# Physical Computing for Materials Acceleration Platforms

arXiv: https://arxiv.org/abs/2208.08566 · HTML: https://mlsanity.com/paper/2208.08566/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 48 references that are in the map.
- First posted on arXiv 17 Aug 2022.

## Builds on (most cited first, 9 of 9)

- [All-Optical Machine Learning Using Diffractive Deep Neural Networks](https://mlsanity.com/paper/1804.08711/) (2018)
- [The Hardware Lottery](https://mlsanity.com/paper/2009.06489/) (2020)
- [Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty](https://mlsanity.com/paper/2011.07586/) (2020)
- [Deep physical neural networks enabled by a backpropagation algorithm for arbitrary physical systems](https://mlsanity.com/paper/2104.13386/) (2021)
- [A Differentiable Physics Engine for Deep Learning in Robotics](https://mlsanity.com/paper/1611.01652/) (2016)
- [Simulation Intelligence: Towards a New Generation of Scientific Methods](https://mlsanity.com/paper/2112.03235/) (2021)
- [Wave Physics as an Analog Recurrent Neural Network](https://mlsanity.com/paper/1904.12831/) (2019)
- [Inverse design of two-dimensional materials with invertible neural networks](https://mlsanity.com/paper/2106.03013/) (2021)
- [Reality-assisted evolution of soft robots through large-scale physical experimentation: a review](https://mlsanity.com/paper/2009.13960/) (2020)

## Built on by (most cited first, 1 of 1)

- [Energy-based learning algorithms for analog computing: a comparative study](https://mlsanity.com/paper/2312.15103/) (2023)

## Abstract (arXiv)

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.