From 6851a2c9f3aa15a1484242d7682dc24ee64356f0 Mon Sep 17 00:00:00 2001 From: Ryan Date: Fri, 12 Jul 2024 08:58:23 -0400 Subject: [PATCH 1/2] Docs: add schema tutorial --- docs/_static/docs_store_Si_relax.json | 1 + docs/index.md | 1 + docs/user/docs_schemas_emmet.md | 507 ++++++++++++++++++++++++++ docs/user/install.md | 2 + docs/user/running-workflows.md | 1 + 5 files changed, 512 insertions(+) create mode 100644 docs/_static/docs_store_Si_relax.json create mode 100644 docs/user/docs_schemas_emmet.md diff --git a/docs/_static/docs_store_Si_relax.json b/docs/_static/docs_store_Si_relax.json new file mode 100644 index 0000000000..f0c5f23223 --- /dev/null +++ b/docs/_static/docs_store_Si_relax.json @@ -0,0 +1 @@ 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17:13:34.897366","run_stats":{"standard":{"average_memory":0.0,"max_memory":241584.0,"elapsed_time":18.833,"system_time":1.114,"user_time":16.166,"total_time":17.28,"cores":40},"overall":{"average_memory":0.0,"max_memory":241584.0,"elapsed_time":18.833,"system_time":1.114,"user_time":16.166,"total_time":17.28,"cores":40}},"@module":"emmet.core.tasks","@class":"TaskDoc","@version":null},"completed_at":"2024-05-19T17:13:46.400349","metadata":{},"hosts":["dbaebabf-134d-426a-b91c-15abf799da65"],"name":"relax","@module":"jobflow.core.schemas","@class":"JobStoreDocument","@version":"0.1.17"}] \ No newline at end of file diff --git a/docs/index.md b/docs/index.md index 646fad8693..61c65534e3 100644 --- a/docs/index.md +++ b/docs/index.md @@ -4,6 +4,7 @@ user/index user/install user/running-workflows +user/docs-schemas-emmet user/fireworks user/codes/index ``` diff --git a/docs/user/docs_schemas_emmet.md b/docs/user/docs_schemas_emmet.md new file mode 100644 index 0000000000..6f69ff8c8d --- /dev/null +++ b/docs/user/docs_schemas_emmet.md @@ -0,0 +1,507 @@ +(docs_schemas_emmet)= + +# An introduction to task documents, schemas, and emmet + +## Introduction + +If you have been [running-workflows](running_workflows), you are now starting +to generate data. `atomate2` stores both input and output data for every step of +its workflows in Task Documents. Task Documents define a *schema* or structure for +organizing information from different types of calculations, which then facilitates +automatic processing with tools like `emmet` or `maggma`. This tutorial will familiarize you with +these basic concepts. + +### Objectives + +* Understand how `atomate2` stores and organizes calculation data +* Expain the meaning of a "Document Model" or schema +* Inspect a `TaskDoc` generated by `atomate2` + +### Prerequisites + +To complete this tutorial, you need + +* A working installation of `atomate2` +* (optional) to complete the [running workflows](running-workflows.md) tutorial. + +## How `atomate2` stores and organizes data. + +As explained in [Configure calculation output database](install.md#configure-calculation-output-database), `atomate2` +stores the results of every `Job` in a database. More specifically, `atomate2` uses a +[`maggma.Store`](https://materialsproject.github.io/maggma/getting_started/stores/) to interface with a data storage backend +(usually MongoDB). Data is stored in a `JSON`-like or python `dict`-like format, which you can think of as a list of +dictionaries, where each dictionary represents one `Job`. Each dictionary in the list is called a "document", +so "document" refers to the output data from a single `Job `. + +To facilitate automated processing and analysis, it's important that every document follows a +consistent format. That's where schemas (also called "Document Models") come in. + +## Document Models + +### Schema for `Job` + +Document models define a specific format (i.e., structure and data types) for a given `Job` or calculation. +`atomate2` uses [pydantic](https://docs.pydantic.dev/latest/) to define these schemas. If you'd like +to learn more about `pydantic`, we suggest reading [this introduction]( +https://realpython.com/python-pydantic/#getting-familiar-with-pydantic). In brief, every Document Model in `atomate2` +is an instance of `pydantic.BaseModel`. The `BaseModel` is then turned into a `dict` (serialized) before being +inserted into the store. + +To understand how this works, we are going to look at the output data from a structural relaxation for Si. If we +examine the `docs` store after running this `Job`, we will see something similar to the following: + +```json +[ + {"uuid":"c2b5eb7d-838b-4dee-896f-95f21867b62b", + "index":1, + "output":{...