Arun Mannodi-Kanakkithodi

Improvements to the radiative efficiency of halide perovskite thin films are necessary to approach fundamental solar cell performance limits. We tailored perovskite crystallization by leveraging the eutectic interaction between zinc bromide and methylammonium chloride additives. The resulting films had high photoluminescence quantum yield and charge carrier decay times and low surface recombination velocity. Nanoprobe x-ray microscopy revealed local melting of the eutectic at grain boundaries during annealing that reduced intragranular structural defect density. Annealing also homogenized the halide distribution and caused zinc segregation to grain boundaries. Solar cells fabricated with the volatile eutectic reached 26.5% power conversion efficiency (PCE, certified 25.9%), and perovskite minimodules (areas more than 15 square centimeters) had 23.9% PCE. Small-area cells with a poly[bis(4-phenyl)(2,4,6-trimethylphenyl)amine] hole transporter retained 85.8% of their initial 24.1% PCE after 1400 hours of continuous operation at 85°C. Furthermore, this approach enables 26.1% external quantum efficiency as light-emitting diodes.

Halide and chalcogenide perovskites are promising materials for electronics, solar absorption, and quantum technologies, but their vast compositional landscape necessitates the use of density functional theory (DFT) simulations accelerated by machine learning (ML) to guide experiments. To advance this research, it is imperative to establish standardized and comprehensive workflows for the systematic study of perovskites. This article outlines the methodologies used to construct a “DFT + ML” perovskite database and implement FAIR (Findable, Accessible, Interoperable, and Reusable) data/model-sharing principles. We discuss our computational workflow, dataset, and different types of ML models, which are each released publicly via the nanoHUB platform.

Ag@Ag ₂ Cu ₂ O ₃ catalyst achieves excellent oxygen reduction reaction (ORR) activity and durability by leveraging an interfacial synergy that optimizes oxygen binding and facilitates ORR.

We developed a framework for predicting the energies and ground state configurations of native point defects, extrinsic dopants and impurities, and defect complexes across zinc blende-phase Cd/Zn–Te/Se/S compounds, important for CdTe-based solar cells. This framework, named DeFecT-FF, is powered by high-throughput density functional theory (DFT) computations and crystal graph-based machine learning force field (MLFF) models trained on the DFT data. The Cd/Zn–Te/Se/S chemical space is chosen because alloying at Cd or Te sites is a promising avenue to tailor the electronic and defect properties of the CdTe absorber layer to potentially improve solar cell performance. The sheer number of defect configurations achievable when considering all possible singular defects and their combinations, symmetry-breaking operations, and defect charge states, as well as the expense of running large supercell calculations, makes this an ideal problem for developing accurate and widely-applicable force field models. Here, we introduce our dataset of structures and energies from HSE06 geometry optimization, including bulk and alloyed supercells with and without defects. Data were gradually expanded using active learning and accurate MLFF models were trained to predict energies and atomic forces across different charge states. Via accelerated prediction and screening, we identified many new low energy defect configurations and obtained high-fidelity defect formation energy diagrams using HSE06 calculations with spin–orbit coupling. The DeFecT-FF framework has been released publicly as an online tool on the nanoHUB platform, allowing users to upload any crystallographic information file, generate defects of interest, and compute defect formation energies as a function of Fermi level and chemical potential conditions, thus bypassing expensive DFT calculations.

LLM-assisted mining converts 200 studies on 2D halide perovskites into a spacer-cation dataset. GNN-GP models predict structural and optoelectronic targets with uncertainty, and clustering reveals chemical spacer families.

Recent advances in machine learning methods for materials discovery have made considerable progress in reducing the time to identify novel materials with tailored properties. Materials domains such as high entropy materials, polymers, semiconductors, and molecules have seen numerous materials-by-design algorithmic advances over the last decade, showing the power of machine learning in aiding the exploration of large chemical and structural spaces. However, a major bottleneck in these methodologies still exists, particularly in their ability to extrapolate to new design spaces. Existing machine learning algorithms are built on a foundation of interpolative mathematical formalisms, allowing them to identify new candidates that are different from known materials but still relatively similar. These algorithms still struggle to extrapolate to completely novel material systems, often referred to as “out-of-domain” samples, as their underlying mathematical formalisms are built upon parameterized similarity metrics fit to existing materials databases. By design, these algorithms are biased toward existing data, and often break down as similarity to known materials breaks down. Recently, out-of-domain machine learning methods have made progress toward alleviating this challenge, often by including some level of known physics in the algorithms. In this article, we explore these recent advances from the perspective of materials-by-design, showcasing how out-of-domain machine learning has made progress in identifying truly novel material candidates, and discuss the remaining challenges.

Halide perovskites (HaPs) hold immense potential for applications such as optoelectronics and catalysis. Their vast compositional space, spanning bulk alloys, defects, impurities, surfaces, and surface defects, poses significant challenges for efficient exploration and optimization. To address this, we trained a unified graph-based deep learned interatomic potential capable of optimizing and predicting energetics across these diverse structural motifs and navigating the complex potential energy surface (PES). Using a comprehensive density functional theory data set of HaP structures, which includes bulk alloys, native and impurity defects, and surface slabs, we rigorously trained and benchmarked the M3GNet-based machine learning interatomic potential (IAP). The M3GNet-IAP framework, trained on DFT-calculated energies, forces, and stresses, enables gradient-based optimization and efficient exploration of the PES. Our models, trained on a data set of ∼12,000 HaP structures across diverse structural domains, demonstrated robust generalizability across the complex PES, achieving low errors (energies (E): 3.7 meV/atom; forces (f): 16.5 meV/Å; stresses (σ): 5.5 MPa), and accurately predicting formation energies, decomposition energies, defect energies, and surface energies. Our unified surrogate model provides a holistic approach to geometry optimization across different structural variations in HaPs and will be transformative for the discovery of promising new compositions, important defects and dopants, and surface properties.

This framework combines high-throughput computations and machine learning to drive the discovery of stable and defect-tolerant chalcogenide semiconductor alloys with attractive optoelectronic properties.