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Patch/restore mdf client (#473)
* Restore missing mdf_client.py from design-renaissance branch This file was part of PR #469 but was not included in the merge, causing ModuleNotFoundError when importing foundry. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * Fix DOI search to return correct dataset The forge DOI search can return multiple results where only one actually has the matching DOI. Previously, get_metadata_by_doi() blindly returned the first result, which often didn't have the requested DOI. Now it iterates through results to find the one with the exact DOI match, fixing test_dataframe_search_by_doi and test_dataframe_download_by_doi tests. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * Move torch/tensorflow to optional extras to fix CI disk space The combined size of torch, tensorflow, and NVIDIA CUDA dependencies exceeded GitHub Actions runner disk space (~4GB+). These ML frameworks are now available as optional extras via pip install .[torch] or pip install .[tensorflow]. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * Fix flake8 linting errors - Remove unused imports (sys, rprint, Optional, pandas, numpy) - Fix unused exception variable - Remove f-string without placeholders - Split long line in MCP server description - Add noqa comment for intentional re-export Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * Replace mdf_forge with internal MDFClient in tests Update test imports to use foundry.mdf_client.MDFClient instead of mdf_forge.Forge, which is no longer a required dependency. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * Add optional extras and document installation Move heavy ML dependencies to optional extras to reduce default install size: - pip install foundry-ml[torch] - pip install foundry-ml[tensorflow] - pip install foundry-ml[huggingface] - pip install foundry-ml[excel] - pip install foundry-ml[examples] - pip install foundry-ml[dev] Update README with extras install instructions and NumPy 2.0 compatibility note. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * Fix DOI search and improve MDFClient query handling MDFClient improvements: - Add Globus Search index ID constants (MDF_INDEX_ID, MDF_TEST_INDEX_ID) - Add match_source_names() method with automatic version suffix stripping - Add _has_field_filters property for elegant advanced mode detection - Use advanced=True automatically for DOI and source_name searches (required for exact field matching in Globus Search) - Add try/finally to ensure query state is always reset after search Foundry search fix: - Pass free-text query to Globus Search for server-side filtering instead of fetching 10 results and filtering client-side - This fixes searches like f.search("Computational Band Gaps") that were failing when the target dataset wasn't in the first 10 results Test additions: - Add test_load_mp_band_gaps_dataset to verify DOI-based dataset loading Re-rendered example notebooks with updated outputs. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
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‎examples/00_hello_foundry/hello_foundry.ipynb‎

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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": "from foundry import Foundry\n\n# Create a Foundry client (uses HTTPS download by default)\n# For cloud environments (Colab, etc.), add: no_browser=True, no_local_server=True\nf = Foundry()"
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"source": [
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"from foundry import Foundry\n",
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"\n",
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"# Create a Foundry client (uses HTTPS download by default)\n",
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"# For cloud environments (Colab, etc.), add: no_browser=True, no_local_server=True\n",
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"f = Foundry()"
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]
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},
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{
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"cell_type": "markdown",
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" <td>root=2022</td>\n",
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" <td>10.18126/jos5-wj65</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>foundry_assorted_computational_band_gaps_v1.1</td>\n",
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" <td>Graph Network Based Deep Learning of Band Gaps...</td>\n",
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" <td>root=2021</td>\n",
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" <td>10.18126/7io9-1z9k</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>foundry_experimental_band_gaps_v1.1</td>\n",
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" <td>Graph Network Based Deep Learning of Band Gaps...</td>\n",
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" <td>root=2021</td>\n",
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" <td>10.18126/wg3u-g8vu</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>foundry_aflow_band_gaps_v1.1</td>\n",
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" <td>Graph Network Based Deep Learning of Band Gaps...</td>\n",
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" <td>root=2021</td>\n",
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" <td>10.18126/6fdy-bsam</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>foundry_oqmd_band_gaps_v1.1</td>\n",
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" <td>Graph Network Based Deep Learning of Band Gaps...</td>\n",
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" <td>root=2021</td>\n",
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" <td>10.18126/w1ey-9y8b</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" dataset_name \\\n",
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"0 foundry_g4mp2_solvation_v1.2 \n",
