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Presentation file for KGC 2023

Materials science has had a key role in lowering CO2 emissions from the electricity sector through the development of technologies for renewable energy generation and high-performance energy storage. However, outside of the energy sector, there remain considerable greenhouse gas emissions linked to materials production, particularly due to growth in the built environment infrastructure, transportation and chemicals manufacture. This Review focuses on the challenge of reducing the emissions impact of materials production. We assess the potential for decarbonization in the cement, metals (including steel and aluminium) and chemicals manufacturing industries, including the potential to reduce emissions from the inputs to the production and the transformation processes, as well as through the design of desired outputs. We also address underexplored research areas and outline opportunities for the materials community to reduce emissions by leveraging innovations along length scales from atoms to materials markets. The materials community must address the greenhouse gas emissions burden of materials production. This Review assesses the potential for decarbonization of the cement, metals and petrochemical industries, revealing opportunities to strengthen the connections across industries and length scales — from the atomic scale through to materials markets — to meet climate targets.

Summary Solutions to many of the world's problems depend upon materials research and development. However, advanced materials can take decades to discover and decades more to fully deploy. Humans and robots have begun to partner to advance science and technology orders of magnitude faster than humans do today through the development and exploitation of closed-loop, autonomous experimentation systems. This review discusses the specific challenges and opportunities related to materials discovery and development that will emerge from this new paradigm. Our perspective incorporates input from stakeholders in academia, industry, government laboratories, and funding agencies. We outline the current status, barriers, and needed investments, culminating with a vision for the path forward. We intend the article to spark interest in this emerging research area and to motivate potential practitioners by illustrating early successes. We also aspire to encourage a creative reimagining of the next generation of materials science infrastructure. To this end, we frame future investments in materials science and technology, hardware and software infrastructure, artificial intelligence and autonomy methods, and critical workforce development for autonomous research.

Metal-insulator transition (MIT) compounds are materials that may exhibit insulating or metallic behavior, depending on the physical conditions, and are of immense fundamental interest owing to their potential applications in emerging microelectronics. There is a dearth of thermally-driven MIT materials, however, which makes delineating these compounds from those that are exclusively insulating or metallic challenging. Here we report a material database comprising temperature-controlled MITs (and metals and insulators with similar chemical composition and stoichiometries to the MIT compounds) from high quality experimental literature, built through a combination of materials-domain knowledge and natural language processing. We featurize the dataset using compositional, structural, and energetic descriptors, including two MIT relevant energy scales, an estimated Hubbard interaction and the charge transfer energy, as well as the structure-bond-stress metric referred to as the global-instability index (GII). We then perform supervised classification, constructing three electronic-state classifiers: metal vs non-metal (M), insulator vs non-insulator (I), and MIT vs non-MIT (T). We identify two important descriptors that separate metals, insulators, and MIT materials in a 2D feature space: the average deviation of the covalent radius and the range of the Mendeleev number. We further elaborate on other important features (GII and Ewald energy), and examine how they affect classification of binary vanadium and titanium oxides. We discuss the relationship of these atomic features to the physical interactions underlying MITs in the rare-earth nickelate family. Last, we implement an online version of the classifiers, enabling quick probabilistic class predictions by uploading a crystallographic structure file.

Summary 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.

Redox flow batteries (RFBs) are a promising electrochemical storage solution for power sector decarbonization, particularly emerging long-duration needs. While the battery architecture can host many different redox chemistries, the vanadium RFB (VRFB) represents the current state-of-the-art due to its favorable combination of performance and longevity. However, the relatively high and volatile price of vanadium has hindered VRFB financing and deployment opportunities. Here we evaluate the vanadium supply chain to understand how it enables or constrains VRFB advancement and assess opportunities for accelerated growth. We find that – while vanadium may not be scarce – its abundance is confounded by highly concentrated production coupled with the disperse nature of sources suitable for potential supply increase. These factors challenge rapid growth, limiting deployment rate and magnitude. We estimate gigawatt-hour deployment scales are feasible over the next decade, which would represent marked expansion of the RFB industry and drive down system costs substantially, though this would require growth rates above historical averages. Accordingly, we review opportunities to accelerate supply chain growth and economic strategies to stabilize the market. Finally, we posit terawatt-hour deployment scales will be challenged by vanadium market conditions and, even, resource availability, motivating the continued efforts developing next-generation RFB chemistries.

