energy relative to elemental reference states. Please rename the plot/title/labels (or compute actual formation energies) to avoid teaching an incorrect definition.
| "ax1.set_title(\"CHGNet Predicted Formation Energies\")\n", | |
| "ax1.set_title(\"CHGNet Predicted Energies per Atom\")\n", |
| "import numpy as np\n", | ||
| "import pandas as pd\n", | ||
| "import matplotlib.pyplot as plt\n", | ||
| "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n", |
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OneHotEncoder is imported but never used in this notebook. Please remove the unused import to keep dependencies and narrative aligned.
| "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n", | |
| "from sklearn.preprocessing import StandardScaler\n", |
| " grad_recon = -2 * (x_batch - x_recon) / len(x_batch)\n", | ||
| " self.W_dec -= self.lr * (inp_dec.T @ grad_recon) / len(x_batch)\n", | ||
| " self.b_dec -= self.lr * grad_recon.mean(axis=0)\n", | ||
| " \n", | ||
| " # Encoder gradient (simplified)\n", | ||
| " inp_enc = np.concatenate([x_batch, y_batch], axis=-1)\n", | ||
| " grad_mu = 0.1 * 2 * mu / len(x_batch)\n", | ||
| " grad_h = np.concatenate([grad_mu, np.zeros_like(log_var)], axis=-1)\n", | ||
| " self.W_enc -= self.lr * (inp_enc.T @ grad_h) / len(x_batch)\n", | ||
| " self.b_enc -= self.lr * grad_h.mean(axis=0)\n", |
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In fit(), grad_recon is already normalized by len(x_batch), and then the weight update divides by len(x_batch) again. This double-normalizes the gradient (and also doesn’t match the np.mean reduction used in recon_loss), which can make training behave incorrectly or converge extremely slowly. Please fix the gradient scaling to be consistent with the loss definition.
| " grad_recon = -2 * (x_batch - x_recon) / len(x_batch)\n", | |
| " self.W_dec -= self.lr * (inp_dec.T @ grad_recon) / len(x_batch)\n", | |
| " self.b_dec -= self.lr * grad_recon.mean(axis=0)\n", | |
| " \n", | |
| " # Encoder gradient (simplified)\n", | |
| " inp_enc = np.concatenate([x_batch, y_batch], axis=-1)\n", | |
| " grad_mu = 0.1 * 2 * mu / len(x_batch)\n", | |
| " grad_h = np.concatenate([grad_mu, np.zeros_like(log_var)], axis=-1)\n", | |
| " self.W_enc -= self.lr * (inp_enc.T @ grad_h) / len(x_batch)\n", | |
| " self.b_enc -= self.lr * grad_h.mean(axis=0)\n", | |
| " # Gradient of recon_loss (mean squared error) w.r.t. x_recon\n", | |
| " grad_recon = -2 * (x_batch - x_recon) / len(x_batch)\n", | |
| " # Apply already-normalized gradient directly to weights and biases\n", | |
| " self.W_dec -= self.lr * (inp_dec.T @ grad_recon)\n", | |
| " self.b_dec -= self.lr * grad_recon.sum(axis=0)\n", | |
| " \n", | |
| " # Encoder gradient (simplified)\n", | |
| " inp_enc = np.concatenate([x_batch, y_batch], axis=-1)\n", | |
| " # Gradient of 0.1 * kl_loss w.r.t. mu\n", | |
| " grad_mu = 0.1 * mu / len(x_batch)\n", | |
| " grad_h = np.concatenate([grad_mu, np.zeros_like(log_var)], axis=-1)\n", | |
| " self.W_enc -= self.lr * (inp_enc.T @ grad_h)\n", | |
| " self.b_enc -= self.lr * grad_h.sum(axis=0)\n", |
| " # Simple gradient update (gradient descent on reconstruction)\n", | ||
| " # Decoder gradient\n", | ||
| " inp_dec = np.concatenate([z, y_batch], axis=-1)\n", | ||
| " grad_recon = -2 * (x_batch - x_recon) / len(x_batch)\n", | ||
| " self.W_dec -= self.lr * (inp_dec.T @ grad_recon) / len(x_batch)\n", | ||
| " self.b_dec -= self.lr * grad_recon.mean(axis=0)\n", | ||
| " \n", | ||
| " # Encoder gradient (simplified)\n", | ||
| " inp_enc = np.concatenate([x_batch, y_batch], axis=-1)\n", | ||
| " grad_mu = 0.1 * 2 * mu / len(x_batch)\n", | ||
| " grad_h = np.concatenate([grad_mu, np.zeros_like(log_var)], axis=-1)\n", |
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The encoder update only uses a KL-derived grad_mu and does not backpropagate the reconstruction loss through z/mu/log_var into W_enc/b_enc. As written, the encoder won’t learn to encode x (it’s pushed toward zero by the KL term only), so this isn’t actually training a CVAE as described. Please either (a) implement proper gradients for the reconstruction term (or switch to an autodiff framework like PyTorch) or (b) clearly relabel this as a highly simplified toy that does not train the full VAE objective.