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"1 foundry_assorted_computational_band_gaps_v1.1 \n",
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"2 foundry_experimental_band_gaps_v1.1 \n",
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"3 foundry_aflow_band_gaps_v1.1 \n",
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"4 foundry_oqmd_band_gaps_v1.1 \n",
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" dataset_name \\\n",
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"0 foundry_g4mp2_solvation_v1.2 \n",
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"\n",
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" title year \\\n",
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"0 DFT Estimates of Solvation Energy in Multiple ... root=2022 \n",
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"1 Graph Network Based Deep Learning of Band Gaps... root=2021 \n",
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"2 Graph Network Based Deep Learning of Band Gaps... root=2021 \n",
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"3 Graph Network Based Deep Learning of Band Gaps... root=2021 \n",
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"4 Graph Network Based Deep Learning of Band Gaps... root=2021 \n",
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"\n",
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" DOI FoundryDataset \n",
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"0 10.18126/jos5-wj65 <foundry.foundry_dataset.FoundryDataset object... \n",
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"1 10.18126/7io9-1z9k <foundry.foundry_dataset.FoundryDataset object... \n",
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"2 10.18126/wg3u-g8vu <foundry.foundry_dataset.FoundryDataset object... \n",
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"3 10.18126/6fdy-bsam <foundry.foundry_dataset.FoundryDataset object... \n",
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"4 10.18126/w1ey-9y8b <foundry.foundry_dataset.FoundryDataset object... "
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"0 10.18126/jos5-wj65 <foundry.foundry_dataset.FoundryDataset object... "
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]
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},
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"execution_count": 3,
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"<h2>DFT Estimates of Solvation Energy in Multiple Solvents</h2>Ward, Logan; Dandu, Naveen; Blaiszik, Ben; Narayanan, Badri; Assary, Rajeev S.; Redfern, Paul C.; Foster, Ian; Curtiss, Larry A.<p>DOI: 10.18126/jos5-wj65</p><h3>Dataset</h3><table><tr><th>short_name</th><td>g4mp2_solvation</td></tr><tr><th>data_type</th><td>tabular</td></tr><tr><th>task_type</th><td><ul><li>supervised</li></ul></td></tr><tr><th>domain</th><td><ul><li>materials science</li><li>chemistry</li></ul></td></tr><tr><th>n_items</th><td>130258.0</td></tr><tr><th>splits</th><td><ul><li><table><tr><th>type</th><td>train</td></tr><tr><th>path</th><td>g4mp2_data.json</td></tr><tr><th>label</th><td>train</td></tr></table></li></ul></td></tr><tr><th>keys</th><td><table><tr><th>key</th><th>type</th><th>filter</th><th>description</th><th>units</th><th>classes</th></tr><tr><td><ul><li>smiles_0</li></ul></td><td>input</td><td></td><td>Input SMILES string</td><td></td><td></td></tr><tr><td><ul><li>smiles_1</li></ul></td><td>input</td><td></td><td>SMILES string after relaxation</td><td></td><td></td></tr><tr><td><ul><li>inchi_0</li></ul></td><td>input</td><td></td><td>InChi after generating coordinates with CORINA</td><td></td><td></td></tr><tr><td><ul><li>inchi_1</li></ul></td><td>input</td><td></td><td>InChi after relaxation</td><td></td><td></td></tr><tr><td><ul><li>xyz</li></ul></td><td>input</td><td></td><td>InChi after relaxation</td><td>XYZ coordinates after relaxation</td><td></td></tr><tr><td><ul><li>atomic_charges</li></ul></td><td>input</td><td></td><td>Atomic charges on each atom, as predicted from B3LYP</td><td></td><td></td></tr><tr><td><ul><li>A</li></ul></td><td>input</td><td></td><td>Rotational constant, A</td><td>GHz</td><td></td></tr><tr><td><ul><li>B</li></ul></td><td>input</td><td></td><td>Rotational constant, B</td><td>GHz</td><td></td></tr><tr><td><ul><li>C</li></ul></td><td>input</td><td></td><td>Rotational constant, C</td><td>GHz</td><td></td></tr><tr><td><ul><li>inchi_1</li></ul></td><td>input</td><td></td><td>InChi after relaxation</td><td></td><td></td></tr><tr><td><ul><li>n_electrons</li></ul></td><td>input</td><td></td><td>Number of electrons</td><td></td><td></td></tr><tr><td><ul><li>n_heavy_atoms</li></ul></td><td>input</td><td></td><td>Number of non-hydrogen atoms</td><td></td><td></td></tr><tr><td><ul><li>n_atom</li></ul></td><td>input</td><td></td><td>Number of atoms in molecule</td><td></td><td></td></tr><tr><td><ul><li>mu</li></ul></td><td>input</td><td></td><td>Dipole moment</td><td>D</td><td></td></tr><tr><td><ul><li>alpha</li></ul></td><td>input</td><td></td><td>Isotropic polarizability</td><td>a_0^3</td><td></td></tr><tr><td><ul><li>R2</li></ul></td><td>input</td><td></td><td>Electronic spatial extant</td><td>a_0^2</td><td></td></tr><tr><td><ul><li>cv</li></ul></td><td>input</td><td></td><td>Heat capacity at 298.15K</td><td>cal/mol-K</td><td></td></tr><tr><td><ul><li>g4mp2_hf298</li></ul></td><td>target</td><td></td><td>G4MP2 Standard Enthalpy of Formation, 298K</td><td>kcal/mol</td><td></td></tr><tr><td><ul><li>bandgap</li></ul></td><td>input</td><td></td><td>B3LYP Band gap energy</td><td>Ha</td><td></td></tr><tr><td><ul><li>homo</li></ul></td><td>input</td><td></td><td>B3LYP Energy of HOMO</td><td>Ha</td><td></td></tr><tr><td><ul><li>lumo</li></ul></td><td>input</td><td></td><td>B3LYP Energy of LUMO</td><td>Ha</td><td></td></tr><tr><td><ul><li>zpe</li></ul></td><td>input</td><td></td><td>B3LYP Zero point vibrational energy</td><td>Ha</td><td></td></tr><tr><td><ul><li>u0</li></ul></td><td>input</td><td></td><td>B3LYP Internal energy at 0K</td><td>Ha</td><td></td></tr><tr><td><ul><li>u</li></ul></td><td>input</td><td></td><td>B3LYP