The pursuit of scalable and manufacturable all-solid-state batteries continues to intensify, motivated by the rapidly increasing demand for safe, dense electrical energy storage. In this perspective, we describe the numerous, often conflicting implications of materials choices that have been made in the search for effective mitigations to the interfacial instabilities plaguing solid-state batteries. Specifically, we show that the manufacturing scalability of solid-state batteries can be governed by at least three principal consequences of materials selection: (1) the availability, scaling capacity, and price volatility of the chosen materials’ constituents, (2) the manufacturing processes needed to integrate the chosen materials into full cells, and (3) the cell performance that may be practically achieved with the chosen materials and processes. While each of these factors is, in isolation, a pivotal determinant of manufacturing scalability, we show that consideration and optimization of their collective effects and trade-offs is necessary to more completely chart a scalable pathway to manufacturing. With examples pulled from recent developments in solid-state batteries, we illustrate the consequences of materials choice on materials availability, processing requirements and challenges, and resultant device performance. We demonstrate that while each of these factors is, by itself, essential to understanding manufacturing scalability, joint consideration of all three provides for a more comprehensive understanding of the specific factors that could impede the scale up to production. Much of the recent activity in solid-state battery research has been aimed at mitigating the various interfacial instabilities that currently prevent the fabrication of a low-cost, high-performance device. With such a wide breadth of options, it can be difficult for researchers to identify the most promising or scalable pathways forward. As such, we aim to empower researchers to make more informed decisions by providing them with insights into how their materials choices are likely to impact the manufacturing scalability of different interfacial mitigation alternatives. In doing so, we hope that generalizable lessons about scalability can be extracted and applied to subsequent challenges in solid-state battery development, thereby accelerating their scale up to manufacturing. In the design of solid-state batteries, a considerable range of materials is under investigation for both the solid electrolyte as well as for mitigations to the numerous interfacial instabilities that hamper device performance. The choices made for these material and device design decisions have many, often conflicting, effects on manufacturing scalability. By modeling example cells reported in the literature, we show that collective evaluation of these trade-offs is imperative to accurately assess potential barriers to manufacturing scale up.

Methods sections adopt a common practice of past-tense narrative using passive voice. Here, we discuss issues with current and historical writing conventions in materials science literature and propose a structured way to facilitate reproducibility, clarity, and machine readability.

Sustained growth in lithium-ion battery (LIB) demand within the transportation sector (and the electricity sector) motivates detailed investigations of whether future raw materials supply will reconcile with resulting material requirements for these batteries. We track the metal content associated with compounds used in LIBs. We find that most of the key constituents, including manganese, nickel, and natural graphite, have sufficient supply to meet the anticipated increase in demand for LIBs. There may be challenges in rapidly scaling the use of materials associated with lithium and cobalt in the short term. Due to long battery lifetimes and multiple end uses, recycling is unlikely to provide significant short-term supply. There are risks associated with the geopolitical concentrations of these elements, particularly for cobalt. The lessons revealed in this work can be relevant to other industries in which the rapid growth of a materials-dependent technology disrupts the global supply of those materials. The key conclusions of this perspective have shown that the supply of most materials contained within lithium-ion batteries will likely meet the demand for the near future. However, there are potential risks associated with the supply of cobalt. Furthermore, if there is rapid adoption of electric vehicles (incentivized by policy interventions including a carbon tax, higher fuel taxes, and more aggressive Corporate Average Fuel Economy targets), demand could outpace supply for some battery-grade materials (even for lithium in the very near term). The implications for research based on this perspective span many scales. First, continued research into cathode materials that alleviate some of these supply issues is of interest, particularly those that are cobalt free. Supply chain research and investigations in the policy domain may also help uncover ways to address materials availability in the future. Future investigations should provide a dynamic analysis with sufficient detail to map technological and operational changes to their impact on cost and to map performance to market value. There has been continued growth in lithium-ion battery-powered electric vehicles. This puts new pressure on the supply of materials used in these products. We present an analysis of supply chain issues for lithium, manganese, cobalt, nickel, and natural graphite focused first on their potential supply concerns and then the scaled demand for these materials. This contribution provides practical considerations that should factor into sustainable energy research, particularly as it relates to global impact on materials supply chains.