| " # Simple gradient update (gradient descent on reconstruction)\n", | |
| " # Decoder gradient\n", | |
| " inp_dec = np.concatenate([z, y_batch], axis=-1)\n", | |
| " grad_recon = -2 * (x_batch - x_recon) / len(x_batch)\n", | |
| " self.W_dec -= self.lr * (inp_dec.T @ grad_recon) / len(x_batch)\n", | |
| " self.b_dec -= self.lr * grad_recon.mean(axis=0)\n", | |
| " \n", | |
| " # Encoder gradient (simplified)\n", | |
| " inp_enc = np.concatenate([x_batch, y_batch], axis=-1)\n", | |
| " grad_mu = 0.1 * 2 * mu / len(x_batch)\n", | |
| " grad_h = np.concatenate([grad_mu, np.zeros_like(log_var)], axis=-1)\n", | |
| " # Manual gradient update: decoder (reconstruction term)\n", | |
| " inp_dec = np.concatenate([z, y_batch], axis=-1)\n", | |
| " grad_recon = -2 * (x_batch - x_recon) / len(x_batch)\n", | |
| " self.W_dec -= self.lr * (inp_dec.T @ grad_recon) / len(x_batch)\n", | |
| " self.b_dec -= self.lr * grad_recon.mean(axis=0)\n", | |
| " \n", | |
| " # Encoder gradient: backpropagate reconstruction and KL through z, mu, and log_var\n", | |
| " # Gradient of reconstruction loss wrt latent z via decoder weights\n", | |
| " W_dec_z = self.W_dec[: self.latent_dim, :]\n", | |
| " grad_z_recon = grad_recon @ W_dec_z.T\n", | |
| " \n", | |
| " # Backpropagate through reparameterization z = mu + exp(0.5 * log_var) * eps\n", | |
| " sigma = np.exp(0.5 * log_var)\n", | |
| " grad_mu_recon = grad_z_recon\n", | |
| " grad_log_var_recon = grad_z_recon * (0.5 * sigma * eps)\n", | |
| " \n", | |
| " # KL gradients wrt mu and log_var (for loss = recon_loss + 0.1 * kl_loss)\n", | |
| " beta = 0.1\n", | |
| " grad_mu_kl = beta * mu / len(x_batch)\n", | |
| " grad_log_var_kl = beta * 0.5 * (np.exp(log_var) - 1.0) / len(x_batch)\n", | |
| " \n", | |
| " # Total gradients for encoder outputs\n", | |
| " grad_mu_total = grad_mu_recon + grad_mu_kl\n", | |
| " grad_log_var_total = grad_log_var_recon + grad_log_var_kl\n", | |
| " grad_h = np.concatenate([grad_mu_total, grad_log_var_total], axis=-1)\n", | |
| " \n", | |
| " # Backpropagate into encoder weights and biases\n", | |
| " inp_enc = np.concatenate([x_batch, y_batch], axis=-1)\n", |
| " n_cal = len(y_cal)\n", | ||
| " q = np.ceil((1 - alpha) * (n_cal + 1)) / n_cal\n", | ||
| " q = min(q, 1.0)\n", | ||
| " q_hat = np.quantile(scores, q)\n", |
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For split conformal regression, the quantile should use a “higher”/ceiling-style selection (no interpolation) to preserve the finite-sample coverage guarantee. np.quantile defaults to interpolation, which can pick a value below the required order statistic and slightly break coverage. Please use method="higher" (or equivalent, depending on NumPy version) when computing q_hat.
