[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-hackingmaterials--matminer":3,"tool-hackingmaterials--matminer":64},[4,17,27,35,43,56],{"id":5,"name":6,"github_repo":7,"description_zh":8,"stars":9,"difficulty_score":10,"last_commit_at":11,"category_tags":12,"status":16},3808,"stable-diffusion-webui","AUTOMATIC1111\u002Fstable-diffusion-webui","stable-diffusion-webui 是一个基于 Gradio 构建的网页版操作界面，旨在让用户能够轻松地在本地运行和使用强大的 Stable Diffusion 图像生成模型。它解决了原始模型依赖命令行、操作门槛高且功能分散的痛点，将复杂的 AI 绘图流程整合进一个直观易用的图形化平台。\n\n无论是希望快速上手的普通创作者、需要精细控制画面细节的设计师，还是想要深入探索模型潜力的开发者与研究人员，都能从中获益。其核心亮点在于极高的功能丰富度：不仅支持文生图、图生图、局部重绘（Inpainting）和外绘（Outpainting）等基础模式，还独创了注意力机制调整、提示词矩阵、负向提示词以及“高清修复”等高级功能。此外，它内置了 GFPGAN 和 CodeFormer 等人脸修复工具，支持多种神经网络放大算法，并允许用户通过插件系统无限扩展能力。即使是显存有限的设备，stable-diffusion-webui 也提供了相应的优化选项，让高质量的 AI 艺术创作变得触手可及。",162132,3,"2026-04-05T11:01:52",[13,14,15],"开发框架","图像","Agent","ready",{"id":18,"name":19,"github_repo":20,"description_zh":21,"stars":22,"difficulty_score":23,"last_commit_at":24,"category_tags":25,"status":16},1381,"everything-claude-code","affaan-m\u002Feverything-claude-code","everything-claude-code 是一套专为 AI 编程助手（如 Claude Code、Codex、Cursor 等）打造的高性能优化系统。它不仅仅是一组配置文件，而是一个经过长期实战打磨的完整框架，旨在解决 AI 代理在实际开发中面临的效率低下、记忆丢失、安全隐患及缺乏持续学习能力等核心痛点。\n\n通过引入技能模块化、直觉增强、记忆持久化机制以及内置的安全扫描功能，everything-claude-code 能显著提升 AI 在复杂任务中的表现，帮助开发者构建更稳定、更智能的生产级 AI 代理。其独特的“研究优先”开发理念和针对 Token 消耗的优化策略，使得模型响应更快、成本更低，同时有效防御潜在的攻击向量。\n\n这套工具特别适合软件开发者、AI 研究人员以及希望深度定制 AI 工作流的技术团队使用。无论您是在构建大型代码库，还是需要 AI 协助进行安全审计与自动化测试，everything-claude-code 都能提供强大的底层支持。作为一个曾荣获 Anthropic 黑客大奖的开源项目，它融合了多语言支持与丰富的实战钩子（hooks），让 AI 真正成长为懂上",138956,2,"2026-04-05T11:33:21",[13,15,26],"语言模型",{"id":28,"name":29,"github_repo":30,"description_zh":31,"stars":32,"difficulty_score":23,"last_commit_at":33,"category_tags":34,"status":16},2271,"ComfyUI","Comfy-Org\u002FComfyUI","ComfyUI 是一款功能强大且高度模块化的视觉 AI 引擎，专为设计和执行复杂的 Stable Diffusion 图像生成流程而打造。它摒弃了传统的代码编写模式，采用直观的节点式流程图界面，让用户通过连接不同的功能模块即可构建个性化的生成管线。\n\n这一设计巧妙解决了高级 AI 绘图工作流配置复杂、灵活性不足的痛点。用户无需具备编程背景，也能自由组合模型、调整参数并实时预览效果，轻松实现从基础文生图到多步骤高清修复等各类复杂任务。ComfyUI 拥有极佳的兼容性，不仅支持 Windows、macOS 和 Linux 全平台，还广泛适配 NVIDIA、AMD、Intel 及苹果 Silicon 等多种硬件架构，并率先支持 SDXL、Flux、SD3 等前沿模型。\n\n无论是希望深入探索算法潜力的研究人员和开发者，还是追求极致创作自由度的设计师与资深 AI 绘画爱好者，ComfyUI 都能提供强大的支持。其独特的模块化架构允许社区不断扩展新功能，使其成为当前最灵活、生态最丰富的开源扩散模型工具之一，帮助用户将创意高效转化为现实。",107662,"2026-04-03T11:11:01",[13,14,15],{"id":36,"name":37,"github_repo":38,"description_zh":39,"stars":40,"difficulty_score":23,"last_commit_at":41,"category_tags":42,"status":16},3704,"NextChat","ChatGPTNextWeb\u002FNextChat","NextChat 是一款轻量且极速的 AI 助手，旨在为用户提供流畅、跨平台的大模型交互体验。它完美解决了用户在多设备间切换时难以保持对话连续性，以及面对众多 AI 模型不知如何统一管理的痛点。无论是日常办公、学习辅助还是创意激发，NextChat 都能让用户随时随地通过网页、iOS、Android、Windows、MacOS 或 Linux 端无缝接入智能服务。\n\n这款工具非常适合普通用户、学生、职场人士以及需要私有化部署的企业团队使用。对于开发者而言，它也提供了便捷的自托管方案，支持一键部署到 Vercel 或 Zeabur 等平台。\n\nNextChat 的核心亮点在于其广泛的模型兼容性，原生支持 Claude、DeepSeek、GPT-4 及 Gemini Pro 等主流大模型，让用户在一个界面即可自由切换不同 AI 能力。此外，它还率先支持 MCP（Model Context Protocol）协议，增强了上下文处理能力。针对企业用户，NextChat 提供专业版解决方案，具备品牌定制、细粒度权限控制、内部知识库整合及安全审计等功能，满足公司对数据隐私和个性化管理的高标准要求。",87618,"2026-04-05T07:20:52",[13,26],{"id":44,"name":45,"github_repo":46,"description_zh":47,"stars":48,"difficulty_score":23,"last_commit_at":49,"category_tags":50,"status":16},2268,"ML-For-Beginners","microsoft\u002FML-For-Beginners","ML-For-Beginners 是由微软推出的一套系统化机器学习入门课程，旨在帮助零基础用户轻松掌握经典机器学习知识。这套课程将学习路径规划为 12 周，包含 26 节精炼课程和 52 道配套测验，内容涵盖从基础概念到实际应用的完整流程，有效解决了初学者面对庞大知识体系时无从下手、缺乏结构化指导的痛点。\n\n无论是希望转型的开发者、需要补充算法背景的研究人员，还是对人工智能充满好奇的普通爱好者，都能从中受益。课程不仅提供了清晰的理论讲解，还强调动手实践，让用户在循序渐进中建立扎实的技能基础。其独特的亮点在于强大的多语言支持，通过自动化机制提供了包括简体中文在内的 50 多种语言版本，极大地降低了全球不同背景用户的学习门槛。此外，项目采用开源协作模式，社区活跃且内容持续更新，确保学习者能获取前沿且准确的技术资讯。