Over the past decades, continual advancement of computational power has led to the prevalence of automation across science, industry, and society, whereby digital solutions were developed algorithmically to exploit technical knowledge of a problem. However, we are currently entering a critical period of computational innovation, particularly in materials science. Non-intuitive materials acceleration for societal solutions (MASS) can now potentially be uncovered by computational machine learning, artificial intelligence, and other self-driving methods for materials discovery. Herein, we invite the ideas for the next generation of materials acceleration platforms (MAPs). Help us reach critical mass of ideas and start a new materials reaction.

在过去的几十年中，计算能力的不断进步导致自动化在科学、工业和社会中的普及，数字解决方案是通过算法开发的，以利用问题的技术知识。然而，我们目前正进入计算创新的关键时期，特别是在材料科学领域。用于社会解决方案 (MASS) 的非直观材料加速现在可以通过计算机机器学习、人工智能和其他用于材料发现的自动驾驶方法来揭示。在这里，我们邀请下一代材料加速平台（MAP）的想法。帮助我们达到临界质量并开始新的材料反应。