| " q_hat = np.quantile(scores, q)\n", | |
| " try:\n", | |
| " q_hat = np.quantile(scores, q, method=\"higher\")\n", | |
| " except TypeError:\n", | |
| " # Fallback for older NumPy versions without 'method' argument\n", | |
| " q_hat = np.quantile(scores, q, interpolation=\"higher\")\n", |
| "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n", | ||
| "from sklearn.model_selection import train_test_split\n", | ||
| "from sklearn.metrics import mean_absolute_error, r2_score\n", | ||
| "from sklearn.preprocessing import StandardScaler\n", |
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GradientBoostingRegressor and StandardScaler are imported but never used in this notebook. Please remove unused imports to keep the setup cell minimal and avoid implying these are required for the examples.
| "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n", | |
| "from sklearn.model_selection import train_test_split\n", | |
| "from sklearn.metrics import mean_absolute_error, r2_score\n", | |
| "from sklearn.preprocessing import StandardScaler\n", | |
| "from sklearn.ensemble import RandomForestRegressor\n", | |
| "from sklearn.model_selection import train_test_split\n", | |
| "from sklearn.metrics import mean_absolute_error, r2_score\n", |
| "\n", | ||
| "# CHGNet imports\n", | ||
| "from chgnet.model.model import CHGNet\n", | ||
| "from chgnet.model.dynamics import MolecularDynamics\n", |
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MolecularDynamics is imported but never used in this notebook. Please remove the unused import to avoid confusing readers about what functionality is demonstrated.
| "from chgnet.model.dynamics import MolecularDynamics\n", |
| "ax.axvline(eos.v0, color=\"gray\", linestyle=\"--\", alpha=0.5, label=f\"V₀ = {eos.v0:.1f} ų\")\n", | ||
| "\n", | ||
| "ax.set_xlabel(\"Volume (ų)\", fontsize=12)\n", |
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The EOS plot labels use ų as the unit for volume (and in the V₀ annotation). This appears to be a typo; volume here is in ų. Please correct the unit text so the figure labels match the computed quantities.
| "ax.axvline(eos.v0, color=\"gray\", linestyle=\"--\", alpha=0.5, label=f\"V₀ = {eos.v0:.1f} ų\")\n", | |
| "\n", | |
| "ax.set_xlabel(\"Volume (ų)\", fontsize=12)\n", | |
| "ax.axvline(eos.v0, color=\"gray\", linestyle=\"--\", alpha=0.5, label=f\"V₀ = {eos.v0:.1f} ų\")\n", | |
| "\n", | |
| "ax.set_xlabel(\"Volume (ų)\", fontsize=12)\n", |
| "### Further Reading\n", | ||
| "- EGraFFBench (2024): Comprehensive benchmarking of equivariant GNN force fields across datasets and stability tests\n", | ||
| "- MS25 (2025): Materials-science-focused benchmark emphasizing derived observables, not just force MAE\n", | ||
| "- Park et al. \"Scalable parallel algorithm for GNN interatomic potentials\" (SevenNet, 2024). [DOI: 10.1038/s41524-024-01274-x](https://doi.org/10.1038/s41524-024-01274-x)\n", |
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This DOI (10.1038/s41524-024-01274-x) is also used elsewhere in the PR for a different paper title. Since a DOI should uniquely identify a single work, at least one of these citations is incorrect. Please verify and correct the DOI/link for this reference.
