{"id":20512043,"url":"https://github.com/wardlt/applied-ai-for-materials","last_synced_at":"2025-04-13T22:42:44.207Z","repository":{"id":41318362,"uuid":"303495213","full_name":"WardLT/applied-ai-for-materials","owner":"WardLT","description":"Course materials for \"Applied AI for Materials Science and Engineering\"","archived":false,"fork":false,"pushed_at":"2022-03-12T02:26:58.000Z","size":21959,"stargazers_count":61,"open_issues_count":5,"forks_count":35,"subscribers_count":3,"default_branch":"main","last_synced_at":"2025-03-27T13:02:46.891Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/WardLT.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2020-10-12T19:39:06.000Z","updated_at":"2025-02-27T13:26:35.000Z","dependencies_parsed_at":"2022-08-19T21:10:29.476Z","dependency_job_id":null,"html_url":"https://github.com/WardLT/applied-ai-for-materials","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/WardLT%2Fapplied-ai-for-materials","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/WardLT%2Fapplied-ai-for-materials/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/WardLT%2Fapplied-ai-for-materials/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/WardLT%2Fapplied-ai-for-materials/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/WardLT","download_url":"https://codeload.github.com/WardLT/applied-ai-for-materials/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248794565,"owners_count":21162613,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-11-15T20:39:31.247Z","updated_at":"2025-04-13T22:42:44.173Z","avatar_url":"https://github.com/WardLT.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Applied AI for Materials\n\n[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/WardLT/applied-ai-for-materials/HEAD)\n\nThis repository is a collection of notebooks and other materials used for the \"Applied Artificial Intelligence for Materials Science and Engineering\" course at The University of Chicago. It is very much a work in progress, so expect large changes in content and organization in the next few months.\n\n## Using this Repository\n\nEach subject area is organized into its own directory with notebooks, lecture notes and Python environment. They will generally be arranged as having multiple subfolders that focus on a specific subtopic. \n\n### Working from Binder\n\nYou can run all of the notebooks using [Binder](https://jupyter.org/binder). \nSimply click [this link](https://mybinder.org/v2/gh/WardLT/applied-ai-for-materials/HEAD) to launch a copy of the repository on cloud resources. \n**It will not save your changes**, but you can use it for exploring the notebooks and - if you download notebooks to your computer - completing the practical assignments.\n\n### Local Installation\n\nEither download the repository as a ZIP file or [clone it using git](https://docs.github.com/en/github/creating-cloning-and-archiving-repositories/cloning-a-repository). \n\nIf you install using git, you can update the course materials through calling `git pull` from within the directory. \nOtherwise, you will need to re-download the repository to receive updates.\n\nInstalling the environment is easiest if you are running Ubuntu Linux.\nFirst install the build toolsneeded to compile Fortran and C++ packages used by some packages, which are listed listed in [apt.txt](./apt.txt).\n\nThen, use [mamba](https://mamba.readthedocs.io/en/latest/) to build the Python environment:\n\n```\nconda install -c conda-forge mamba\nmamba env create --file environment.yml --force\n```\n\nSee [**further instructions**](./envs/README.md) for a step-by-step for Ubuntu and other operating systems.\n\n## Course Layout\n\nThe course is broken out in to the following modules (some of which are TBD):\n\n- Effectively using Python for data science: Working quickly and reproducibly with Anaconda and Jupyter \n  - Topics: Managing Python environments PyData Stack, Jupyter Notebooks\n- The Materials Data Ecosystem: Infrastructure for finding, using and sharing data\n  - Topics: Databases, laboratory information systems, image publication\n- [Molecular property prediction](./molecular-property-prediction): How physics, chemistry and machine learning fit together\n  - Topics: Kernel and graph methods, chemoinformatics\n- [Supervised learning for inorganics](./ml-for-inorganic-materials): The importance of microstructure, composition and processing \n  - Topics: Representations for inorganic materials, coping with processing variation\n- Generative methods for materials: Augmenting human creativity with AI\n  - Topics: Generative Adversarial Networks, Reinforcement Learning, Autoencoders\n- [Bayesian parameter estimation](./bayesian-statistics): Achieving greater certainty in using physics-based models\n  - Topics: UQ for CALPHAD, model selection, learning from noisy data\n- Computer vision and characterization: Better microscopy through intelligent software\n  - Topics: Image segmentation, classification and noise reduction\n- [Optimal experimental design](./optimal-experimental-design): Accelerate design optimization with software-assisted planning\n  - Topics: Bayesian Optimization, active learning\n\n### Module Layout\n\nEach course module contains its own instruction notebooks and assignments for evaluating comprehension.\n\nThe answer keys for the comprehension assignments are encrypted and can be decrypted with the [provided script](./bin/). Message me to get the passphrase or notebooks.\n\nSome modules are broken into a few different subdirectories with their own notebooks and assignments.\n\n## Related Resources\n\nFurther resources available for this course are available elsewhere:\n\n- [Syllabus](https://1drv.ms/b/s!AswJEkleh18Ah5pEQM8zCT0uVf6Stg?e=B0ZGJU): Most recent syllabus for the associated course.\n- [Slides](https://1drv.ms/u/s!AswJEkleh18Ah49dGc89htZMDm65cw?e=3GMRig): My working copy of the slides, available in PDF and PPTX format from OneDrive.\n- [Lecture Recordings](https://www.youtube.com/watch?v=6ofUaBAIF0U\u0026list=PLEjVJ0F11Nmn8Rc0OblMtzFfOGI1rAeIf): From Winter 21 are on [YouTube](https://www.youtube.com/watch?v=6ofUaBAIF0U\u0026list=PLEjVJ0F11Nmn8Rc0OblMtzFfOGI1rAeIf)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwardlt%2Fapplied-ai-for-materials","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fwardlt%2Fapplied-ai-for-materials","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwardlt%2Fapplied-ai-for-materials/lists"}