如果你正寻找一条清晰、友好且专业的机器学习入门之路，ML-For-Beginners 将是理想的起点。",84991,"2026-04-05T10:45:23",[14,51,52,53,15,54,26,13,55],"数据工具","视频","插件","其他","音频",{"id":57,"name":58,"github_repo":59,"description_zh":60,"stars":61,"difficulty_score":10,"last_commit_at":62,"category_tags":63,"status":16},3128,"ragflow","infiniflow\u002Fragflow","RAGFlow 是一款领先的开源检索增强生成（RAG）引擎，旨在为大语言模型构建更精准、可靠的上下文层。它巧妙地将前沿的 RAG 技术与智能体（Agent）能力相结合，不仅支持从各类文档中高效提取知识，还能让模型基于这些知识进行逻辑推理和任务执行。\n\n在大模型应用中，幻觉问题和知识滞后是常见痛点。RAGFlow 通过深度解析复杂文档结构（如表格、图表及混合排版），显著提升了信息检索的准确度，从而有效减少模型“胡编乱造”的现象，确保回答既有据可依又具备时效性。其内置的智能体机制更进一步，使系统不仅能回答问题，还能自主规划步骤解决复杂问题。\n\n这款工具特别适合开发者、企业技术团队以及 AI 研究人员使用。无论是希望快速搭建私有知识库问答系统，还是致力于探索大模型在垂直领域落地的创新者，都能从中受益。RAGFlow 提供了可视化的工作流编排界面和灵活的 API 接口，既降低了非算法背景用户的上手门槛，也满足了专业开发者对系统深度定制的需求。作为基于 Apache 2.0 协议开源的项目，它正成为连接通用大模型与行业专有知识之间的重要桥梁。",77062,"2026-04-04T04:44:48",[15,14,13,26,54],{"id":65,"github_repo":66,"name":67,"description_en":68,"description_zh":69,"ai_summary_zh":69,"readme_en":70,"readme_zh":71,"quickstart_zh":72,"use_case_zh":73,"hero_image_url":74,"owner_login":75,"owner_name":76,"owner_avatar_url":77,"owner_bio":78,"owner_company":79,"owner_location":79,"owner_email":79,"owner_twitter":79,"owner_website":80,"owner_url":81,"languages":82,"stars":99,"forks":100,"last_commit_at":101,"license":102,"difficulty_score":23,"env_os":103,"env_gpu":103,"env_ram":103,"env_deps":104,"category_tags":108,"github_topics":109,"view_count":10,"oss_zip_url":79,"oss_zip_packed_at":79,"status":16,"created_at":114,"updated_at":115,"faqs":116,"releases":142},947,"hackingmaterials\u002Fmatminer","matminer","Data mining for materials science","matminer 是一个专为材料科学领域设计的数据挖掘库，帮助研究人员轻松处理、分析和利用材料数据。它通过整合丰富的数据集和强大的数据处理方法，解决了材料科学研究中数据获取繁琐、特征提取复杂以及数据分析门槛高的问题。无论是从公开数据库中获取材料信息，还是将材料属性转化为机器学习可用的特征，matminer 都能大幅简化这些流程。\n\n这款工具非常适合从事材料科学的研究人员和开发者使用，尤其是那些希望通过数据驱动方法加速新材料发现或优化现有材料性能的用户。对于希望将机器学习技术引入材料研究的团队来说，matminer 提供了开箱即用的功能，例如数据集加载、特征工程和数据预处理等，极大地降低了技术门槛。\n\nmatminer 的独特亮点在于其与社区紧密合作，支持多种公开材料数据集，并提供了清晰的引用机制，确保研究透明性和对原作者的尊重。此外，它还兼容 Python 3.11+，并与相关工具如 automatminer 和 matbench 无缝衔接，进一步扩展了其功能范围。如果你正在寻找一个易用且功能强大的材料数据挖掘助手，matminer 将是一个值得信赖的选择。","# \u003Cimg alt=\"matminer\" src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fhackingmaterials_matminer_readme_9fec2a110360.png\" width=\"300\">\n\n[![testing](https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Factions\u002Fworkflows\u002Ftest.yml\u002Fbadge.svg?branch=main)](https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Factions?query=workflow%3Atesting+branch%3Amain)\n![python](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FPython-3.11+-blue.svg?logo=python&logoColor=white)\n\nmatminer is a library for performing data mining in the field of materials science.\n\n- **[Website (including documentation)](https:\u002F\u002Fhackingmaterials.github.io\u002Fmatminer\u002F)**\n- **[Examples Repository](https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer_examples)**\n- **[Help\u002FSupport Forum](https:\u002F\u002Fmatsci.org\u002Fc\u002Fmatminer\u002F16)**\n- **[Source Repository](https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer)**\n\nmatminer supports Python 3.11+.\n\n#### Related packages:\n\n- If you like matminer, you might also try [automatminer](https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fautomatminer).\n- If you are interested in furthering development of datasets in matminer, you may be interested in [matbench](https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatbench).\n- If you are looking for figrecipes, it is now in its [own repo](https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Ffigrecipes).