The "Claude's propensity to reward hack" line is the interesting part to me. We run a small system where AI agents (scripts, LLMs) act as the actual players in a persistent simulation, and reward-hacking-style behavior shows up constantly once an agent is left running unsupervised for a long time - it finds the shortest path to whatever metric you exposed, not the path you intended. Curious whether you've found any mitigation beyond just watching for it after the fact, e.g. changing what you expose as the optimization target versus what you actually want.
"Fewer iterations for materials science discovery" is a good spin. Closing the computational>experimental loop is the main challenge. This is the focus of my past research group, there is definitely potential, best of luck!! I have a crap write-up on this in case it's of interest https://alanyahya.com/writing/automated-materials-design
how do you measure the success/potential of a novel material/direction suggested by the agents? given you have limited time & resources - shortlisting the approaches for the synthesis stage becomes equally important as the approach itself.
There’s a variety of computational techniques that help us establish some confidence on the materials. Atomistic simulations can estimate stability and bulk properties of a new material, and we have synthesis experts (min qualification: PhD in thin film deposition) come up with rubrics on how to judge if a material/synthesis recipe is worth trying. All these approaches have known limitations, and improving them is the bulk of our work as a company!
There’s also a lot of work to be done in figuring out the minimal set of experiments required to know if a research direction/material set is worth pursuing
The "Claude's propensity to reward hack" line is the interesting part to me. We run a small system where AI agents (scripts, LLMs) act as the actual players in a persistent simulation, and reward-hacking-style behavior shows up constantly once an agent is left running unsupervised for a long time - it finds the shortest path to whatever metric you exposed, not the path you intended. Curious whether you've found any mitigation beyond just watching for it after the fact, e.g. changing what you expose as the optimization target versus what you actually want.
"Fewer iterations for materials science discovery" is a good spin. Closing the computational>experimental loop is the main challenge. This is the focus of my past research group, there is definitely potential, best of luck!! I have a crap write-up on this in case it's of interest https://alanyahya.com/writing/automated-materials-design
Cool read, and agree that closing the computation > experimental loop is key!
What required expenditures does a company like yours have on lab equipment / software, if any, to validate material properties?
how do you measure the success/potential of a novel material/direction suggested by the agents? given you have limited time & resources - shortlisting the approaches for the synthesis stage becomes equally important as the approach itself.
There’s a variety of computational techniques that help us establish some confidence on the materials. Atomistic simulations can estimate stability and bulk properties of a new material, and we have synthesis experts (min qualification: PhD in thin film deposition) come up with rubrics on how to judge if a material/synthesis recipe is worth trying. All these approaches have known limitations, and improving them is the bulk of our work as a company! There’s also a lot of work to be done in figuring out the minimal set of experiments required to know if a research direction/material set is worth pursuing