Quantum Chemistry-Informed Active Learning to Accelerate the Design and Discovery of Sustainable Energy Storage Materials

Doan, Hieu A.; Agarwal, Garvit; Qian, Hai; Counihan, Michael J.; Rodríguez‐López, Joaquín; Moore, Jeffrey S.; Assary, Rajeev S.

doi:10.1021/acs.chemmater.0c00768

Cited by 67 publications

(92 citation statements)

References 57 publications

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“…Hence, it is inadequate to combine both datasets as equivalent or include DFT directly as a prior following state-of-the-art model-free BO. 32,33 We herein define a data-fused probabilistic constraint approach according to Eq. (2):…”

Section: Data Fusion Approach: Incorporation Of Phase Thermodynamics Into Iterative Composition Selectionmentioning

confidence: 99%

A data fusion approach to optimize compositional stability of halide perovskites

Sun

Tiihonen

Oviedo

et al. 2021

Matter

121

172

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Improving the heat-moisture-light stability of organic-inorganic perovskites, a widely studied semiconductor material class, is a critical challenge. Compositional search within multinary perovskites employing brute force synthesis followed by environmental tests are prohibitively expensive in large chemical spaces. To identify the most stable multi-cation lead iodide perovskites containing Cs, formamidinium (FA) and methylammonium (MA), we fuse results from density functional theory (DFT) calculations and in situ thin-film degradation test within an end-toend machine learning (ML) algorithm to inform the compositional optimization of CsxMAyFA1-x-yPbI3. We integrate phase thermodynamics modelling as a probabilistic constraint in a Bayesian optimization (BO) loop, which effectively guides the experimental search while considering both structural and environmental stability. After three optimization rounds and only sampling 1.8% of the compositional space, we identify thin-film compositions centred at Cs0.17MA0.03FA0.80PbI3 that achieve a 3x delay in macroscopic degradation onset under elevated temperature, humidity, and light compared with the more complex state-of-the-art Cs0.05(MA0.17FA0.83)0.95Pb(I0.83Br0.17)3. We find up to 8% of MA can be incorporated into the perovskite structure before stability is significantly compromised. Cs is beneficial at low concentrations, however, beyond 17% is found to contribute to reduced stability. Synchrotron-based grazing-incidence wide-angle X-ray scattering (GIWAXS) further validates that the interplay of chemical decomposition and phase separation governs the non-linear instability landscape of this compositional space. We reveal the detrimental role of the ẟ-CsPbI3 minority phase in accelerating degradation and it can be kinetically suppressed by cooptimising Cs and MA content, providing insights into simplifying perovskite compositions for further environmental stability enhancement. Our approach realizes the effectiveness of MLenabled data fusion in achieving a holistic, efficient, and physics-informed experimentation for multinary systems, potentially generalisable to materials search in the vast structural and alloyed spaces beyond halide perovskites.

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Section: Data Fusion Approach: Incorporation Of Phase Thermodynamics Into Iterative Composition Selectionmentioning

confidence: 99%

A data fusion approach to optimize compositional stability of halide perovskites

Sun

Tiihonen

Oviedo

et al. 2021

Matter

121

172

View full text Add to dashboard Cite

show abstract

“…The burgeoning field of materials informatics has led to many successes, with some of the most notable contributions resulting from the combination of first principle computations and machine learning. ML applied on DFT data has seen the development of predictive and design tools [29][30][31] , the discovery of novel materials for batteries, capacitors, solar cells, and thermoelectrics [32][33][34][35][36] , and the efficient exploration of extremely large chemical spaces 37,38 . Recent work from our group involved performing high-throughput DFT computations to study the formation energies and charge transition levels of impurities in halide perovskites 6 and Cdchalcogenides 11 , following which ML models were trained for the prediction and screening of impurity atoms that can shift the equilibrium Fermi level as determined by dominant native defects.…”

Section: Semiconductorsmentioning

confidence: 99%

Universal Machine Learning Framework for Defect Predictions in Zinc Blende Semiconductors