}, + "completed_at":"2024-05-19T17:13:46.400349", + "metadata":{}, + "hosts":["dbaebabf-134d-426a-b91c-15abf799da65"], + "name":"relax", + "@module":"jobflow.core.schemas", + "@class":"JobStoreDocument", + "@version":"0.1.17" + }, +] +``` + + +This document follows a schema (`JobStoreDocument`, defined [here](https://github.com/materialsproject/jobflow/blob/main/src/jobflow/core/schemas.py)) +that contains information about the `Job`, such as: + - `uuid`: a unique identifier for the `Job` + - `output`: The actual job output (e.g., calculation results). We'll examine this in the next section. + - `completed_at`: The time the job was completed. + - `name`: The name of the job (in this case "relax" because we did a structure relaxation) + - `@module`, `@class`, `@version`: These keys store the specific origin and version of the document model so that it can + be easily re-created from the `dict`. + +Because every `atomate2` document is first created as a `JobStoreDocument` before being inserted into the database, +you can be assured that every `Job` you run will contain these keys. Document Models have the additional benefit +of validating the data types, so for example, `name` is guaranteed to a `str`, whereas `index` is guaranteed to be a `int`. + +```{warning} +In this tutorial, we show only **excerpts** of the output data to highlight key points. For example, +in the box above we have collapsed the `outputs` key. You are encouraged to open the +[complete output data (`.json` format)](../_static/docs_store_Si_relax.json) in a separate tab of your web browser +and refer to it as you read through this tutorial. +``` + +### Schema for `output` + +The output data for the calculation itself (the contents of the `Job`) are stored in the `output` key. **The schema +of `output` will vary depending on the type of calculation (e.g., VASP relaxation, Q-Chem static, etc.), but will +always be consistent for a particular `Job` type**. In the case of a VASP calculation, the schema is called +a `TaskDoc`. + +That being said, most `Job` types have a few features in common, which we will highlight in our example. If we look +at the top-level keys of `output` from the `JobStoreDocument` in the previous section, we see: + +```json +{ + "builder_meta": {...} + "nsites": 2, + "elements": ["Si"], + "nelements": 1, + "composition": {"Si": 2}, + "composition_reduced": {"Si": 1}, + "formula_pretty": "Si", + "formula_anonymous": "A", + "chemsys": "Si", + "volume": 40.163300666862035, + "density": 2.3223723738160613, + "density_atomic": 20.081650333431018, + "symmetry": {...}, + "tags": null, + "dir_name": "/scratch/gpfs/.../job_2024-05-19-21-13-15-058677-64911", + "state": "successful", + "calcs_reversed": [...], + "structure": {...}, + "task_type": "Structure Optimization", + "task_id": null, + "orig_inputs": {...}, + "input": {...}, + "output": {...}, + "@module": "emmet.core.tasks", + "@class": "TaskDoc", + "@version": null +} +``` + +Even though we are looking at an example for a VASP calculations, **`atomate2` uses hierarchichal or modular Document +Models wherever possible**. Therefore, the Task Documents generated for other calculation types have the same +general structure (e.g., `inputs`, `outputs`, structure metadata, `custodian`, `orig_inputs`, `calcs_reversed`, etc.) + +We describe many of these top-level keys in more detail in the following subsections. + +```{note} +You can also generate `TaskDoc` from VASP calculations that you have run manually. To do + so, use the `from_directory` class method: + ```python + from emmet.core.tasks import TaskDoc + doc = TaskDoc.from_directory("") +``` + +#### Structure Metadata + +The root level of the `TaskDoc` has keys containing basic structural information including: + - `nsites`: The number of sites + - `composition`: Full composition for the material. + - `elements`: List of elements in the material. + - `formula_pretty`: Cleaned representation of the formula. + - `chemsys`: dash-delimited string of elements in the material. + +And more. These keys illustrate another principle of Document Models -- they are +**hierarchical**. Specifically, the structure metadata keys are populated by _another_ `pydantic` schema called +[StructureMetadata](https://github.com/materialsproject/emmet/blob/efc0b22ff835b11b1c825cbc93d4bbebf84a0d91/emmet-core/emmet/core/structure.py#L18) +defined in `emmet`. So the `TaskDoc` schema comprises several subsidiary models that organize different types of +information, as discussed further below. + +#### `structure` + +The `structure` key contains the **final output structure** of the calculation as a serialized `pymatgen.Structure` object. + +```json +"structure": { + "@module": "pymatgen.core.structure", + "@class": "Structure", + "charge": 0, + "lattice": {...