\n\n\n#### Citation\n\nIf you find matminer useful, please encourage its development by citing the following paper in your research:\n```\nWard, L., Dunn, A., Faghaninia, A., Zimmermann, N. E. R., Bajaj, S., Wang, Q.,\nMontoya, J. H., Chen, J., Bystrom, K., Dylla, M., Chard, K., Asta, M., Persson,\nK., Snyder, G. J., Foster, I., Jain, A., Matminer: An open source toolkit for\nmaterials data mining. Comput. Mater. Sci. 152, 60-69 (2018).\n```\n\nMatminer helps users apply methods and data sets developed by the community. Please also cite the original sources, as this will add clarity to your article and credit the original authors:\n\n- If you use one or more **datasets** accessed through matminer, check the dataset metadata info for relevant citations on the original datasets.\n- If you use one or more **data retrieval methods**, check ``citations()`` method of the data retrieval class. This method will provide a list of BibTeX-formatted citations for that featurizer, making it easy to keep track of and cite the original publications.\n- If you use one or more **featurizers**, please take advantage of the ```citations()``` function present for every featurizer in matminer.\n","# \u003Cimg alt=\"matminer\" src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fhackingmaterials_matminer_readme_9fec2a110360.png\" width=\"300\">\n\n[![testing](https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Factions\u002Fworkflows\u002Ftest.yml\u002Fbadge.svg?branch=main)](https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Factions?query=workflow%3Atesting+branch%3Amain)\n![python](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FPython-3.11+-blue.svg?logo=python&logoColor=white)\n\nmatminer 是一个用于在材料科学领域进行数据挖掘的库。\n\n- **[网站（包括文档）](https:\u002F\u002Fhackingmaterials.github.io\u002Fmatminer\u002F)**\n- **[示例仓库](https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer_examples)**\n- **[帮助\u002F支持论坛](https:\u002F\u002Fmatsci.org\u002Fc\u002Fmatminer\u002F16)**\n- **[源代码仓库](https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer)**\n\nmatminer 支持 Python 3.11+。\n\n#### 相关软件包：\n\n- 如果你喜欢 matminer，你也可以试试 [automatminer](https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fautomatminer)。\n- 如果你对进一步开发 matminer 中的数据集感兴趣，可以看看 [matbench](https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatbench)。\n- 如果你在寻找 figrecipes，它现在已迁移到 [自己的仓库](https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Ffigrecipes)。\n\n\n#### 引用\n\n如果你觉得 matminer 有用，请通过引用以下论文来鼓励其发展：\n```\nWard, L., Dunn, A., Faghaninia, A., Zimmermann, N. E. R., Bajaj, S., Wang, Q.,\nMontoya, J. H., Chen, J., Bystrom, K., Dylla, M., Chard, K., Asta, M., Persson,\nK., Snyder, G. J., Foster, I., Jain, A., Matminer: An open source toolkit for\nmaterials data mining. Comput. Mater. Sci. 152, 60-69 (2018).\n```\n\nMatminer 帮助用户应用社区开发的方法和数据集。请同时引用原始来源，这将为你的文章提供更清晰的背景并表彰原作者的贡献：\n\n- 如果你使用了通过 matminer 访问的一个或多个**数据集**，请检查数据集的元数据信息以获取相关引用，了解原始数据集的出处。\n- 如果你使用了一个或多个**数据检索方法**，请检查数据检索类的 ``citations()`` 方法。该方法会提供该特征化工具相关的 BibTeX 格式引用列表，方便你跟踪和引用原始出版物。\n- 如果你使用了一个或多个**特征化工具**，请利用 matminer 中每个特征化工具提供的 ```citations()``` 函数。","# matminer 快速上手指南\n\nmatminer 是一个用于材料科学领域数据挖掘的 Python 库，支持 Python 3.11+。\n\n## 环境准备\n\n### 系统要求\n- 操作系统：Windows、macOS 或 Linux\n- Python 版本：Python 3.11 及以上\n\n### 前置依赖\n确保已安装以下工具：\n- [pip](https:\u002F\u002Fpip.pypa.io\u002Fen\u002Fstable\u002Finstallation\u002F)\n- （可选）国内用户建议配置 [PyPI 镜像源](https:\u002F\u002Fmirrors.aliyun.com\u002Fpypi\u002Fsimple\u002F) 以加速安装。\n\n## 安装步骤\n\n推荐使用 pip 安装 matminer：\n\n```bash\npip install matminer\n```\n\n如果需要从源码安装，请克隆 GitHub 仓库并运行以下命令：\n\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer.git\ncd matminer\npip install .