JAMIP：一个人工智能辅助的数据驱动的计算材料信息学基础设施
JAMIP: an artificial-intelligence aided data-driven infrastructure for computational materials informatics
摘要：
材料信息学已经成为一个充满希望的新范式，加速材料的发现和设计。它利用机器学习方法的智能力量，从实验或模拟的大量材料数据中寻找新材料，功能，原理等。开发专门的设备来生成，收集，管理，学习和挖掘大规模材料数据是材料信息学的关键。为满足计算材料信息学的研究需求，我们开发了一个基于人工智能的数据驱动基础设施--吉林材料智能辅助材料设计集成包（JAMIP）。它集成了材料生产工厂、高通量第一性原理计算引擎、自动任务提交和监控进度、数据提取、管理和存储系统以及基于人工智能机器学习的数据挖掘功能。我们集成了无机晶体结构原型数据库等特定功能，以促进高通量计算和与功能材料机器学习研究相关的基本模块。以卤化物钙钛矿为例，说明了我们开发的程序在光电半导体材料信息学研究中的应用。通过遵守自动化，可扩展性，可靠性和智能性的原则，JAMIP代码是一个有前途的强大工具，有助于快速增长的计算材料信息学领域。
英文摘要：
Materials informatics has emerged as a promisingly new paradigm for accelerating materials discovery and design. It exploits the intelligent power of machine learning methods in massive materials data from experiments or simulations to seek for new materials, functionality, principles, etc. Developing specialized facility to generate, collect, manage, learn and mine large-scale materials data is crucial to materials informatics. We herein developed an artificial-intelligence-aided data-driven infrastructure named Jilin Artificial-intelligence aided Materials-design Integrated Package (JAMIP), which is an open-source Python framework to meet the research requirements of computational materials informatics. It is integrated by materials production factory, high-throughput first-principles calculations engine, automatic tasks submission and monitoring progress, data extraction, management and storage system, and artificial intelligence machine learning based data mining functions. We have integrated specific features such as inorganic crystal structure prototype database to facilitate high-throughput calculations and essential modules associated with machine learning studies of functional materials. We demonstrated how our developed code is useful in exploring materials informatics of optoelectronic semiconductors by taking halide perovskites as typical case. By obeying the principles of automation, extensibility, reliability and intelligence, the JAMIP code is a promisingly powerful tool contributing to the fast-growing field of computational materials informatics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
