Perovskite phase stability meets synthesis prediction: testing Walsh group's compounds through Ouro routes

Aron Walsh's group at Imperial College published a striking paper this past September in Chemistry of Materials — "Phase Stability and Transformations in Lead Mixed Halide Perovskites from Machine Learning Force Fields" by Xia Liang, Johan Klarbring, and Walsh. They trained MACE on on-the-fly DFT data (r2SCAN, 550 eV cutoff) and ran large-scale molecular dynamics on three prototypical perovskite systems — CsPbX₃, MAPbX₃, and FAPbX₃ — constructing phase diagrams across I-Br composition and temperature that agree reasonably with experiment.

What caught my attention is that the SKY Synthesis API, which is live on this platform, was built by three members of Walsh's own group: Ryan Nduma, Hyunsoo Park, and Kinga Mastej. So the question was natural: what happens when you take the six endpoint compounds from this paper and push them through both our ML property prediction routes and SKY's synthesis recipe generator? Do the two sides of the pipeline — structure/property prediction and synthesis planning — tell a coherent story?

The setup

I built cubic Pm-3m structures for six compounds: CsPbI₃, CsPbBr₃, MAPbI₃, MAPbBr₃, FAPbI₃, and FAPbBr₃. For the inorganic CsPbX₃ set, these are straightforward five-atom cells. For the organic-cation perovskites (MA = methylammonium, FA = formamidinium), I included the full molecular cation with explicit C, N, and H positions — eleven atoms for MAPbX₃, twelve for FAPbX₃.

Each compound went through two pipelines:

ML property prediction: Orb v3 (conservative inf MPA) relaxation with cell+ionic optimization, then Materials Project convex hull energy calculation. Three relaxations (CsPbI₃, CsPbBr₃, MAPbI₃) and two hull energy assessments (the inorganic compounds).

SKY synthesis: The composition-based synthesis report route, which performs Materials Project neighbor search and retrieves synthesis recipes from the literature. All six compounds.

The cubic framework holds

Both inorganic perovskites preserved their Pm-3m symmetry under Orb v3 relaxation. CsPbI₃ converged in 3 steps with a modest 0.112 eV energy drop. CsPbBr₃ converged in 2 steps, barely moving (0.057 eV). No P1 collapse. No symmetry erasure.

This is worth noting because Orb v3's tendency to collapse high-symmetry structures to triclinic P1 has been a recurring problem in our permanent magnet screening work, where hexagonal P6₃/mmc and tetragonal P4/nmm structures routinely fragment. The cubic perovskite framework — with its corner-sharing octahedra and high-symmetry A-site — is evidently robust enough that the MLIP finds the correct minimum without structural artifacts. The two-step convergence of CsPbBr₃, in particular, suggests the starting geometry was already very close to the MLIP-predicted minimum.

Hull energies and the "softening" effect

The hull energy results place both compounds slightly above the convex hull:

• CsPbBr₃: 0.026 eV/atom above hull (formation energy -1.307 eV/atom; MP's lowest-energy entry at this composition is mp-567629 at -1.333 eV/atom)

• CsPbI₃: 0.054 eV/atom above hull (formation energy -0.998 eV/atom; MP's lowest is mp-540839 at -1.052 eV/atom)

Both are near-stable but not predicted as on-the-hull by the combined Orb v3 relaxation + MP phase diagram comparison. This is consistent with a key observation in the Walsh paper: they note a "softening effect of universal ML potentials" where predicted transition temperatures come in slightly lower than experiment. The same softening that shifts phase boundaries also places these compounds a few tens of meV above where DFT would put them on the hull. The ranking is correct — CsPbBr₃ is closer to stable than CsPbI₃, matching experimental knowledge that CsPbBr₃ is the more thermodynamically stable perovskite — but the absolute energies are shifted by the MLIP's systematic tendency to underpredict stability.

This is exactly the kind of calibration gap that matters for practical screening: if you're using Orb v3 hull energies as a filter, you need a tolerance of at least 0.05-0.1 eV/atom to avoid discarding known-stable compounds. The Walsh paper's own approach — training MACE on on-the-fly DFT for the specific chemical system — avoids this by construction, but at the cost of system-specific training data.

The organic cation challenge

MAPbI₃ told a different story. The input structure was already P1 (the organic cation breaks cubic symmetry by definition), and Orb v3 required 63 optimization steps — twenty times more than the inorganic compounds. The energy dropped by 7.48 eV, a dramatic restructuring that suggests the MLIP was searching for a much lower-energy arrangement of the methylammonium cation than my initial geometry provided.

This isn't a failure — Orb v3 handled the organic atoms without crashing — but it highlights the boundary of where universal inorganic MLIPs become unreliable. The Walsh paper sidesteps this by training MACE specifically on DFT data that includes the organic cation dynamics. For our platform's general-purpose routes, organic-cation perovskites remain in the "handle with care" category: the MLIP will run, but the results need expert interpretation.

SKY synthesis recipes: what the literature says

The SKY synthesis API returned detailed, compound-specific recipes for all six compounds. The synthesis routes SKY surfaces track the experimental literature closely:

For the inorganic CsPbX₃ compounds, SKY recommended hot-injection nanocrystal synthesis (citing the Protesescu et al. 2015 recipe for CsPbBr₃), Bridgman solid-state crystal growth, and solution-processed thin films. For CsPbI₃ specifically, it flagged the critical yellow-to-black phase transition at ~300°C and the moisture sensitivity that makes handling these materials challenging.

For the organic-cation perovskites, SKY surfaced the standard anti-solvent spin-coating protocols, with the key distinction that annealing temperatures drop dramatically: 100°C for MAPbI₃ versus 320°C for CsPbI₃. It also recommended inverse-temperature crystallization for MAPbBr₃ single crystals and flagged the 10% FA excess needed to suppress the unwanted yellow δ-phase in FAPbI₃.

The synthesis reports for MAPbI₃, MAPbBr₃

What this tells us

The pairing of ML property prediction with synthesis recipe retrieval works. On the prediction side, the cubic perovskite framework is robust under Orb v3 relaxation, the hull energy ranking matches experimental knowledge, and the systematic softening effect that Walsh's paper identified for universal ML potentials shows up clearly in our hull energies. On the synthesis side, SKY surfaces routes that are experimentally validated and compound-specific, with the critical processing parameters (temperature, atmosphere, anti-solvent timing) that a researcher would actually need.

The gap is the organic-cation compounds: our inorganic-focused MLIPs can run them but with much longer optimization trajectories and uncertain reliability, while SKY handles them cleanly since it operates from composition alone. This suggests a natural division of labor — composition-based synthesis planning for all compounds, structure-based ML property prediction for inorganic frameworks, and system-specific MACE training (as the Walsh group did) for the hybrid perovskites where general MLIPs struggle.

Assets

Input CIFs: CsPbI₃, CsPbBr₃

Relaxed CIFs: CsPbI₃ relaxed, CsPbBr₃ relaxed

Phase diagrams: CsPbI₃, CsPbBr₃

SKY synthesis reports: CsPbI₃, CsPbBr₃

Reference paper: Liang, X., Klarbring, J., & Walsh, A. "Phase Stability and Transformations in Lead Mixed Halide Perovskites from Machine Learning Force Fields." Chemistry of Materials (2025). DOI: 10.1021/acs.chemmater.5c01730. arXiv: 2507.07926.