\n```\n\n国内用户可以使用阿里云镜像加速安装：\n\n```bash\npip install matminer -i https:\u002F\u002Fmirrors.aliyun.com\u002Fpypi\u002Fsimple\u002F\n```\n\n## 基本使用\n\n以下是一个简单的示例，展示如何使用 matminer 加载数据集并进行特征提取：\n\n```python\nfrom matminer.datasets import load_dataset\nfrom matminer.featurizers.structure import DensityFeatures\n\n# 加载示例数据集\ndf = load_dataset(\"elastic_tensor_2015\")\n\n# 查看数据集前几行\nprint(df.head())\n\n# 使用 DensityFeatures 提取结构特征\nfeaturizer = DensityFeatures()\ndf = featurizer.featurize_dataframe(df, col_id=\"structure\")\n\n# 查看添加的特征列\nprint(df.head())\n```\n\n更多示例代码和教程，请参考 [matminer 示例库](https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer_examples)。\n\n---\n\n如果在研究中使用 matminer，请引用以下论文：\n\n```\nWard, L., Dunn, A., Faghaninia, A., Zimmermann, N. E. R., Bajaj, S., Wang, Q.,\nMontoya, J. H., Chen, J., Bystrom, K., Dylla, M., Chard, K., Asta, M., Persson,\nK., Snyder, G. J., Foster, I., Jain, A., Matminer: An open source toolkit for\nmaterials data mining. Comput. Mater. Sci. 152, 60-69 (2018).\n```","一位材料科学家正在研究新型合金的性能，希望通过数据挖掘找到影响合金强度的关键因素。\n\n### 没有 matminer 时\n- 数据收集耗时耗力：需要手动从不同来源查找和整理材料数据集，效率低下且容易出错。  \n- 特征工程复杂：必须自己编写代码提取材料特征（如晶体结构、化学成分等），过程繁琐且难以保证准确性。  \n- 缺乏标准化流程：每次分析都需要重新设计方法，导致结果难以复现或与其他研究对比。  \n- 学习成本高：需要熟悉多种工具和数据库接口，增加了项目启动的时间成本。  \n- 难以追踪引用：使用第三方数据或方法时，容易遗漏必要的文献引用，影响研究可信度。\n\n### 使用 matminer 后\n- 快速获取数据：通过内置的数据集接口，一键加载高质量的公开材料数据集，节省大量时间。  \n- 自动化特征提取：利用丰富的 featurizer 工具，轻松生成描述材料特性的特征变量，减少手动编码工作量。  \n- 标准化分析流程：提供统一的 API 和文档支持，确保研究过程透明且易于复现。  \n- 降低学习门槛：清晰的文档和示例代码帮助快速上手，无需深入了解底层实现细节即可高效使用。  \n- 自动化引用管理：通过 `citations()` 方法，自动生成所需引用的文献列表，避免遗漏重要参考文献。\n\nmatminer 让材料科学研究者专注于核心问题，而不是被数据处理和特征工程拖慢脚步。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fhackingmaterials_matminer_9fec2a11.png","hackingmaterials","Hacking Materials Research Group","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002Fhackingmaterials_387c1061.png","",null,"https:\u002F\u002Fhackingmaterials.lbl.gov","https:\u002F\u002Fgithub.com\u002Fhackingmaterials",[83,87,91,95],{"name":84,"color":85,"percentage":86},"HTML","#e34c26",69.6,{"name":88,"color":89,"percentage":90},"Python","#3572A5",30.1,{"name":92,"color":93,"percentage":94},"Makefile","#427819",0.2,{"name":96,"color":97,"percentage":98},"CSS","#663399",0.1,578,208,"2026-03-23T07:11:00","NOASSERTION","未说明",{"notes":105,"python":106,"dependencies":107},"建议访问官网文档获取更多使用和配置信息，首次运行可能需要下载额外数据集或模型文件。","3.11+",[],[51,13,54],[110,111,112,67,113],"materials-science","data-mining","machine-learning","condensed-matter","2026-03-27T02:49:30.150509","2026-04-06T05:17:56.852973",[117,122,127,132,137],{"id":118,"question_zh":119,"answer_zh":120,"source_url":121},4180,"matminer 是否兼容 pandas v2？","在 #912 和 #929 中已解决了大部分兼容性问题，当前版本已支持 pandas v2。如果遇到其他问题，可以在该 Issue 下反馈。建议确保 numpy 和 pandas 的版本均为最新以避免潜在冲突。","https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fissues\u002F915",{"id":123,"question_zh":124,"answer_zh":125,"source_url":126},4177,"BaseFeaturizer() 对某些类型的特征提取器支持不佳，如何改进？","可以通过让 BaseFeaturizer 继承 scikit-learn 的 BaseEstimator 来改进，这样可以更好地与 Pipelines 集成。但需要注意的是，初始化方法中不能使用 *args 和 **kwargs（详见 [scikit-learn 源码](https:\u002F\u002Fgithub.com\u002Fscikit-learn\u002Fscikit-learn\u002Fblob\u002Fa24c8b46\u002Fsklearn\u002Fbase.py#L176)）。此问题已在 PR #156 中解决。","https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fissues\u002F135",{"id":128,"question_zh":129,"answer_zh":130,"source_url":131},4178,"无法克隆 matminer 仓库，出现 LFS 错误怎么办？","该问题通常是由于 Git LFS 文件缺失导致的。建议检查网络连接，或者尝试重新安装 Git LFS。此外，如果数据集过大，可以考虑将数据集移至单独的存储服务中，以减少仓库体积。","https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fissues\u002F36",{"id":133,"question_zh":134,"answer_zh":135,"source_url":136},4179,"FunctionFeaturizer 在并行特征提取时为何会出错？","这是一个与操作系统相关的 bug，可能与集合排序的实现方式有关。