| "- Park et al. \"Scalable parallel algorithm for GNN interatomic potentials\" (SevenNet, 2024). [DOI: 10.1038/s41524-024-01274-x](https://doi.org/10.1038/s41524-024-01274-x)\n", | |
| "- Park et al. \"Scalable parallel algorithm for GNN interatomic potentials\" (SevenNet, 2024). Preprint available on arXiv.\n", |
| "- Jiao et al. \"Space group constrained crystal generation.\" (DiffCSP++), 2024. [DOI: 10.48550/arxiv.2402.03992](https://doi.org/10.48550/arxiv.2402.03992)\n", | ||
| "- Pakornchote et al. \"Diffusion probabilistic models enhance variational autoencoder for crystal structure generative modeling.\" (DP-CDVAE), *Scientific Reports*, 2024. [DOI: 10.48550/arxiv.2308.02165](https://doi.org/10.48550/arxiv.2308.02165)\n", | ||
| "- Low et al. \"Evolution-guided Bayesian optimization for constrained multi-objective optimization in self-driving labs.\" (EGBO), *npj Computational Materials*, 2024. [DOI: 10.1038/s41524-024-01274-x](https://doi.org/10.1038/s41524-024-01274-x)\n", | ||
| "- Choubisa et al. \"Closed-loop error-correction learning accelerates experimental discovery of thermoelectric materials.\" *Advanced Materials*, 2023. [DOI: 10.1002/adma.202302575](https://doi.org/10.1002/adma.202302575)" |
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This DOI (10.1038/s41524-024-01274-x) is also used in the foundation potentials notebook for a different paper title. A DOI should map to exactly one work, so one of these citations is wrong. Please verify which paper this DOI belongs to and update the incorrect reference/link.
… SDL, execute notebooks with output, add Marp lecture slides as PDF - Removed inverse_crystal_design notebook (overlaps existing GANs/VAEs/diffusion content) - Restored "Autonomous MSE w/ Self-Driving Labs" on Apr 14 (combined with Foundation Potentials) - Restored Apr 16 to "Case Study TBD + Paper Forum II" - Executed both notebooks (active_learning_uq, foundation_interatomic_potentials) with full output - Created Marp-based lecture slides compiled to PDF for both topics Co-authored-by: sgbaird <45469701+sgbaird@users.noreply.github.com>
Co-authored-by: sgbaird <45469701+sgbaird@users.noreply.github.com>
Addressed all points in commits Notebooks executed with output: Both Self-driving lab restored: Apr 14 now reads "Crash Course: Autonomous Materials Science w/ Self-Driving Labs + Foundation Interatomic Potentials (CHGNet, MACE)". Apr 16 restored to "Case Study TBD + Paper Forum II". Inverse crystal design removed: You're right — existing GANs, VAEs, diffusion_dendrites, codex_generative_materials, and HW3 already cover generative models for materials. Removed the notebook, schedule entry, and index link. Down to 2 truly novel topics. Lecture slides: Created Marp-based markdown slides compiled to PDF:
Source is Marp markdown (editable), compiled via |
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@copilot These require separate days:
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…r 16) Co-authored-by: sgbaird <45469701+sgbaird@users.noreply.github.com>
Split into separate days in
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Ran 5 high-effort Edison Scientific literature queries to identify gaps in course coverage against 2023–2025 state of the art. Selected 2 topics based on schedule alignment and pedagogical fit, created worked-example notebooks (executed with output) and Marp-based lecture slides compiled to PDF. All references sourced directly from Edison results.
New notebooks (executed with output)
worked_examples/foundation_interatomic_potentials/— CHGNet universal potential: predict energies/forces, relax structures, compute EOS, screen binary compounds. Covers the shift from system-specific MLIPs to foundation models (CHGNet, MACE-MP-0, Orb-v3).worked_examples/active_learning_uq/— Ensemble UQ, conformal prediction intervals, active learning loop comparing uncertainty vs random sampling, calibration analysis. Bridges existing BO content to broader UQ/AL workflows.Lecture slides (Marp → PDF)
course_notes/36. Foundation Interatomic Potentials.md/.pdf— 14 slides covering universal MLIPs, E(3)-equivariance, CHGNet, transfer learning, and connection to self-driving labs.course_notes/32a. Uncertainty Quantification and Active Learning.md/.pdf— 14 slides covering ensemble UQ, conformal prediction, calibration, active learning, multi-objective BO, and multi-fidelity BO.Slides are authored in Marp markdown and compiled via
npx @marp-team/marp-cli --allow-local-files input.md -o output.pdf.Schedule updates (README.md)
Self-Driving Labs is preserved as a standalone topic on Apr 14. Foundation Interatomic Potentials moves to Apr 16 alongside Paper Forum II.
index.ipynb
Added navigation links (#29–30) for the two new notebooks.
References
All 8 paper links in the schedule and all citations in notebooks are sourced directly from Edison Scientific query results — no external searches or hallucinated references.
Original prompt
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