Machine learning for high-throughput experimental exploration of metal halide perovskites
TL;DR: In this paper, the authors provide an overview of the state of the art in automated metal halide perovskites (MHPs) synthesis and existing methods for navigating multicomponent compositional space.
About: This article is published in Joule.The article was published on 2021-11-17. It has received 23 citations till now.
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TL;DR: Li et al. as discussed by the authors presented a machine learning-guided framework of sequential learning for manufacturing the process optimization of perovskite solar cells, which enabled a faster optimization in comparison with other conventional researcher-driven design-of-experiment methods.
30 citations
TL;DR: Self-driving Lab (SDL) as discussed by the authors is a machine-learning-assisted modular experimental platform that iteratively operates a series of experiments selected by the machine learning algorithm to achieve a user-defined objective.
Abstract: Accelerating the discovery of new molecules and materials, as well as developing green and sustainable ways to synthesize them, will help to address global challenges in energy, sustainability and healthcare. The recent growth of data science and automated experimentation techniques has resulted in the advent of self-driving labs (SDLs) via the integration of machine learning, lab automation and robotics. An SDL is a machine-learning-assisted modular experimental platform that iteratively operates a series of experiments selected by the machine learning algorithm to achieve a user-defined objective. These intelligent robotic assistants help researchers to accelerate the pace of fundamental and applied research through rapid exploration of the chemical space. In this Review, we introduce SDLs and provide a roadmap for their implementation by non-expert scientists. We present the status quo of successful SDL implementations in the field and discuss their current limitations and future opportunities to accelerate finding solutions for societal needs. Self-driving labs (SDLs) combine machine learning with automated experimental platforms, enabling rapid exploration of the chemical space and accelerating the pace of materials and molecular discovery. In this Review, the application of SDLs, their limitations and future opportunities are discussed, and a roadmap is provided for their implementation by non-expert scientists.
15 citations
TL;DR: In this paper , the authors highlighted the role and pipelines of ML frameworks to close the gap between experiment and theory in metal halide perovskite (MHP) applications.
Abstract: Metal halide perovskite (MHP) is a promising next generation energy material for various applications, such as solar cells, light emitting diodes, lasers, sensors, and transistors. MHPs show excellent mechanical, dielectric, photovoltaic, photoluminescence, and electronic properties, and such intriguing physical and chemical properties have drawn attention recently. However, there exists a chasm between the successful applications of MHPs and theoretical understandings. The difficulty arises from the intrinsic properties of MHPs, including structural disorder, ionic interactions, nonadiabatic effects, and composition diversity. Machine learning (ML) approaches have shown great promise as a tool to overcome the theoretical obstacles in many fields of science. In this perspective, the pending theoretical challenges from experiments are overviewed and promising ML approaches, including ab initio ML potentials, materials design/optimization models, and data mining strategies are proposed. Possible roles and pipelines of ML frameworks are highlighted to close the gap between experiment and theory in MHPs.
10 citations
01 Jan 2022-Journal of Materials Chemistry C. Materials for Optical, Magnetic and Electronic Devices
TL;DR: In this article , microcrystalline Cs 2 Ag x Na 1− x Bi y In 1− y Cl 6 perovskites with tailored composition emitting broadband yellow-white photoluminescence with a quantum yield of up to 92% were produced by a new green approach under ambient conditions.
Abstract: Microcrystalline Cs 2 Ag x Na 1− x Bi y In 1− y Cl 6 perovskites with tailored composition emitting broadband yellow-white photoluminescence with a quantum yield of up to 92% were produced by a new “green” approach under ambient conditions.
8 citations
TL;DR: In this paper , the influence of organic cation rotation on structure, optoelectrical properties, and stability of hybrid organic-inorganic perovskites (HOIPs) is summarized.
7 citations
References
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Book•
01 Jan 1995TL;DR: Detailed notes on Bayesian Computation Basics of Markov Chain Simulation, Regression Models, and Asymptotic Theorems are provided.