Internal energy at 298.15K</td><td>Ha</td><td></td></tr><tr><td><ul><li>h</li></ul></td><td>input</td><td></td><td>B3LYP Enthalpy at 298.15K</td><td>Ha</td><td></td></tr><tr><td><ul><li>u0_atom</li></ul></td><td>input</td><td></td><td>B3LYP atomization energy at 0K</td><td>Ha</td><td></td></tr><tr><td><ul><li>g</li></ul></td><td>input</td><td></td><td>B3LYP Free energy at 298.15K</td><td>Ha</td><td></td></tr><tr><td><ul><li>g4mp2_0k</li></ul></td><td>target</td><td></td><td>G4MP2 Internal energy at 0K</td><td>Ha</td><td></td></tr><tr><td><ul><li>g4mp2_energy</li></ul></td><td>target</td><td></td><td>G4MP2 Internal energy at 298.15K</td><td>Ha</td><td></td></tr><tr><td><ul><li>g4mp2_enthalpy</li></ul></td><td>target</td><td></td><td>G4MP2 Enthalpy at 298.15K</td><td>Ha</td><td></td></tr><tr><td><ul><li>g4mp2_free</li></ul></td><td>target</td><td></td><td>G4MP2 Free eergy at 0K</td><td>Ha</td><td></td></tr><tr><td><ul><li>g4mp2_atom</li></ul></td><td>target</td><td></td><td>G4MP2 atomization energy at 0K</td><td>Ha</td><td></td></tr><tr><td><ul><li>sol_acetone</li></ul></td><td>target</td><td></td><td>Solvation energy, acetone</td><td>kcal/mol</td><td></td></tr><tr><td><ul><li>sol_acn</li></ul></td><td>target</td><td></td><td>Solvation energy, acetonitrile</td><td>kcal/mol</td><td></td></tr><tr><td><ul><li>sol_dmso</li></ul></td><td>target</td><td></td><td>Solvation energy, dimethyl sulfoxide</td><td>kcal/mol</td><td></td></tr><tr><td><ul><li>sol_ethanol</li></ul></td><td>target</td><td></td><td>Solvation energy, ethanol</td><td>kcal/mol</td><td></td></tr><tr><td><ul><li>sol_water</li></ul></td><td>target</td><td></td><td>Solvation energy, water</td><td>kcal/mol</td><td></td></tr></table></td></tr></table>"
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],
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"text/plain": [
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"<foundry.foundry_dataset.FoundryDataset at 0x1342b8230>"
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"<foundry.foundry_dataset.FoundryDataset at 0x140201070>"
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]
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},
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"execution_count": 4,
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": "# Get the schema - what columns/fields are in this dataset?\nschema = dataset.get_schema()\n\nprint(f\"Dataset: {schema['name']}\")\nprint(f\"Data Type: {schema['data_type']}\")\nprint(f\"\\nSplits: {[s['name'] for s in schema['splits']]}\")\nprint(f\"\\nFields:\")\nfor field in schema['fields']:\n print(f\" - {field['name']} ({field['role']}): {field['description'] or 'No description'}\")"
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Dataset: foundry_g4mp2_solvation_v1.2\n",
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"Data Type: tabular\n",
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"\n",
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"Splits: ['train']\n",
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"\n",
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"Fields:\n",
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" - smiles_0 (input): Input SMILES string\n",
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" - smiles_1 (input): SMILES string after relaxation\n",
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" - inchi_0 (input): InChi after generating coordinates with CORINA\n",
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" - inchi_1 (input): InChi after relaxation\n",
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" - xyz (input): InChi after relaxation\n",
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" - atomic_charges (input): Atomic charges on each atom, as predicted from B3LYP\n",
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" - A (input): Rotational constant, A\n",
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" - B (input): Rotational constant, B\n",
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" - C (input): Rotational constant, C\n",
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" - inchi_1 (input): InChi after relaxation\n",
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" - n_electrons (input): Number of electrons\n",
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" - n_heavy_atoms (input): Number of non-hydrogen atoms\n",
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" - n_atom (input): Number of atoms in molecule\n",
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" - mu (input): Dipole moment\n",
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" - alpha (input): Isotropic polarizability\n",
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" - R2 (input): Electronic spatial extant\n",
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" - cv (input): Heat capacity at 298.15K\n",
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" - g4mp2_hf298 (target): G4MP2 Standard Enthalpy of Formation, 298K\n",
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" - bandgap (input): B3LYP Band gap energy\n",
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" - homo (input): B3LYP Energy of HOMO\n",
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" - lumo (input): B3LYP Energy of LUMO\n",
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" - zpe (input): B3LYP Zero point vibrational energy\n",
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" - u0 (input): B3LYP Internal energy at 0K\n",
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" - u (input): B3LYP Internal energy at 298.15K\n",
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" - h (input): B3LYP Enthalpy at 298.15K\n",
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" - u0_atom (input): B3LYP atomization energy at 0K\n",
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" - g (input): B3LYP Free energy at 298.15K\n",
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" - g4mp2_0k (target): G4MP2 Internal energy at 0K\n",