Leveraging new data sources is a key step in accelerating the pace of materials design and discovery. To complement the strides in synthesis planning driven by historical, experimental, and computed data, we present an automated method for connecting scientific literature to synthesis insights. Starting from natural language text, we apply word embeddings from language models, which are fed into a named entity recognition model, upon which a conditional variational autoencoder is trained to generate syntheses for arbitrary materials. We show the potential of this technique by predicting precursors for two perovskite materials, using only training data published over a decade prior to their first reported syntheses. We demonstrate that the model learns representations of materials corresponding to synthesis-related properties, and that the model's behavior complements existing thermodynamic knowledge. Finally, we apply the model to perform synthesizability screening for proposed novel perovskite compounds., Added new funding support to the acknowledgments section in this version

Establishing the composition-structure-property relationships for amorphous materials is critical for many important natural and engineering processes, including the dissolution of highly complex volcanic glasses. In this investigation, we performed force field molecular dynamics (MD) simulations to generate detailed structural representations for ten natural CaO-MgO-Al2O3-SiO2-TiO2-FeO-Fe2O3-Na2O-K2O glasses with compositions ranging from rhyolitic to basaltic. Based on the resulting atomic structural representations at 300 K, we have calculated the partial radial distribution functions, nearest interatomic distances and coordination number, which are consistent with the literature data on silicate-based glasses. Based on these structural attributes and classical bond valence models, we have introduced a novel structural descriptor, i.e., average metal-oxygen (M-O) bond strength parameter, which has captured the log dissolution rates of the ten glasses at both acidic and basic conditions (based on literature data) with R2 values of ~0.80-0.92 based on linear regression. This structural descriptor is seen to outperform several other structural descriptors also derived from MD simulation results, including the average metal oxide dissociation energy, the average self-diffusion coefficient of all the atoms at their melting points, and the energy barrier of self-diffusion. Furthermore, we showed that the MD-derived descriptors generally exhibit better predictive performance than the degree of depolymerization parameter commonly used to describe glass and mineral reactivity. The results suggest that the structural descriptors derived from MD simulations, especially the average M-O bond strength parameter, are promising structural descriptors for connecting composition with dissolution rates of highly complex natural glasses., 49 pages, 13 figures

Metallic materials have enabled technological progress over thousands of years. The accelerated demand for structural (that is, load-bearing) alloys in key sectors such as energy, construction, safety and transportation is resulting in predicted production growth rates of up to 200 per cent until 2050. Yet most of these materials require a lot of energy when extracted and manufactured and these processes emit large amounts of greenhouse gases and pollution. Here we review methods of improving the direct sustainability of structural metals, in areas including reduced-carbon-dioxide primary production, recycling, scrap-compatible alloy design, contaminant tolerance of alloys and improved alloy longevity. We discuss the effectiveness and technological readiness of individual measures and also show how novel structural materials enable improved energy efficiency through their reduced mass, higher thermal stability and better mechanical properties than currently available alloys. Structural metals enable improved energy efficiency through their reduced mass, higher thermal stability and better mechanical properties; here, methods of improving the sustainability of structural metals, from recycling to contaminant tolerance, are described.

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.

This is a PDF file of an unedited manuscript that has been accepted for publication in Journal of Cleaner Production. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. The final version will be available at: https://doi.org/10.1016/j.jclepro.2019.01.126

Recycling is a key strategy to reduce the environmental impact associated with industrial resource use. Recent improvements in materials recovery technologies offer the possibility for recouping additional value from recycling. However, incorporation of secondary raw materials into production may be constrained by operational complexity in two-stage blending processes. In this paper, we derive an analytical solution to demonstrate the importance of integrated planning (IP) approaches for two-stage blending operations in recycling. Our results suggest that the quality of materials obtained from the first stage strongly influences performance in the second stage. Current disjointed planning (DP) approaches in the recycling industry, where individual stages are independently planned without decision-making about intermediate blend design, overlook this interaction and, therefore, make conservative use of lower quality materials. We develop an IP model using a formulation of the pooling problem and apply it to an industrial-scale aluminum recycling facility located in Europe. The results suggest that the IP model can reduce material costs by more than 5%, for the case examined, and can enable increased use of undervalued raw materials. This study also investigates the impact of variations in operational conditions on the benefits of IP. Keywords: Production; Material recycling; Integrated planning; Two-stage blending process (pooling problem); Design of intermediate products, National Science Foundation (U.S.) (Award 1605050), Fundação para a Ciência e a Tecnologia (Project MITP-TB/PFM/0005/2013)