该问题已在分支 https:\u002F\u002Fgithub.com\u002Fmontoyjh\u002FMatMiner\u002Ftree\u002Ffunction_featurize 中修复，建议更新到最新版本以解决问题。","https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fissues\u002F295",{"id":138,"question_zh":139,"answer_zh":140,"source_url":141},4181,"MPContrib 电子输运数据集是否可以通过 matminer 加载？","目前 MPContrib 数据集尚未集成到 matminer 中。如果需要加载类似数据集，可以使用 `load_dataset()` 方法加载其他可用数据集。例如：\n```python\nfrom matminer.datasets import load_dataset\n\ndf = load_dataset(\"matbench_phonons\")\n```\n建议关注后续更新以获取更多数据集支持。","https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fissues\u002F606",[143,148,153,158,163,168,173,178,183,187,191,195,199,203,207,211,215,219,223,227],{"id":144,"version":145,"summary_zh":146,"released_at":147},103585,"v0.10.0","## What's Changed\r\n* \u003Cb>BREAKING\u003C\u002Fb>: `impute_nan=True` by default, and no warnings are thrown when `impute_nan=False`.\r\n* Migrate setup.py to pyproject.toml by @DanielYang59 in https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fpull\u002F950\r\n* ChemEnvSiteFingerprint.from_preset() removal of not-implemented CEs by @kaueltzen in https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fpull\u002F948\r\n* General housekeeping by @esoteric-ephemera in https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fpull\u002F955\r\n* xfail tests pending rewrite to current MP API client by @esoteric-ephemera in https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fpull\u002F956\r\n* Automated dependency upgrades by @tschaume in https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fpull\u002F947\r\n* update deployment to py3.12 and status badges by @esoteric-ephemera in https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fpull\u002F957\r\n* Migrate to src layout by @esoteric-ephemera in https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fpull\u002F958\r\n\r\n## New Contributors\r\n* @kaueltzen made their first contribution in https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fpull\u002F948\r\n\r\n**Full Changelog**: https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fcompare\u002Fv0.9.3...v0.10.0\r\n\r\n## What's Changed\r\n* Migrate setup.py to pyproject.toml by @DanielYang59 in https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fpull\u002F950\r\n* ChemEnvSiteFingerprint.from_preset() removal of not-implemented CEs by @kaueltzen in https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fpull\u002F948\r\n* General housekeeping by @esoteric-ephemera in https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fpull\u002F955\r\n* xfail tests pending rewrite to current MP API client by @esoteric-ephemera in https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fpull\u002F956\r\n* Automated dependency upgrades by @tschaume in https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fpull\u002F947\r\n* update deployment to py3.12 and status badges by @esoteric-ephemera in https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fpull\u002F957\r\n* Migrate to src layout by @esoteric-ephemera in https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fpull\u002F958\r\n\r\n## New Contributors\r\n* @kaueltzen made their first contribution in https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fpull\u002F948\r\n\r\n**Full Changelog**: https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fcompare\u002Fv0.9.3...v0.10.0","2026-01-22T21:09:42",{"id":149,"version":150,"summary_zh":151,"released_at":152},103586,"v0.10.0rc0","## What's Changed\r\n* Migrate setup.py to pyproject.toml by @DanielYang59 in https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fpull\u002F950\r\n* ChemEnvSiteFingerprint.from_preset() removal of not-implemented CEs by @kaueltzen in https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fpull\u002F948\r\n* General