Abstract: FUNDAMENTALS OF BAYESIAN INFERENCE Probability and Inference Single-Parameter Models Introduction to Multiparameter Models Asymptotics and Connections to Non-Bayesian Approaches Hierarchical Models FUNDAMENTALS OF BAYESIAN DATA ANALYSIS Model Checking Evaluating, Comparing, and Expanding Models Modeling Accounting for Data Collection Decision Analysis ADVANCED COMPUTATION Introduction to Bayesian Computation Basics of Markov Chain Simulation Computationally Efficient Markov Chain Simulation Modal and Distributional Approximations REGRESSION MODELS Introduction to Regression Models Hierarchical Linear Models Generalized Linear Models Models for Robust Inference Models for Missing Data NONLINEAR AND NONPARAMETRIC MODELS Parametric Nonlinear Models Basic Function Models Gaussian Process Models Finite Mixture Models Dirichlet Process Models APPENDICES A: Standard Probability Distributions B: Outline of Proofs of Asymptotic Theorems C: Computation in R and Stan Bibliographic Notes and Exercises appear at the end of each chapter.
16,079 citations
TL;DR: The art and science of cause and effect have been studied in the social sciences for a long time as mentioned in this paper, see, e.g., the theory of inferred causation, causal diagrams and the identification of causal effects.
Abstract: 1. Introduction to probabilities, graphs, and causal models 2. A theory of inferred causation 3. Causal diagrams and the identification of causal effects 4. Actions, plans, and direct effects 5. Causality and structural models in the social sciences 6. Simpson's paradox, confounding, and collapsibility 7. Structural and counterfactual models 8. Imperfect experiments: bounds and counterfactuals 9. Probability of causation: interpretation and identification Epilogue: the art and science of cause and effect.
12,606 citations
TL;DR: In this article, the authors present a new science leading to such an approach, namely geostatistics, which is a new approach for estimating the estimation of ore grades and reserves.
Abstract: Knowledge of ore grades and ore reserves as well as error estimation of these values, is fundamental for mining engineers and mining geologists. Until now no appropriate scientific approach to those estimation problems has existed: geostatistics, the principles of which are summarized in this paper, constitutes a new science leading to such an approach. The author criticizes classical statistical methods still in use, and shows some of the main results given by geostatistics. Any ore deposit evaluation as well as proper decision of starting mining operations should be preceded by a geostatistical investigation which may avoid economic failures.
4,203 citations
Book•
01 Jan 2016
TL;DR: The response to this technological revolution must be integrated and comprehensive, involving all stakeholders of the global polity, from the public and private sectors to academia and civil society, as mentioned in this paper.
Abstract: We stand on the brink of a technological revolution that will fundamentally alter the way we live, work, and relate to one another. In its scale, scope, and complexity, the transformation will be unlike anything humankind has experienced before. We do not yet know just how it will unfold, but one thing is clear: the response to it must be integrated and comprehensive, involving all stakeholders of the global polity, from the public and private sectors to academia and civil society.
3,501 citations
TL;DR: In this paper, general guidelines for the development of lead-free piezoelectric ceramics are presented, ranging from atom to phase diagram, and the current development stage in lead free piezoceramics is then critically assessed.
Abstract: A large body of work has been reported in the last 5 years on the development of lead-free piezoceramics in the quest to replace lead–zirconate–titanate (PZT) as the main material for electromechanical devices such as actuators, sensors, and transducers. In specific but narrow application ranges the new materials appear adequate, but are not yet suited to replace PZT on a broader basis. In this paper, general guidelines for the development of lead-free piezoelectric ceramics are presented. Suitable chemical elements are selected first on the basis of cost and toxicity as well as ionic polarizability. Different crystal structures with these elements are then considered based on simple concepts, and a variety of phase diagrams are described with attractive morphotropic phase boundaries, yielding good piezoelectric properties. Finally, lessons from density functional theory are reviewed and used to adjust our understanding based on the simpler concepts. Equipped with these guidelines ranging from atom to phase diagram, the current development stage in lead-free piezoceramics is then critically assessed.
2,510 citations