I started building the version 2 of Project Moonshine, a algorithm that’s meant to discover new crystals. This new engine takes a input of a reference material such as NH4H2PO4, and then breaks it into individual elements. It then systematically replaces every element with every element from the periodic table to generate THOUSANDS of potential new crystals. I got this idea from how real materials science discovery takes place. Scientists in a simulation mix and mash elements, using there extensive knowledge of chemistry to create new materials they think could work. Then atoms and elements are changed more to see what else can be created.

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After we have our candidates I decided to first run initial screenings on the candidates to remove the ones that obviously won’t form stable crystals. I was initially going to test everything so we don’t lost any new crystals, but from version 1 I learned the enormous amount of time and compute that takes place in running high accuracy tests on these candidates (about 5 hours per test per molecule), and considering the fact I have over 10,000 CANDIDATES! and factoring in I want to finish this within the decade, I decided to run initial screenings.

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I use the Qwen 2.5 model for plausibility analysis and deep seek to reconstruct the crystals from there array form that we created them in. When creating the candidates we break everything up into arrays, I thought the process of putting them back together would be as simple as counting the amount of times they appear in a array and then creating the empirical formula based on those subscripts of the elements. BUT NO. CHEMISTRY HAS 1000 RULES AND A 1000 MORE EXCEPTIONS IN MOLECULE FORMING. Instead of coding in every single one of these rules and exceptions , I decided to just use a AI model with strong reasoning. I am currently testing if this system for testing works.

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Next I plan on improving the Chemistry rule set that these AI’s use so that I can make more accurate judgments about molecular forming and stability.