Accurate Crystal Structure Prediction of New 2D Hybrid Organic Inorganic Perovskites

Low dimensional hybrid organic-inorganic perovskites (HOIPs) represent a promising class of electronically active materials for both light absorption and emission. The design space of HOIPs is extremely large, since a diverse space of organic cations can be combined with different inorganic frameworks. This immense design space allows for tunable electronic and mechanical properties, but also necessitates the development of new tools for in silico high throughput analysis of candidate structures. In this work, we present an accurate, efficient, transferable and widely applicable machine learning interatomic potential (MLIP) for predicting the structure of new 2D HOIPs. Using the MACE architecture, an MLIP is trained on 86 diverse experimentally reported HOIP structures. The model is tested on 73 unseen perovskite compositions, and achieves chemical accuracy with respect to the reference electronic structure method. Our model is then combined with a simple random structure search algorithm to predict the structure of hypothetical HOIPs given only the proposed composition. Success is demonstrated by correctly and reliably recovering the crystal structure of a set of experimentally known 2D perovskites. Such a random structure search is impossible with ab initio methods due to the associated computational cost, but is relatively inexpensive with the MACE potential. Finally, the procedure is used to predict the structure formed by a new organic cation with no previously known corresponding perovskite. Laboratory synthesis of the new hybrid perovskite confirms the accuracy of our prediction. This capability, applied at scale, enables efficient screening of thousands of combinations of organic cations and inorganic layers.Comment: 14 pages and 9 figures in the main text. Supplementary included in pd

Leveraging Domain Adaptation for Accurate Machine Learning Predictions of New Halide Perovskites

We combine graph neural networks (GNN) with an inexpensive and reliable structure generation approach based on the bond-valence method (BVM) to train accurate machine learning models for screening 222,960 halide perovskites using statistical estimates of the DFT/PBE formation energy (Ef), and the PBE and HSE band gaps (Eg). The GNNs were fined tuned using domain adaptation (DA) from a source model, which yields a factor of 1.8 times improvement in Ef and 1.2 - 1.35 times improvement in HSE Eg compared to direct training (i.e., without DA). Using these two ML models, 48 compounds were identified out of 222,960 candidates as both stable and that have an HSE Eg that is relevant for photovoltaic applications. For this subset, only 8 have been reported to date, indicating that 40 compounds remain unexplored to the best of our knowledge and therefore offer opportunities for potential experimental examination

Dataset of theoretical multinary perovskite oxides

Abstract Perovskite oxides (ternary chemical formula ABO3) are a diverse class of materials with applications including heterogeneous catalysis, solid-oxide fuel cells, thermochemical conversion, and oxygen transport membranes. However, their multicomponent (chemical formula $${A}_{x}{A}_{1-x}^{\text{'}}{B}_{y}{B}_{1-y}^{\text{'}}{O}_{3}$$ A x A 1 − x ' B y B 1 − y ' O 3 ) chemical space is underexplored due to the immense number of possible compositions. To expand the number of computed $${A}_{x}{A}_{1-x}^{{\prime} }{B}_{y}{B}_{1-y}^{{\prime} }{O}_{3}$$ A x A 1 − x ′ B y B 1 − y ′ O 3 compounds we report a dataset of 66,516 theoretical multinary oxides, 59,708 of which are perovskites. First, 69,407 $${A}_{0.5}{A}_{0.5}^{{\prime} }{B}_{0.5}{B}_{0.5}^{{\prime} }{O}_{3}$$ A 0.5 A 0.5 ′ B 0.5 B 0.5 ′ O 3 compositions were generated in the a − b + a − Glazer tilting mode using the computationally-inexpensive Structure Prediction and Diagnostic Software (SPuDS) program. Next, we optimized these structures with density functional theory (DFT) using parameters compatible with the Materials Project (MP) database. Our dataset contains these optimized structures and their formation (ΔH f ) and decomposition enthalpies (ΔH d ) computed relative to MP tabulated elemental references and competing phases, respectively. This dataset can be mined, used to train machine learning models, and rapidly and systematically expanded by optimizing more SPuDS-generated $${A}_{0.5}{A}_{0.5}^{{\prime} }{B}_{0.5}{B}_{0.5}^{{\prime} }{O}_{3}$$ A 0.5 A 0.5 ′ B 0.5 B 0.5 ′ O 3 perovskite structures using MP-compatible DFT calculations