Mission:

Increasingly, industry leaders wish to leverage data-driven models (AI and ML) and well-calibrated simulations to lower R&D costs by replacing expensive laboratory measurements, particularly in the materials design and manufacturing business. The vision is good, but the reasoning is often flawed.  Building data-driven models and eliminating experiments are often mutually exclusive ideas in scientific discovery. The strength of data-driven models is directly proportional to the amount and quality of data that they are trained on, and experiments are where the data are produced. 

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Thus, this vision of simulation and AI driven exploration and design relies on a significant change to the status-quo in many laboratories.  Of course there are shining exceptions, but in our exposure to many industrial chemistry and bio-sciences labs, we have consistently seen opportunities to increase the lab throughput by 10, 100, or even 1000x with similarly lower costs per sample. However, when suggested to an industry scientist, these ideas are often perceived as too expensive or impractical. Some of this is a skills gap. Some of it is an imagination gap. And, much of it is an examples gap. Every lab that these scientists have ever seen is designed with significant manual work to formulate samples that are then shipped out to multiple buildings/ laboratories for manual characterization.  

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The goal of this project is to develop tools, methodologies, and examples that can bridge this gap for scientists so that they embrace instead of be wary of these efforts.  There are numerous mental and design shifts needed to achieve these goals, and so the primary goal of the First Congress is to identify and bring together key participants from academia, industry, and government to discuss experimental needs across these sectors, share information on the state of the art, and determine a path for how this community can collaborate, be governed, and produce meaningful results towards shared goals going forward.