housekeeping by @esoteric-ephemera in https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fpull\u002F955\r\n* xfail tests pending rewrite to current MP API client by @esoteric-ephemera in https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fpull\u002F956\r\n* Automated dependency upgrades by @tschaume in https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fpull\u002F947\r\n* update deployment to py3.12 and status badges by @esoteric-ephemera in https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fpull\u002F957\r\n* Migrate to src layout by @esoteric-ephemera in https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fpull\u002F958\r\n\r\n## New Contributors\r\n* @kaueltzen made their first contribution in https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fpull\u002F948\r\n\r\n**Full Changelog**: https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fcompare\u002Fv0.9.3...v0.10.0rc0","2026-01-22T20:34:49",{"id":154,"version":155,"summary_zh":156,"released_at":157},103587,"v0.9.3","This release contains the usual dependency compatibility updates, as well as:\r\n\r\n- new `impute_nan` setting to many featurizers, that allows missing features to be imputed automatically as the mean of the remainder of that column. This is particularly helpful when e.g., trying to use a feature that uses elemental data on a dataset where maybe one row contains an element not present in the dataset (#892)\r\n- added a featurizer based on elemental pseudo-inverse of tabulated optical\u002Ftransport data (#892)\r\n\r\n## What's Changed\r\n* Optical and transport data as elemental pseudo-inverse contributions by @gbrunin in https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fpull\u002F892\r\n* Do not run test PyPI build on forks by @ml-evs in https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fpull\u002F933\r\n* Added missing `impute_nan` for `WenAlloys` and removed unnecessary warning by @gbrunin in https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fpull\u002F937\r\n* Suppress pymatgen element data warnings when imputing by @ml-evs in https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fpull\u002F934\r\n* Disable release creation workflow on forks by @gbrunin in https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fpull\u002F940\r\n* Bug fixes in pseudo inverse computations by @gbrunin in https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fpull\u002F942\r\n* Disable test PyPI build by @ml-evs in https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fpull\u002F941\r\n* Automatic deps upgrades by @ml-evs in https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fpull\u002F944\r\n* spglib 2.5 compatibility by @esoteric-ephemera in https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fpull\u002F943\r\n* Support NumPy 2 by @DanielYang59 in https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fpull\u002F949\r\n\r\n## New Contributors\r\n* @gbrunin made their first contribution in https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fpull\u002F892\r\n* @esoteric-ephemera made their first contribution in https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fpull\u002F943\r\n\r\n**Full Changelog**: https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fcompare\u002Fv0.9.2...v0.9.3","2024-10-06T12:44:05",{"id":159,"version":160,"summary_zh":161,"released_at":162},103588,"v0.9.2","This release simply removes the upper bound on pandas v2, which, since the changes implemented in v0.9.1, is no longer necessary.