Physical Computing for Materials Acceleration Platforms
- URL: http://arxiv.org/abs/2208.08566v1
- Date: Wed, 17 Aug 2022 23:03:54 GMT
- Title: Physical Computing for Materials Acceleration Platforms
- Authors: Erik Peterson, Alexander Lavin
- Abstract summary: 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 outline a simulation-based MAP program to design computers that use physics itself to solve optimization problems.
We expect to introduce a new era of innovative collaboration between materials researchers and computer scientists.
- Score: 81.09376948478891
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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