In the past several years, Materials Genome Initiative (MGI) efforts have produced myriad examples of computationally designed materials in the fields of energy storage, catalysis, thermoelectrics, and hydrogen storage as well as large data resources that are used to screen for potentially transformative compounds. The bottleneck in high-Throughput materials design has thus shifted to materials synthesis, which motivates our development of a methodology to automatically compile materials synthesis parameters across tens of thousands of scholarly publications using natural language processing techniques. To demonstrate our framework's capabilities, we examine the synthesis conditions for various metal oxides across more than 12 thousand manuscripts. We then apply machine learning methods to predict the critical parameters needed to synthesize titania nanotubes via hydrothermal methods and verify this result against known mechanisms. Finally, we demonstrate the capacity for transfer learning by using machine learning models to predict synthesis outcomes on materials systems not included in the training set and thereby outperform heuristic strategies.

Research publications are the major repository of scientific knowledge. However, their unstructured and highly heterogenous format creates a significant obstacle to large-scale analysis of the information contained within. Recent progress in natural language processing (NLP) has provided a variety of tools for high-quality information extraction from unstructured text. These tools are primarily trained on non-technical text and struggle to produce accurate results when applied to scientific text, involving specific technical terminology. During the last years, significant efforts in information retrieval have been made for biomedical and biochemical publications. For materials science, text mining (TM) methodology is still at the dawn of its development. In this review, we survey the recent progress in creating and applying TM and NLP approaches to materials science field. This review is directed at the broad class of researchers aiming to learn the fundamentals of TM as applied to the materials science publications.

Given the emergence of data science and machine learning throughout all aspects of society, but particularly in the scientific domain, there is increased importance placed on obtaining data. Data in materials science are particularly heterogeneous, based on the significant range in materials classes that are explored and the variety of materials properties that are of interest. This leads to data that range many orders of magnitude, and these data may manifest as numerical text or image-based information, which requires quantitative interpretation. The ability to automatically consume and codify the scientific literature across domains - enabled by techniques adapted from the field of natural language processing - therefore has immense potential to unlock and generate the rich datasets necessary for data science and machine learning. This review focuses on the progress and practices of natural language processing and text mining of materials science literature and highlights opportunities for extracting additional information beyond text contained in figures and tables in articles. We discuss and provide examples for several reasons for the pursuit of natural language processing for materials, including data compilation, hypothesis development, and understanding the trends within and across fields. Current and emerging natural language processing methods along with their applications to materials science are detailed. We, then, discuss natural language processing and data challenges within the materials science domain where future directions may prove valuable.

Lithium-ion battery demand, particularly for electric vehicles, is projected to increase by over 300% throughout the next decade. With these expected increases in demand, cobalt (Co)-dependent technologies face the risk of significant impact from supply concentration and mining limitations in the short term. Increased extraction and secondary recovery form the basis of modeling scenarios that examine implications on Co supply to 2030. Demand for Co is estimated to range from 235 to 430 ktonnes in 2030. This upper bound on Co demand in 2030 corresponds to 280% of world refinery capacity in 2016. Supply from scheduled and unscheduled production as well as secondary production is estimated to range from 320 to 460 ktonnes. Our analysis suggests the following: (1) Co price will remain relatively stable in the short term, given that this range suggests even a supply surplus, (2) future Co supply will become more diversified geographically and mined more as a byproduct of nickel (Ni) over this period, and (3) for this demand to be met, attention should be paid to sustained investments in refined supply of Co and secondary recovery.

Decision analysis-a systematic approach to solving complex problems-offers tools and frameworks to support decision making that are increasingly being applied to environmental challenges. Alternatives analysis is a method used in regulation and product design to identify, compare, and evaluate the safety and viability of potential substitutes for hazardous chemicals.Assess whether decision science may assist the alternatives analysis decision maker in comparing alternatives across a range of metrics.A workshop was convened that included representatives from government, academia, business, and civil society and included experts in toxicology, decision science, alternatives assessment, engineering, and law and policy. Participants were divided into two groups and prompted with targeted questions. Throughout the workshop, the groups periodically came together in plenary sessions to reflect on other groups' findings.We conclude the further incorporation of decision science into alternatives analysis would advance the ability of companies and regulators to select alternatives to harmful ingredients, and would also advance the science of decision analysis.We advance four recommendations: (1) engaging the systematic development and evaluation of decision approaches and tools; (2) using case studies to advance the integration of decision analysis into alternatives analysis; (3) supporting transdisciplinary research; and (4) supporting education and outreach efforts.