}, + "properties": {}, + "sites": [...] + } +``` + +#### `builder_meta` + +The `builder_meta` key contains information about the software used to generate the data in the `TaskDoc`. Here is the +example from our structure relaxation: + +```json +"builder_meta": { + "emmet_version": "0.83.0", + "pymatgen_version": "2024.4.13", + "pull_request": null, + "database_version": null, + "build_date": "2024-05-19T21:13:45.541000", + "license": null + } +``` + +#### Calculation metadata: `dir_name`, `run_stats`, `task_label`, and `task_type` + +- `task_label`: A user-definable label for the specific calculation +- `task_type`: A standardized label specifying the specific type of calculation being performed. +- `dir_name`: The path of the directory in which input/output files were written +- `run_stats`: Information about the walltime, cpu time, and computational resources utilized. + +```json +"task_type": "Structure Optimization", +"task_label": "relax", +"dir_name": "della-r3c1n3:/scratch/gpfs/ab6989/MPScanRelaxSet/atomate2/Ca_Mg_runs/job_2024-05-19-21-13-15-058677-64911", +"run_stats": { + "average_memory": 0, + "max_memory": 241584, + "elapsed_time": 18.833, + "system_time": 1.114, + "user_time": 16.166, + "total_time": 17.28, + "cores": 40 + } +``` + +#### Calculation Inputs + +`atomate2` stores a record of not just the outputs of a calculation, but also the inputs, and any modifications that +were made to those inputs. + +The `input` key contains the **summarized final input data for the calculation**. It's schema is defined by +[InputDoc](https://github.com/materialsproject/emmet/blob/efc0b22ff835b11b1c825cbc93d4bbebf84a0d91/emmet-core/emmet/core/tasks.py#L175) +and includes everything one needs to specify a VASP calculation: a `Structure` object, INCAR settings, +Pseudopotential specifications, etc. Let's just look at the top-level keys of our `input` section: + +```json +"input": { + "structure": {...}, + "parameters": {...}, + "pseudo_potentials": {...}, + "potcar_spec": [ ... ], + "xc_override": "PS", + "is_lasph": true, + "is_hubbard": false, + "hubbards": {}, + "magnetic_moments": [ + 0.6, + 0.6 + ] + }, +``` + +#### Calculation Outputs + +Much like `input`, the `output` key is populated by a nested schema called [`OutputDoc`](https://github.com/materialsproject/emmet/blob/efc0b22ff835b11b1c825cbc93d4bbebf84a0d91/emmet-core/emmet/core/tasks.py#L97). +`OutputDoc` captures key summary information about the final result of a VASP calculation, including the `Structure`, final energy, `energy_per_atom`, and `bandgap`. In our example: + +```json +"output": { + "structure": {...}, + "density": 2.3223723738160613, + "energy": -11.48288783, + "forces": [ + [ + 0, + 0, + 0 + ], + [ + 0, + 0, + 0 + ] + ], + "stress": [ + [ + 0.04088458, + 0, + 0 + ], + [ + 0, + 0.04088458, + 0 + ], + [ + 0, + 0, + 0.04088458 + ] + ], + "energy_per_atom": -5.741443915, + "bandgap": 0.45999999999999996 + }, + +``` + + + + +#### `custodian` and `orig_inputs` + +There is also a key called `orig_inputs` that contains the **original inputs given by the user** when the calculation +was launched. It is possible for `input` and `orig_inputs` to differ if `custodian` is invoked to apply some adjustment +to the calculation settings. `orig_inputs` is retained to provide 100% transparent provenance in such cases. + +In addition, there is a `custodian` key that will capture and list any corrections or changes made by `custodian` +during the calculation, as well as additional metadata. In our case, the `custodian.corrections` list is empty, +which means that no modifications or restarts were made. + +```json +"custodian": [ + { + "corrections": [], + "job": { + "@module": "custodian.vasp.jobs", + "@class": "VaspJob", + "@version": "2024.4.18", + "vasp_cmd": [ + "srun", + "/scratch/gpfs/ab6989/MPScanRelaxSet/atomate2/vasp_std" + ], + "output_file": "vasp.out", + "stderr_file": "std_err.txt", + "suffix": "", + "final": true, + "backup": true, + "auto_npar": false, + "auto_gamma": true, + "settings_override": null, + "gamma_vasp_cmd": [ + "vasp_gam" + ], + "copy_magmom": false, + "auto_continue": false + } + } + ], +``` + +#### `calcs_reversed` + +Most Task Documents also contain a key called `calcs_reversed` which, as the name implies, contains calculation inputs +and outputs **in reverse order**. These are stored as a `list`, so index `[0]` corresponds to the last (most recent) +calculation, whereas index `[-1]` is the first calculation. Each element in the list contains `input`, `output`, `dir_name`, +and other keys that give a complete specification of that calculation step. + +In this example, there is only one element in `calcs_reversed`, because we just did a one-step `Job`. However, more +complex workflows that contain multiple individual calculations would have an entry for each step. + +```json +"calcs_reversed": [ + { "dir_name": "/scratch/gpfs/.../job_2024-05-19-21-13-15-058677-64911", + "vasp_version": "6.4.2", + "has_vasp_completed": "successful", + "input": { + "incar": {...}, + "kpoints": {...}, + "nkpoints": 20, + "potcar": ["PAW_PBE"], + "potcar_spec": [ ... ], + "potcar_type": ["PAW_PBE"], + "parameters": {...}, + "lattice_rec": {...}, + "structure": {...}, + "is_hubbard": false, + "hubbards": {} + }, + "output": { + "energy": -11.48288783, + "energy_per_atom": -5.741443915, + "structure": { .... }, + "efermi": 5.96853235, + "is_metal": false, + "bandgap": 0.45999999999999996, + "cbm": 6.2225, + "vbm": 5.7625, + "is_gap_direct": false, + "direct_gap": 2.5146000000000006, + "transition": "(0.000,0.000,0.000)-(0.429,0.429,-0.000)", + "mag_density": -1.2698159551931228e-7, + "epsilon_static": null, + "epsilon_static_wolfe": null, + "epsilon_ionic": null, + "frequency_dependent_dielectric": { + "real": null, + "imaginary": null, + "energy": null + }, + "ionic_steps": [ ... ], + "force_constants": null, + "normalmode_frequencies": null, + "normalmode_eigenvals": null, + "normalmode_eigenvecs": null, + "elph_displaced_structures": { + "temperatures": null, + "structures": null + }, + "dos_properties": {...}, + "run_stats": { + "average_memory": 0, + "max_memory": 241584, + "elapsed_time": 18.833, + "system_time": 1.114, + "user_time": 16.166, + "total_time": 17.28, + "cores": 40 + } + }, + "completed_at": "2024-05-19 17:13:34.897366", + "task_name": "standard", + "output_file_paths": { + "chgcar": "CHGCAR", + "aeccar0": "AECCAR0", + "aeccar1": "AECCAR1", + "aeccar2": "AECCAR2" + }, + "bader": null, + "ddec6": null, + "run_type": "PBESol", + "task_type": "Structure Optimization", + "calc_type": "PBESol Structure Optimization" + }, + ] +``` + +There is some redundancy in the information stored in `input`, `output`, and `calcs_reversed`, but this is by design. +`input` and `output` capture summary information about the first and last steps of the `Job`, whereas +`calcs_reversed` records practically every detail of all the intermediate steps. + + +```{note} +The `TaskDoc` `calcs_reversed` section is designed to capture **all the information that can be obtained from a VASP OUTCAR** +(or `vasprun.xml`). Therefore, if you query your output data from the `atomate2` database, you should not need to +manually look up anything from the OUTCAR. Chances are very good that the information is available somewhere in the +`TaskDoc`. For example: + - you can get the electronic energy of the last SCF iteration (index `[-1]`) of the first +ionic step (index `[0]`) in `calcs_reversed[0].output.ionic_steps[0].electronic_steps[0].e_fr_energy`. + - you can retrieve any INCAR parameter, such as ENCUT, from `calcs_reversed[0].input.incar["ENCUT"]` +``` + +## `emmet` + +### Materials Project and Community document models + +Most document models used by `atomate2` "live" in a separate package called [`emmet`](https://github.com/materialsproject/emmet) +(or more specifically, `emmet-core`), which is installed by default as a dependency of `atomate2`. In general, +mature document models that are used in the Materials Project website or database are developed in `emmet`, +whereas some document models that are more niche or are in earlier stages of development may exist +in `atomate2` itself. + +Here is a partial listing of the codes and calculation types currently supported