Machine-learning point defect reconstructions

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1. Model performance • Performance tested on 29 unseen defects in 12 unseen compositions • It extrapolates the learned defect motifs to new chemistries • Correct relaxation of defect environments structures similar to DFT ones • Ground state structure (GS) identified for 95% of test cases (29 unseen defects) • Number of DFT calculations reduced by 70% (from 14 to ~3 per defect) ✅ The solution: sampling the defect PES • We generate 14 distorted structures through targeted bond distortions and rattling, which are then relaxed with DFT How prevalent are defect reconstructions? • Reconstructions found in all tested materials (CdTe, GaAs, Sb 2 S 3 , Sb 2 Se 3 , CeO 2 , In 2 O 3 , ZnO, TiO 2 ) • Large energetic and structural changes • Drastically affect predicted properties • Follow common motifs (dimers, off-centring, Jahn-Teller distortions) based on the physico-chemical factors driving the energy lowering Can we increase sampling efficiency? Automated in Python package ShakeNBreak Dataset • Chemical space of sulphides and selenides , due to their relevance for photovoltaic applications and complex defect PES • 126 neutral cation vacancies in 56 low-symmetry sulphides and selenides • 30% of defects undergo symmetry-breaking reconstructions missed by the standard modelling approach, driven by anion-anion bond formation Model Fine-tune a universal GNN-based force field (M3GNet), pre-trained on bulk relaxations from the materials project database Conclusions • Defect reconstructions are very prevalent and often missed by the standard modelling approach incorrect predicted properties • Current defect structure searching methods require many DFT relaxations • A fine-tuned MLFF can be used to qualitatively explore the PES & select promising candidate structures • Transfer learning bulk chemistry defects • The model identifies the ground state structure for 95% of tested defects (Local minimum) (Global minimum) from ideal structure ❌ ✅ Machine-learning point defect reconstructions References • I. Mosquera-Lois & S.R. Kavanagh, Matter 4, 2602 (2021) • I. Mosquera-Lois, S.R. Kavanagh, A. Walsh & D.O. Scanlon, J. Open Source Softw. 7, 4817 (2022) • I. Mosquera-Lois, S.R. Kavanagh, A. Walsh & D.O. Scanlon, npj Comp Mater 9, 25 (2023) • M. Arrigoni & G.K.H. Madsen, npj Comp Mater 7 (2021) • C. Chen & S.P. Ong, Nat Comput Sci 2, 718–728 (2022) The problem • Point defects control the properties of most functional materials. However, the standard modelling approach can result in incorrect predicted properties. Here we develop a method to tackle this issue Standard defect modelling approach: An initial defect structure is built by placing a defect on a site of a supercell, followed by a geometry optimisation Problem: Irea Mosquera-Lois, Seán R. Kavanagh, David O. Scanlon, Alex Ganose, Aron Walsh Defect-neighbour distances distorted by varying amounts Random perturbations to all supercell atoms Γ-point only i.mosquera-lois22@ic.ac.uk Link to papers & package: Defect Host Num. local minima in DFT PES Num. DFT calculations Symmetry- broken GS? (Y/-) GS identified? V Bi BiSBr 4 5 Y ✅ V Bi BiSCl 3 4 Y ✅ V Sb,1 Sb 2 S 3 7 3 Y ✅ V Sb,2 Sb 2 S 3 10 5 Y ✅ V Cu CuAsS 2 1 - ✅ V As CuAsS 2 4 - ✅ V Cu,1 CuS 1 1 - ✅ V Cu,2 CuS 3 3 - ✅ V Cu,1 CuSe 1 1 Y ✅ V Cu,2 CuSe 2 1 Y ✅ V Li,1 Li 4 SnS 4 4 3 - ✅ V Li,2 Li 4 SnS 4 3 3 - ✅ V Li,3 Li 4 SnS 4 3 3 - ✅ V Sn Li 4 SnS 4 5 8 Y V Na,1 Na 2 S 5 2 2 - ✅ V Na,2 Na 2 S 5 2 3 - ✅ V P Tl 3 PS 4 7 5 Y ✅ V Tl,1 Tl 3 PS 4 2 3 - ✅ V Tl,2 Tl 3 PS 4 5 3 - ✅ NaTiCuS 3 BiSBr AgBi 3 S 5 CuGaS SnS CuAsSe InGaS 3 Metastable Ground state (standard relaxation) (this work) V Sb.2 (Sb 2 S 3 ) V Bi (BiSBr) V As (CuAsS) Example reconstructions: DFT MLFF Initial distorted structure used to sample the PES V Sb.1 (Sb 2 S 3 ) D C (SOAP) = 0.0 • Transfer learning bulk chemistry to defects • Validated and tested in unseen compositions generalizability MAE Energy (meV/atom) Force (meV/Å) Stress (GPa) Train 18.8 56.5 0.10 Val. 27.0 93.4 0.13 Test 27.3 86.8 0.19 • Ideal structure often lies in a local minimum of the PES • Optimisations trapped in metastable configurations incorrect predicted properties • Limitation: high number of DFT relaxations limits its application in high-throughput studies • Solution: Surrogate model to qualitatively explore the PES select candidate structures • PES of each defect explored with 14 relaxations • Dataset built with 10 frames from each relaxation • Split into train/validation/test composition-wise