Howard Chu @ Symas

LLM-Driven Discovery of High-Entropy Catalysts via Retrieval-Augmented Generation

CO2 reduction requires efficient catalysts, yet materials discovery remains bottlenecked by 10-20 year development cycles requiring deep domain expertise. This paper demonstrates how large language models can assist the catalyst discovery process

Data Access: Downloaded from Open Catalyst Project repositories as PyTorch Geometric Data objects and #LMDB files.

https://arxiv.org/html/2603.15712v1

redb is a pure #rust embedded DB inspired by #LMDB but experienced programmers prefer the original. Convenient juxtaposition of these two search results...

Another LMDB: lichen metabolites database. I'm sure there's a lichen subscribe pun hidden in there too

Pro tip: software is written to solve particular problems. But all proprietary software is written to solve the same problem: how to extract maximal profits from consumers.

If it's not open source, it's not worth running.

If you're a software professional, and the projects you work on are proprietary, you're part of the problem, not the solution.

It's kind of astounding that people could get so far into computing that they could create LLMs in the first place, while never encountering the phrase "garbage in, garbage out". Like, how is it even possible to not realize that the system you're building is worse than the sum of its inputs?