Mannodi-Kanakkithodi

Xiang

Jacoby

et al. 2021

Preprint

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Point defects or impurities are either naturally present in semiconductors or may be intentionally introduced to tune their electronic and optical properties. The nature of impurity energy levels can strongly influence the performance of a semiconductor in applications ranging from solar cells to photodiodes to infrared sensors to qubits for quantum computing. In this work, we develop a framework powered by machine learning (ML) and high-throughput density functional theory (DFT) computations for the prediction and screening of functional impurities in group IV, III-V, and II-VI zinc blende semiconductors. Elements spanning the length and breadth of the periodic table are considered as impurity atoms at the cation, anion, or interstitial sites in supercells of 34 candidate semiconductors, leading to a chemical space of 12,000 points, 10% of which are used to generate a DFT dataset of charge dependent defect formation energies. Descriptors based on tabulated elemental properties, defect coordination environment, and relevant semiconductor properties are used to train ML regression models for the DFT computed properties, resulting in statistical predictions of the neutral state formation energies and charge transition levels of all possible impurities in the given set of compounds. Kernel ridge regression, Gaussian process regression, and neural networks, with appropriate feature selection and hyperparameter optimization, are seen to yield similar predictive performances and meaningful uncertainty estimates. We apply the ML framework to screen all impurities with lower formation energy than dominant native defects in all group IV, III-V, and II-VI zinc blende semiconductors. An online tool resulting from this work for predicting and visualizing defect properties in semiconductors is made available on github.

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“…Hence, it is inadequate to combine both datasets as equivalent or include DFT directly as a prior following state-of-the-art model-free BO. 28,29 We herein define a data-fused probabilistic constraint approach according to Eq. ( 2):…”

Section: Csxmaya1-x-ypbi3 (mentioning

confidence: 99%

A Physical Data Fusion Approach to Optimize Compositional Stability of Halide Perovskites

Sun

Tiihonen²,

Oviedo³

et al. 2020

Preprint

View full text Add to dashboard Cite

Compositional search within multinary perovskites employing brute force synthesis are prohibitively expensive in large chemical spaces. To identify the most stable multi-cation lead iodide perovskites containing Cs, formamidinium (FA) and methylammonium (MA), we fuse results from density functional theory (DFT) calculations and in situ thin-film degradation test within an end-to-end machine learning (ML) algorithm to inform the compositional optimization of CsxMAyFA1-x-yPbI3. We integrate phase thermodynamics modelling as a probabilistic constraint in a Bayesian optimization (BO) loop, which effectively guides the experimental search while considering both structural and environmental stability. After three optimization rounds and only sampling 1.8% of the compositional space, we identify thin-film compositions centred at Cs0.17MA0.03FA0.80PbI3 that achieve a 3x delay in macroscopic degradation onset under elevated temperature, humidity, and light compared with the more complex state-of-the-art Cs0.05(MA0.17FA0.83)0.95Pb(I0.83Br0.17)3. We find up to 8% of MA can be incorporated into the perovskite structure before stability is significantly compromised. Cs is beneficial at low concentrations, however, beyond 17% is found to contribute to reduced stability. Synchrotron-based grazing-incidence wide-angle X-ray scattering (GIWAXS) further validates that the interplay of chemical decomposition and phase separation governs the non-linear instability landscape of this compositional space. We reveal the detrimental role of the ẟ-CsPbI3 minority phase in accelerating degradation and it can be kinetically suppressed by co-optimising Cs and MA content, providing insights into simplifying perovskite compositions for further environmental stability enhancement. Our approach realizes the effectiveness of ML-enabled data fusion in achieving a holistic, efficient, and physics-informed experimentation for multinary systems, potentially generalisable to materials search in the vast structural and alloyed spaces beyond halide perovskites.

show abstract

Quantum Chemistry-Informed Active Learning to Accelerate the Design and Discovery of Sustainable Energy Storage Materials

Cited by 67 publications

References 57 publications

A data fusion approach to optimize compositional stability of halide perovskites

A data fusion approach to optimize compositional stability of halide perovskites

Universal Machine Learning Framework for Defect Predictions in Zinc Blende Semiconductors

A Physical Data Fusion Approach to Optimize Compositional Stability of Halide Perovskites

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