AutoMat: Accelerated Computational Electrochemical systems Discovery
Carnegie Mellon University（卡内基梅隆大学）；Citrine Informatics；Northeastern University（东北大学）；Massachusetts Institute of Technology（麻省理工学院）；Julia Computing；Deep Forest Sciences
Emil Annevelink, Rachel Kurchin, Eric Muckley, Lance Kavalsky, Vinay I. Hegde, Valentin Sulzer, Shang Zhu, Jiankun Pu, David Farina, Matthew Johnson, Dhairya Ga… 展开作者
Emil Annevelink, Rachel Kurchin, Eric Muckley, Lance Kavalsky, Vinay I. Hegde, Valentin Sulzer, Shang Zhu, Jiankun Pu, David Farina, Matthew Johnson, Dhairya Gandhi, Adarsh Dave, Hongyi Lin, Alan Edelman, Bharath Ramsundar, James Saal, Christopher Rackauckas, Viral Shah, Bryce Meredig, Venkatasubramanian Viswanathan
备注：
v1-3:4 pages, 1 figure, accepted to NeurIPS Climate Change and AI Workshop 2020, updating acknowledgements and citations v4: substantially updated content and author list, accepted to MRS Bulletin
Large-scale electrification is vital to addressing the climate crisis, but several scientific and technological challenges remain to fully electrify both the chemical industry and transportation. In both of these areas, new electrochemical materials will be critical, but their development currently relies heavily on human-time-intensive experimental trial and error and computationally expensive first-principles, meso-scale and continuum simulations. We present an automated workflow, AutoMat, that accelerates these computational steps by introducing both automated input generation and management of simulations across scales from first principles to continuum device modeling. Furthermore, we show how to seamlessly integrate multi-fidelity predictions such as machine learning surrogates or automated robotic experiments "in-the-loop". The automated framework is implemented with design space search techniques to dramatically accelerate the overall materials discovery pipeline by implicitly learning design features that optimize device performance across several metrics. We discuss the benefits of AutoMat using examples in electrocatalysis and energy storage and highlight lessons learned.
34 Examples of LLM Applications in Materials Science and Chemistry: Towards Automation, Assistants, Agents, and Accelerated Scientific Discovery
ETH Zurich（苏黎世联邦理工学院）；University of Missouri-Columbia（密苏里大学哥伦比亚分校）；McGill University（麦吉尔大学）；University of Houston（休斯顿大学）；Duke University（杜克大学）；University of California at Berkeley（加州大学伯克利分校）；Lawrence Berkeley National Laboratory（伯克利国家实验室）；University of Southern California（南加州大学）；Humboldt University of Berlin（柏林洪堡大学）；Université catholique de Louvain（列日天主大学）；Matgenix SRL（Matgenix公司）；University of California, Los Angeles（加州大学洛杉矶分校）；Friedrich-Schiller-Universität Jena（耶拿弗里德里希-施莱尔大学）；Federal Institute of Materials Research and Testing (BAM)（材料研究与测试联邦机构）；Argonne National Laboratory（阿贡国家实验室）；University of Toronto（多伦多大学）；EPFL（洛桑联邦理工学院）；Massachusetts Institute of Technology（麻省理工学院）；University of Amsterdam（阿姆斯特丹大学）；Acceleration（加速机构）
Accelerated automated screening of viscous graphene suspensions with various surfactants for optimal electrical conductivity
功能复合薄膜在柔性和/或电子器件、电信和多功能新兴涂层中具有广泛的应用。快速筛选其特性是一项具有挑战性的任务，特别是多个组件定义目标特性。在这项工作中，我们提出了一种用于加速自动筛选粘性石墨烯悬浮液以获得最佳导电性的歧管。使用Opentrons OT 2机器人自动移液器，我们通过在羟丙基甲基纤维素水溶液中制备288个石墨烯悬浮液样品来测试3种最具工业意义的表面活性剂- PVP、SDS和T80。通过我们定制的电动4点探针测量设置和计算机视觉算法，我们随后使用定制测量了每个样品的电导率，并确定PVP基样品实现了最高性能，峰值为10.4 mS/cm。实验过程的自动化使我们能够使用机器人进行大部分实验，而人类研究人员的参与保持在最低限度。整个实验在不到18小时内完成，其中只有3个涉及人类。
Developing and Validating a High-Throughput Robotic System for the Accelerated Development of Porous Membranes
Department of Materials Science and Engineering, University of Toronto（材料科学与工程系，多伦多大学）；Department of Chemical Engineering, University of Toronto（化学工程系，多伦多大学）；Department of Engineering Science, University of Toronto（工程科学系，多伦多大学）；Department of Mechanical Engineering, University of Waterloo（机械工程系，滑铁卢大学）
Materials Acceleration Platform for Electrochemistry: a Platform for Autonomous Electrochemistry
CanmetMATERIALS, Natural Resources Canada（加拿大自然资源部材料研究中心）；Department of Materials Science and Engineering, University of Toronto（多伦多大学材料科学与工程系）；Material Measurement Laboratory, National Institute of Standards and Technology（美国国家标准与技术研究院材料测量实验室）
NIMS-OS: An automation software to implement a closed loop between artificial intelligence and robotic experiments in materials science
Center for Basic Research on Materials（材料基础研究中心）；National Institute for Materials Science（国家材料科学研究所）；Graduate School of Frontier Sciences, The University of Tokyo（东京大学前沿科学研究生院）；Research Center for Energy and Environmental Materials(GREEN)（能源与环境材料研究中心（GREEN））；Center for Advanced Battery Collaboration, Research Center for Energy and Environmental Materials (GREEN)（先进电池协作中心，能源与环境材料研究中心（GREEN））
