Progress and prospects for accelerating materials science with automated and autonomous workflows

Accelerating materials research by integrating automation with artificial intelligence is increasingly recognized as a grand scientific challenge to discover and develop materials for emerging and future technologies. While the solid state materials science community has demonstrated a broad range of high throughput methods and effectively leveraged computational techniques to accelerate individual research tasks, revolutionary acceleration of materials discovery has yet to be fully realized. This perspective review presents a framework and ontology to outline a materials experiment lifecycle and visualize materials discovery workflows, providing a context for mapping the realized levels of automation and the next generation of autonomous loops in terms of scientific and automation complexity. Expanding autonomous loops to encompass larger portions of complex workflows will require integration of a range of experimental techniques as well as automation of expert decisions, including subtle reasoning about data quality, responses to unexpected data, and model design. Recent demonstrations of workflows that integrate multiple techniques and include autonomous loops, combined with emerging advancements in artificial intelligence and high throughput experimentation, signal the imminence of a revolution in materials discovery

Progress and prospects for accelerating materials science with automated and autonomous workflows

Accelerating materials research by integrating automation with artificial intelligence is increasingly recognized as a grand scientific challenge to discover and develop materials for emerging and future technologies. While the solid state materials science community has demonstrated a broad range of high throughput methods and effectively leveraged computational techniques to accelerate individual research tasks, revolutionary acceleration of materials discovery has yet to be fully realized. This perspective review presents a framework and ontology to outline a materials experiment lifecycle and visualize materials discovery workflows, providing a context for mapping the realized levels of automation and the next generation of autonomous loops in terms of scientific and automation complexity. Expanding autonomous loops to encompass larger portions of complex workflows will require integration of a range of experimental techniques as well as automation of expert decisions, including subtle reasoning about data quality, responses to unexpected data, and model design. Recent demonstrations of workflows that integrate multiple techniques and include autonomous loops, combined with emerging advancements in artificial intelligence and high throughput experimentation, signal the imminence of a revolution in materials discovery

Tracking materials science data lineage to manage millions of materials experiments and analyses

In an era of rapid advancement of algorithms that extract knowledge from data, data and metadata management are increasingly critical to research success. In materials science, there are few examples of experimental databases that contain many different types of information, and compared with other disciplines, the database sizes are relatively small. Underlying these issues are the challenges in managing and linking data across disparate synthesis and characterization experiments, which we address with the development of a lightweight data management framework that is generally applicable for experimental science and beyond. Five years of managing experiments with this system has yielded the Materials Experiment and Analysis Database (MEAD) that contains raw data and metadata from millions of materials synthesis and characterization experiments, as well as the analysis and distillation of that data into property and performance metrics via software in an accompanying open source repository. The unprecedented quantity and diversity of experimental data are searchable by experiment and analysis attributes generated by both researchers and data processing software. The search web interface allows users to visualize their search results and download zipped packages of data with full annotations of their lineage. The enormity of the data provides substantial challenges and opportunities for incorporating data science in the physical sciences, and MEAD’s data and algorithm management framework will foster increased incorporation of automation and autonomous discovery in materials and chemistry research

Combinatorial screening yields discovery of 29 metal oxide photoanodes for solar fuel generation

Combinatorial synthesis combined with high throughput electrochemistry enabled discovery of 29 ternary oxide photoanodes, 15 with visible light response for oxygen evolution. Y₃Fe₅O₁₂ and trigonal V₂CoO₆ emerge as particularly promising candidates due to their photorepsonse at sub-2.4 eV illumination

Hindered Aluminum Plating and Stripping in Urea/NMA/Al(OTF)3_3 as a Cl-Free Electrolyte for Aluminum Batteries

Conventional electrolytes for aluminum metal batteries are highly corrosive because they must remove the Al$_2$O$_3$ layer to enable plating and stripping. However, such corrosiveness impacts the stability of all cell parts, thus hampering the real application of aluminum-metal batteries. The urea/NMA/Al(OTF)$_3$ electrolyte is a non-corrosive alternative to the conventional [EMImCl]: AlCl$_3$ ionic liquid electrolyte (ILE). Unfortunately, this electrolyte demonstrates poor Al plating/stripping, probably because (being not corrosive) it cannot remove the Al$_2$O$_3$ passivation layer. This work proves that no plating/stripping occurs on the Al electrode despite modifying the Al surface. We highlight how urea/NMA/Al(OTF)$_3$ electrolyte and the state of the Al electrode surface impact the interphase layer formation and, consequently, the likelihood and reversibility of Al plating/stripping. We point up the requirement for carefully drying electrolyte mixture and components, as water results in hydrogen evolution reaction and creation of an insulating interphase layer containing Al(OH)$_3$, AlF$_3$, and re-passivated Al oxide, which finally blocks the path for the possible Al plating/stripping

Analyzing machine learning models to accelerate generation of fundamental materials insights

Machine learning for materials science envisions the acceleration of basic science research through automated identification of key data relationships to augment human interpretation and gain scientific understanding. A primary role of scientists is extraction of fundamental knowledge from data, and we demonstrate that this extraction can be accelerated using neural networks via analysis of the trained data model itself rather than its application as a prediction tool. Convolutional neural networks excel at modeling complex data relationships in multi-dimensional parameter spaces, such as that mapped by a combinatorial materials science experiment. Measuring a performance metric in a given materials space provides direct information about (locally) optimal materials but not the underlying materials science that gives rise to the variation in performance. By building a model that predicts performance (in this case photoelectrochemical power generation of a solar fuels photoanode) from materials parameters (in this case composition and Raman signal), subsequent analysis of gradients in the trained model reveals key data relationships that are not readily identified by human inspection or traditional statistical analyses. Human interpretation of these key relationships produces the desired fundamental understanding, demonstrating a framework in which machine learning accelerates data interpretation by leveraging the expertize of the human scientist. We also demonstrate the use of neural network gradient analysis to automate prediction of the directions in parameter space, such as the addition of specific alloying elements, that may increase performance by moving beyond the confines of existing data

One‐Shot Active Learning for Globally Optimal Battery Electrolyte Conductivity**

Non-aqueous aprotic battery electrolytes need to perform well over a wide range of temperatures in practical applications. Herein we present a one-shot active learning study to find all conductivity optima, confidence bounds, and relating formulation trends in the temperature range from −30 °C to 60 °C. This optimization is enabled by a high-throughput formulation and characterization setup guided by one-shot active learning utilizing robust and heavily regularized polynomial regression. Whilst there is an initially good agreement for intermediate and low temperatures, there is a need for the active learning step to improve the model for high temperatures. Optimized electrolyte formulations likely correspond to the highest physically possible conductivities within this formulation system when compared to literature data. A thorough error propagation analysis yields a fidelity assessment of conductivity measurements and electrolyte formulation