\r\n\r\n## What's Changed\r\n* Attempt to release the pandas pin by @ml-evs in https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fpull\u002F929\r\n* Automated dependency upgrades by @tschaume in https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fpull\u002F930\r\n\r\n\r\n**Full Changelog**: https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fcompare\u002Fv0.9.1...v0.9.2","2024-03-27T14:45:47",{"id":164,"version":165,"summary_zh":166,"released_at":167},103589,"v0.9.1","This release contains several compatibility fixes across tests and dependencies, including support for the latest numpy and pymatgen versions.\r\nDependencies have been tightened to make sure that any future incompatibilities do not render this package entirely unusable.\r\nUsers are encouraged to install the pinned dependencies from `requirements\u002F*.txt` if they run into any issues.\r\n\r\n## What's Changed\r\n* Linting and test updates by @ml-evs in https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fpull\u002F912\r\n* Prevent warning in fingerprint.py during yaml.safe_load by @afonari in https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fpull\u002F914\r\n* Fix maximum element failure by @JaGeo in https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fpull\u002F921\r\n* Begin unpicking dependency issues by @ml-evs in https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fpull\u002F922\r\n* Correct valence electron numbers by @DanielYang59 in https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fpull\u002F917\r\n* Reintroduce pandas pin and update code for numpy 1.24+ by @ml-evs in https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fpull\u002F925\r\n* More dependency upgrades: support for latest pymatgen and numpy by @ml-evs in https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fpull\u002F928\r\n\r\n## New Contributors\r\n* @afonari made their first contribution in https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fpull\u002F914\r\n* @JaGeo made their first contribution in https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fpull\u002F921\r\n* @DanielYang59 made their first contribution in https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fpull\u002F917\r\n\r\n**Full Changelog**: https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fcompare\u002Fv0.9.0...v0.9.1","2024-03-26T12:52:14",{"id":169,"version":170,"summary_zh":171,"released_at":172},103590,"v0.9.0","## What's Changed\r\n* Add hint how to avoid OOM errors to `BaseFeaturizer.set_n_jobs()` doc str by @janosh in https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fpull\u002F894\r\n* Add ability to customize `BaseFeaturizer` `pbar` by passing dict by @janosh in https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fpull\u002F893\r\n* Assign `__init__` arg to instance attribute to fix sklearn base `__repr__` by @ml-evs in https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fpull\u002F896\r\n* Fixes for CI and tests by @ml-evs in https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fpull\u002F901\r\n* Optimize `BandCenter` and `IntersticeDistribution` featurizers by @ml-evs in https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fpull\u002F897\r\n* Prequel to dependency upgrades by @ml-evs in https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fpull\u002F903\r\n* Skip the thermo tests rather than crash if MP API is missing by @ml-evs in https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fpull\u002F905\r\n* Defer `MPRester` import to allow it to act more like an optional dep by @ml-evs in https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fpull\u002F906\r\n\r\n## New Contributors\r\n* @ml-evs made their first contribution in https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fpull\u002F896\r\n\r\n**Full