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.
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.
Predictive materials design has rapidly accelerated in recent years with the advent of large-scale resources, such as materials structure and property databases generated by ab initio computations. In the absence of analogous ab initio frameworks for materials synthesis, high-throughput and machine learning techniques have recently been harnessed to generate synthesis strategies for select materials of interest. Still, a community-accessible, autonomously-compiled synthesis planning resource which spans across materials systems has not yet been developed. In this work, we present a collection of aggregated synthesis parameters computed using the text contained within over 640,000 journal articles using state-of-the-art natural language processing and machine learning techniques. We provide a dataset of synthesis parameters, compiled autonomously across 30 different oxide systems, in a format optimized for planning novel syntheses of materials.
Zeolites
are porous, aluminosilicate materials with many industrial
and “green” applications. Despite their industrial relevance,
many aspects of zeolite synthesis remain poorly understood requiring
costly trial and error synthesis. In this paper, we create natural
language processing techniques and text markup parsing tools to automatically
extract synthesis information and trends from zeolite journal articles.
We further engineer a data set of germanium-containing zeolites to
test the accuracy of the extracted data and to discover potential
opportunities for zeolites containing germanium. We also create a
regression model for a zeolite’s framework density from the
synthesis conditions. This model has a cross-validated root mean squared
error of 0.98 T/1000 Å
3
, and many of the model decision
boundaries correspond to known synthesis heuristics in germanium-containing
zeolites. We propose that this automatic data extraction can be applied
to many different problems in zeolite synthesis and enable novel zeolite
morphologies.
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