in `emmet-core`: + +Software: + - VASP + - Q-Chem + - FEFF + - OpenMM + +Calculation Types: + - Structure optimization + - Static / single point energy + - Frequency + - Band structure + - Elastic tensor + +### Code-agnostic document models for analysis + +So far, we have introduced Document Models as a way of parsing input and output data from a specific +calculation software (VASP). However, document models are also useful for capturing data from +"downstream" analysis that is not dependent on the specific code used to generate the data. +Hence, **many document models in `emmet-core` are agnostic or independent of the specific software +used in the initial calculation**. + +To take a simple example, `emmet-core` contains a schema called [`ElectronicStructureSummaryData`](https://github.com/materialsproject/emmet/blob/main/emmet-core/emmet/core/electronic_structure.py#L86) that stores +the `band_gap`, conduction band minimum (`cbm`), valence band maximum (`vbm`), and Fermi level (`e_fermi`): + +```python +class ElectronicStructureBaseData(BaseModel): + task_id: MPID = Field( + ..., + description="The source calculation (task) ID for the electronic structure data. " + "This has the same form as a Materials Project ID.", + ) + + band_gap: float = Field(..., description="Band gap energy in eV.") + + cbm: Optional[Union[float, Dict]] = Field( + None, description="Conduction band minimum data." + ) + + vbm: Optional[Union[float, Dict]] = Field( + None, description="Valence band maximum data." + ) + + efermi: Optional[float] = Field(None, description="Fermi energy in eV.") +``` + +Clearly, this simple document model could be used to store output from any periodic DFT code. + +### Builders + +`emmet-core` also defines `Builder` classes, which take raw calculation results (e.g., the `TaskDoc`) +from our example, perform some analysis or transformation, and then create new document models +in additional `Store`. This paradigm makes it possible to construct automated data processing +pipelines, and is the basis for how the Materials Project database. For more about how builders and +stores work together, see the [`maggma` documentation](https://materialsproject.github.io/maggma/concepts/). + +## Conclusion + +In this tutorial, you learned that `atomate2` uses schema or "Document Models" (based on `pydantic.BaseModel`) +to structure and validate output data. You examined the typical structure of a calculation output by +looking at the schema for a VASP structure optimization (`TaskDoc`). You also learned that `emmet` serves as +a "library" of mature document models used by the Materials Project. + +At this point, you might: + +* Explore how to query results from the docs store: [maggma tutorial](https://materialsproject.github.io/maggma/getting_started/query_101/) +* Learn how to create automatic processing pipelines with `Builder` [maggma tutorial](https://materialsproject.github.io/maggma/getting_started/simple_builder/). + diff --git a/docs/user/install.md b/docs/user/install.md index 781dd2d9fd..bc2c14800e 100644 --- a/docs/user/install.md +++ b/docs/user/install.md @@ -456,6 +456,8 @@ See the following pages for more information on the topics we covered here: - To see how to run and customize the existing Workflows in atomate2, try the [](running_workflows) tutorial (suggested next step). +- To learn more about `TaskDocument` and how `atomate2` organizes output data, review + the [Introduction to task documents, schemas, and emmet](docs_schemas_emmet.md) tutorial. - To see how to manage and execute many workflows at once, try the [](atomate2_fireWorks) tutorial. diff --git a/docs/user/running-workflows.md b/docs/user/running-workflows.md index 28322571af..149b74ea7a 100644 --- a/docs/user/running-workflows.md +++ b/docs/user/running-workflows.md @@ -170,5 +170,6 @@ At this point, you might: * Learn how to chain workflows together: [](connecting_vasp_jobs). * Learn how to customise VASP input settings: [](modifying_input_sets). +* Learn more about [Document Models and Schemas](docs_schemas_emmet) * Configure atomate2 with FireWorks to manage and execute many workflows at once: [](atomate2_fireWorks). 