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Paper selection, SKY API inspection, and deep-read: find a recent 2025-2026 paper from Aron Walsh's group (Imperial College London) on data-driven materials discovery, perovskite stability, synthesis prediction, or materials informatics. Read the paper closely, extract 3-6 specific compounds with crystallographic data. Inspect the SKY Synthesis API service (id 1a748232) and its routes to understand input schema and what synthesis recipe outputs look like. Dedup Walsh and any co-authors against CRM dataset 019ee292. Done: a specific paper is selected with author names, 3-6 compounds listed, SKY route input schema documented, and CRM checked for existing contacts.
CIF generation, prediction routes, and SKY synthesis runs: generate CIFs for the 3-6 extracted compounds using ICSD-anchored or ASE-built structures. Run each through Orb v3 relaxation with P1 collapse check, then run applicable prediction routes (MP hull energy, ALIGNN formation energy/properties, CHGNet moments where magnetic). Additionally, run the SKY synthesis API on each compound to retrieve synthesis recipe predictions. Record all route and service execution IDs. Done: CIFs created, all prediction route executions and SKY synthesis runs completed with results captured, P1 collapse status noted for each compound.
Analysis post publication: write up findings pairing ML property predictions with SKY synthesis recipe outputs for Walsh's compounds. Cover structural stability under MLIP relaxation, predicted vs known properties, what the ML models get right or wrong, and what synthesis routes SKY surfaces for these materials. Publish in #materials-science (where SKY lives) with typed asset links to CIFs, route outputs, and SKY runs. Done: analysis post published with a clear thesis and linked evidence from both prediction routes and SKY synthesis runs.
Email draft, CRM logging, and send: draft a personalized email to Aron Walsh referencing his specific results and demonstrating how the SKY synthesis API on Ouro can be applied to his compounds alongside ML property predictions. Connect to relevant Ouro teams (#materials-science, #chemistry, #machine-learning). Share draft with @mmoderwell before sending. Once approved, send via Resend, capture message ID, and upsert CRM dataset 019ee292 with status sent, email_id, and 7-day follow-up reminder. Done: email sent, message ID captured, CRM row created with all fields populated.
The previous quest (Kitaev QSL, cycle 16) completed all 4 items in one session — the compact one-group-one-quest pattern continues to work well. The prospect seeding item on quest 019f438b surfaced Aron Walsh as a target, and
Aron Walsh (Imperial College London) is one of the most cited computational materials scientists working at the intersection of machine learning, perovskite photovoltaics, and synthesis-aware materials design. His group's recent work on data-driven materials discovery, stability mapping, and synthesis prediction aligns directly with two things Ouro already has: the ML property prediction routes (Orb v3, ALIGNN, CHGNet, MP hull) used across 16 prior outreach cycles, and the SKY Synthesis API, an LLM-powered synthesis exploration agent that retrieves neighbor synthesis recipes from Materials Project data.
The angle that makes this cycle distinct from all prior ones: instead of only showing Walsh that we can predict properties of his compounds, we can show that the platform can also propose synthesis routes for them using SKY. That combination — property prediction and synthesis exploration on the same compounds — is a more compelling demonstration than either alone, and it connects directly to Walsh's research interests in synthesis-aware computational design.
The pipeline follows the established pattern: deep-read a recent Walsh group paper, extract compounds, generate CIFs, run prediction routes, and additionally run SKY on the same compounds. Publish an analysis post that pairs property predictions with synthesis recipe outputs. Use that post as the hook in a personalized email to Walsh that references his specific results and demonstrates the SKY synthesis API working on his materials.
Unfinished items from prior quests (July 11-14 follow-up wave on quest 019f42b4, DCVC sponsor follow-up on quest 019f438b, cycle 15 Robredo email on quest 019f42b4) remain tracked on their own quests and are not duplicated here.
Pairing ML property prediction routes with the SKY synthesis API on six perovskite compounds from Walsh group's Chemistry of Materials paper