Changelog**: https:\u002F\u002Fgithub.com\u002Fhackingmaterials\u002Fmatminer\u002Fcompare\u002Fv0.8.0...v0.9.0","2023-06-27T15:36:52",{"id":174,"version":175,"summary_zh":176,"released_at":177},103591,"v0.8.0","Version 0.8.0\n\n### Commits\n\n- [97a0bc6a] trying again ... (#890)\n- [6a34b631] rm publishPackage workflow_dispatch\n- [2ef3a560] update docs\n- [c67904f1] yet another description change (#889)\n- [5ef7d3cc] try explicitly checking out main\n- [48e652de] minor description change (#888)\n- [1fdd6983] update docs\n- [d6392009] release workflow_dispatch\n- [0c53b605] set git user\n- [d1298264] add description (#887)\n- [0d65adc8] Automated dependency upgrades (#886)\n- [aabebc3b] add sphinx dep\n- [f4f450ad] work on release through github actions (#885)\n- [a0ae13d8] also pull tags for scm version\n- [78ce6d6b] merge docs build into release\n- [34c569e9] skip tests for now\n- [c7625801] only run tests on changes in matminer subdir\n- [08f1dae7] fix action typo\n- [cb8634d9] add docs and release to github actions\n- [e59bb209] echo tests\n- [ca7fa1ea] no-deps\n- [ff38fea5] Automated dependency upgrades (#884)\n- [bf1d37c1] cache-dependency-path 2\n- [fc5d9b85] dependabot not needed anymore\n- [7f6a1c1a] cache-dependency-path\n- [b770dac1] update all test actions\n- [10b3d9ab] move jsonschema to mpds extra\n- [26b0e37d] update github actions\n- [5d9c661a] include dev dependencies\n- [61c5f3e7] add requirements files (pip-compile)\n- [3f742f3f] setup.py: explicitly list dependencies\n- [a0f754a0] delete old requirements files\n- [8f1742e5] add upgrade-dependencies action\n- [7f8520b9] reformat to make linter happy\n- [e8bacc12] reformat\n- [0f6f62c6] allow for automatic retries of dataset downloads, plus alter tests (attempt to fix hash problem that fails tests randomly)\n- [4a86c0a2] Merge pull request #839 from hackingmaterials\u002Fdependabot\u002Fpip\u002Fujson-5.4.0\n- [66b2163d] format fix\n- [618f6f71] fix linting\n- [ead30fea] Merge pull request #809 from jacksund\u002Fmain\n- [886524ab] Merge pull request #841 from janosh\u002Fcrystal-nn-fingerprint-docs\n- [2dd87615] explain meaning of 'cn' | 'ops' in CrystalNNFingerprint.from_preset doc string\n- [19e73191] Bump ujson from 5.2.0 to 5.4.0\n- [b3e29548] fix psitestats tests\n- [d471b57e] Merge branch 'hackingmaterials:main' into main\n","2022-11-11T00:12:35",{"id":179,"version":180,"summary_zh":181,"released_at":182},103592,"v0.7.8","manual base release for new github actions","2022-11-10T22:21:06",{"id":184,"version":185,"summary_zh":79,"released_at":186},103593,"v0.7.6-release","2022-01-19T16:34:09",{"id":188,"version":189,"summary_zh":79,"released_at":190},103594,"v0.7.6","2022-01-19T16:26:55",{"id":192,"version":193,"summary_zh":79,"released_at":194},103595,"v0.7.4","2021-08-03T15:49:18",{"id":196,"version":197,"summary_zh":79,"released_at":198},103596,"v0.7.3","2021-06-29T18:03:07",{"id":200,"version":201,"summary_zh":79,"released_at":202},103597,"v0.7.2","2021-06-08T22:01:02",{"id":204,"version":205,"summary_zh":79,"released_at":206},103598,"v0.7.0","2021-06-08T01:05:03",{"id":208,"version":209,"summary_zh":79,"released_at":210},103599,"v0.6.5","2021-02-17T17:11:13",{"id":212,"version":213,"summary_zh":79,"released_at":214},103600,"v0.6.4","2020-10-27T23:37:04",{"id":216,"version":217,"summary_zh":79,"released_at":218},103601,"v0.6.3","2020-05-04T02:52:19",{"id":220,"version":221,"summary_zh":79,"released_at":222},103602,"v0.6.2","2019-10-14T16:28:10",{"id":224,"version":225,"summary_zh":79,"released_at":226},103603,"v0.6.0","2019-08-30T16:48:12",{"id":228,"version":229,"summary_zh":79,"released_at":230},103604,"v0.5.9","2019-08-09T22:35:52"]