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Model" or schema * Inspect a `TaskDoc` generated by `atomate2` ### Prerequisites @@ -34,13 +34,13 @@ dictionaries, where each dictionary represents one `Job`. Each dictionary in the so "document" refers to the output data from a single `Job `. To facilitate automated processing and analysis, it's important that every document follows a -consistent format. That's where schemas (also called "Document Models") come in. +consistent format. That's where schemas (also called "Document Models") come in. ## Document Models ### Schema for `Job` -Document models define a specific format (i.e., structure and data types) for a given `Job` or calculation. +Document models define a specific format (i.e., structure and data types) for a given `Job` or calculation. `atomate2` uses [pydantic](https://docs.pydantic.dev/latest/) to define these schemas. If you'd like to learn more about `pydantic`, we suggest reading [this introduction]( https://realpython.com/python-pydantic/#getting-familiar-with-pydantic). In brief, every Document Model in `atomate2` @@ -128,7 +128,7 @@ at the top-level keys of `output` from the `JobStoreDocument` in the previous se } ``` -Even though we are looking at an example for a VASP calculations, **`atomate2` uses hierarchichal or modular Document +Even though we are looking at an example for a VASP calculations, **`atomate2` uses hierarchical or modular Document Models wherever possible**. Therefore, the Task Documents generated for other calculation types have the same general structure (e.g., `inputs`, `outputs`, structure metadata, `custodian`, `orig_inputs`, `calcs_reversed`, etc.) @@ -291,7 +291,7 @@ There is also a key called `orig_inputs` that contains the **original inputs giv was launched. It is possible for `input` and `orig_inputs` to differ if `custodian` is invoked to apply some adjustment to the calculation settings. `orig_inputs` is retained to provide 100% transparent provenance in such cases. -In addition, there is a `custodian` key that will capture and list any corrections or changes made by `custodian` +In addition, there is a `custodian` key that will capture and list any corrections or changes made by `custodian` during the calculation, as well as additional metadata. In our case, the `custodian.corrections` list is empty, which means that no modifications or restarts were made. @@ -422,7 +422,7 @@ The `TaskDoc` `calcs_reversed` section is designed to capture **all the informat manually look up anything from the OUTCAR. Chances are very good that the information is available somewhere in the `TaskDoc`. For example: - you can get the electronic energy of the last SCF iteration (index `[-1]`) of the first -ionic step (index `[0]`) in `calcs_reversed[0].output.ionic_steps[0].electronic_steps[0].e_fr_energy`. +ionic step (index `[0]`) in `calcs_reversed[0].output.ionic_steps[0].electronic_steps[0].e_fr_energy`. - you can retrieve any INCAR parameter, such as ENCUT, from `calcs_reversed[0].input.incar["ENCUT"]` ``` @@ -435,7 +435,7 @@ Most document models used by `atomate2` "live" in a separate package called [`em mature document models that are used in the Materials Project website or database are developed in `emmet`, whereas some document models that are more niche or are in earlier stages of development may exist in `atomate2` itself. - + Here is a partial listing of the codes and calculation types currently supported in `emmet-core`: Software: @@ -457,7 +457,7 @@ So far, we have introduced Document Models as a way of parsing input and output calculation software (VASP). However, document models are also useful for capturing data from "downstream" analysis that is not dependent on the specific code used to generate the data. Hence, **many document models in `emmet-core` are agnostic or independent of the specific software -used in the initial calculation**. +used in the initial calculation**. To take a simple example, `emmet-core` contains a schema called [`ElectronicStructureSummaryData`](https://github.com/materialsproject/emmet/blob/main/emmet-core/emmet/core/electronic_structure.py#L86) that stores the `band_gap`, conduction band minimum (`cbm`), valence band maximum (`vbm`), and Fermi level (`e_fermi`): @@ -504,4 +504,3 @@ At this point, you might: * Explore how to query results from the docs store: [maggma tutorial](https://materialsproject.github.io/maggma/getting_started/query_101/) * Learn how to create automatic processing pipelines with `Builder` [maggma tutorial](https://materialsproject.github.io/maggma/getting_started/simple_builder/). -