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" - g4mp2_energy (target): G4MP2 Internal energy at 298.15K\n",
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" - g4mp2_enthalpy (target): G4MP2 Enthalpy at 298.15K\n",
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" - g4mp2_free (target): G4MP2 Free eergy at 0K\n",
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" - g4mp2_atom (target): G4MP2 atomization energy at 0K\n",
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" - sol_acetone (target): Solvation energy, acetone\n",
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" - sol_acn (target): Solvation energy, acetonitrile\n",
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" - sol_dmso (target): Solvation energy, dimethyl sulfoxide\n",
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" - sol_ethanol (target): Solvation energy, ethanol\n",
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" - sol_water (target): Solvation energy, water\n"
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]
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}
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],
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"source": [
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"# Get the schema - what columns/fields are in this dataset?\n",
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"schema = dataset.get_schema()\n",
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"\n",
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"print(f\"Dataset: {schema['name']}\")\n",
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"print(f\"Data Type: {schema['data_type']}\")\n",
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"print(f\"\\nSplits: {[s['name'] for s in schema['splits']]}\")\n",
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"print(f\"\\nFields:\")\n",
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"for field in schema['fields']:\n",
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" print(f\" - {field['name']} ({field['role']}): {field['description'] or 'No description'}\")"
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]
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"outputs": [
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"text": [
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"Processing records: 100%|█████████████████████████████████| 1/1 [00:00<00:00, 3266.59it/s]\n",
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"Transferring data: 0%| | 0/1 [00:00<?, ?it/s]"
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]
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},
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"name": "stdout",
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"text": [
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"Error: GC_NOT_CONNECTED - globus connect offline\n"
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"Data keys: dict_keys(['train'])\n"
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]
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Training data shape: <class 'tuple'>\n",
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"\n",
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"Inputs (X): <class 'pandas.core.frame.DataFrame'>\n",
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"Targets (y): <class 'pandas.core.frame.DataFrame'>\n"
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]
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}
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],
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"source": [
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"# For ML datasets, data is typically split into inputs (X) and targets (y)\n",
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"# Let's explore the training split\n",
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"cell_type": "code",
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Foundry works with PyTorch and TensorFlow out of the box!\n"
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]
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}
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],
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"source": [
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"# For PyTorch users:\n",
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"# torch_dataset = dataset.get_as_torch(split='train')\n",
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},
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{
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"cell_type": "code",
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"@misc{https://doi.org/10.18126/jos5-wj65\n",
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"doi = {10.18126/jos5-wj65}\n",
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"url = {https://doi.org/10.18126/jos5-wj65}\n",
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"author = {Ward, Logan and Dandu, Naveen and Blaiszik, Ben and Narayanan, Badri and Assary, Rajeev S. and Redfern, Paul C. and Foster, Ian and Curtiss, Larry A.}\n",
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"title = {DFT Estimates of Solvation Energy in Multiple Solvents}\n",
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"keywords = {machine learning, foundry}\n",
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"publisher = {Materials Data Facility}\n",
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"year = {root=2022}}\n"
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]
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}
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],
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"source": [
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"# Get BibTeX citation\n",
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"citation = dataset.get_citation()\n",
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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}

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