A ''technology lottery'' describes a research idea or technology succeeding over others because it is suited to the available software and hardware, not necessarily because it is superior to alternative directions--examples abound, from the synergies of deep learning and GPUs to the disconnect of urban design and autonomous vehicles. The nascent field of Self-Driving Laboratories (SDL), particularly those implemented as Materials Acceleration Platforms (MAPs), is at risk of an analogous pitfall: the next logical step for building MAPs is to take existing lab equipment and workflows and mix in some AI and automation. In this whitepaper, we argue that the same simulation and AI tools that will accelerate the search for new materials, as part of the MAPs research program, also make possible the design of fundamentally new computing mediums. We need not be constrained by existing biases in science, mechatronics, and general-purpose computing, but rather we can pursue new vectors of engineering physics with advances in cyber-physical learning and closed-loop, self-optimizing systems. Here we outline a simulation-based MAP program to design computers that use physics itself to solve optimization problems. Such systems mitigate the hardware-software-substrate-user information losses present in every other class of MAPs and they perfect alignment between computing problems and computing mediums eliminating any technology lottery. We offer concrete steps toward early ''Physical Computing (PC) -MAP'' advances and the longer term cyber-physical R&D which we expect to introduce a new era of innovative collaboration between materials researchers and computer scientists.
翻译:一种“ 技术彩票” 描述一种研究想法或技术, 因为它适合现有的软件和硬件, 并不一定是因为它优于替代方向- 样板, 从深层次学习和GPU的协同效应到城市设计和自主车辆的断裂。 自我驱动实验室(SDL)的新兴领域, 特别是材料加速平台(MAPs), 面临类似的陷阱风险: 建立MAP的下一个合乎逻辑的步骤是采用现有的实验室设备和工作流程, 以及某些AI和自动化系统中的混合。 在这份白皮书中, 我们争辩说, 加速寻找新材料的模拟和AI 工具, 从深层次学习和GPUs的协同作用到城市设计和自主车辆的脱机。 我们不需要受到科学、 中科技和一般目的计算(MAPs) 中现有的偏见的制约, 但我们可以追求新的工程物理载体, 在网络物理学习、闭路路、自我优化系统中的进步。 在这里, 我们提出一个基于模拟的IMAPP( ) 程序, 加速搜索新材料, 作为MAPS研究程序的一部分, 也有可能设计全新的新的计算媒体- 系统,, 从而降低每个计算机的系统。