Accelerating Photovoltaic Materials Development via High-Throughput Experiments and Machine-Learning-Assisted Diagnosis

Abstract

Accelerating the experimental cycle for new materials development is vital for addressing the grand energy challenges of the 21st century. We fabricate and characterize 75 unique halide perovskite-inspired solution-based thin-film materials within a two-month period, with 87% exhibiting band gaps between 1.2 eV and 2.4 eV that are of interest for energy-harvesting applications. This increased throughput is enabled by streamlining experimental workflows, developing a set of precursors amenable to high-throughput synthesis, and developing machine-learning assisted diagnosis. We utilize a deep neural network to classify compounds based on experimental X-ray diffraction data into 0D, 2D, and 3D structures more than 10 times faster than human analysis and with 90% accuracy. We validate our methods using lead-halide perovskites and extend the application to novel lead-free compositions. The wider synthesis window and faster cycle of learning enables three noteworthy scientific findings: (1) we realize four inorganic layered perovskites, A3B2Br9 (A = Cs, Rb; B = Bi, Sb) in thin-film form via one-step liquid deposition; (2) we report a multi-site lead-free alloy series that was not previously described in literature, Cs3(Bi1-xSbx)2(I1-xBrx)9; and (3) we reveal the effect on bandgap (reduction to <2 eV) and structure upon simultaneous alloying on the B-site and X-site of Cs3Bi2I9 with Sb and Br. This study demonstrates that combining an accelerated experimental cycle of learning and machine-learning based diagnosis represents an important step toward realizing fully-automated laboratories for materials discovery and development.

NIPS 2018 Workshop: Machine Learning for Molecules and Materials

References in corpus (4)

• Scikit-learn: Machine Learning in Python
• Identifying defect-tolerant semiconductors with high minority carrier lifetimes: Beyond hybrid lead halide perovskites
• Accelerating Materials Development via Automation, Machine Learning, and High-Performance Computing
• A recommendation engine for suggesting unexpected thermoelectric chemistries

Cited by in corpus (15)

• Autonomous discovery in the chemical sciences part I: Progress
• Autonomous optimization of nonaqueous battery electrolytes via robotic experimentation and machine learning
• Autonomous discovery of battery electrolytes with robotic experimentation and machine-learning
• Autonomous synthesis of metastable materials
• A Machine Learning and Computer Vision Approach to Rapidly Optimize Multiscale Droplet Generation
• Golem: An algorithm for robust experiment and process optimization
• Electronic structure calculations of twisted multi-layer graphene superlattices
• Exploring Supervised Machine Learning for Multi-Phase Identification and Quantification from Powder X-Ray Diffraction Spectra
• Using Scalable Computer Vision to Automate High-throughput Semiconductor Characterization
• Choosing a Suitable Acquisition Function for Batch Bayesian Optimization: Comparison of Serial and Monte Carlo Approaches
• Automating Crystal-Structure Phase Mapping: Combining Deep Learning with Constraint Reasoning
• Quantitative predictions of photo-emission dynamics in metal halide perovskites via machine learning
• Gemini: Dynamic Bias Correction for Autonomous Experimentation and Molecular Simulation
• Multi-Variable Batch Bayesian Optimization in Materials Research: Synthetic Data Analysis of Noise Sensitivity and Problem Landscape Effects
• Online Preconditioning of Experimental Inkjet Hardware by Bayesian Optimization in Loop