The use of low-cost feedstocks such as waste cooking oils has gained prominence in biodiesel production due to its potential economic and environmental advantages. Since these feedstocks are derived from multiple sources, its compositional variability has led to quality concerns that may significantly limit its utilization. One potential strategy to address this concern is to use stochastic blending models to optimize the mixing of secondary and primary oils (e.g., palm, canola, or soya). In this paper, we present a stochastic blending model that embeds a second, key source of uncertainty: the future price of feedstocks, a topic of tremendous concern for producers. The stochastic blending model embeds a chance-constrained formulation to account for compositional variability and uses time-series methods to address feedstock price uncertainty. The model was developed to support production-planning decisions to minimize cost and cost variation in biodiesel production. We demonstrate that the proposed approach is useful for determining an optimal planning of feedstock acquisition, blending and storage in order to minimize the risks associated with feedstock price fluctuations. Results show that addressing the compositional uncertainty via the chance-constrained formulation will allow for the use of waste cooking oil in biodiesel blends without compromising their technical performance.

[EN] Organic structure directing agents (OSDAs) play a crucial role in the synthesis of micro- and mesoporous materials especially in the case of zeolites. Despite the wide use of OSDAs, their interaction with zeolite frameworks is poorly understood, with researchers relying on synthesis heuristics or computationally expensive techniques to predict whether an organic molecule can act as an OSDA for a certain zeolite. In this paper, we undertake a data-driven approach to unearth generalized OSDA-zeolite relationships using a comprehensive database comprising of 5,663 synthesis routes for porous materials. To generate this comprehensive database, we use natural language processing and text mining techniques to extract OSDAs, zeolite phases, and gel chemistry from the scientific literature published between 1966 and 2020. Through structural featurization of the OSDAs using weighted holistic invariant molecular (WHIM) descriptors, we relate OSDAs described in the literature to different types of cage-based, small-pore zeolites. Lastly, we adapt a generative neural network capable of suggesting new molecules as potential OSDAs for a given zeolite structure and gel chemistry. We apply this model to CHA and SFW zeolites generating several alternative OSDA candidates to those currently used in practice. These molecules are further vetted with molecular mechanics simulations to show the model generates physically meaningful predictions. Our model can automatically explore the OSDA space, reducing the amount of simulation or experimentation needed to find new OSDA candidates., The authors thank the Spanish Goverment under Awards "Severo Ochoa" (SEV-2016-0683) and RTI2018-101033-BI00 (MCIU/AEI/FEDER, UE) and Generalitat Valenciana under Award AICO/2019/060 for support. We would like to acknowledge partial funding from the National Science Foundation DMREF Awards 1922311, 1922372, and 1922090, the Office of Naval Research (ONR) under contract N00014-20-1-2280, the MIT Energy Initiative, and MIT International Science and Technology Initiatives (MISTI) Seed Funds. Z.J. was supported by the Department of Defense (DoD) through the National Defense Science & Engineering Graduate (NDSEG) Fellowship Program. D.S.-K. was additionally funded by the MIT Energy Fellowship.

REWAS, a sustainability driven conference within The Minerals, Metals & Materials Society (TMS), has a long history of bringing together academia and industry to exchange and reflect on the latest technology developments in the process optimization and waste management fields. The first edition of REWAS (REcycling and WASte symposium) took place in 1999. The scope of the conference has since then broadened to include environmental sustainability, resource management and manufacturing efficiency, liaising these developments to the metallurgical industry in a broader societal and systemic context. The 2022 edition of REWAS which will be hosted at the TMS 2022 Annual Meeting & Exhibition in Anaheim, California, provides a resolute outlook towards Developing Tomorrow’s Technical Cycles. Within the metals and materials industry, technical cycles refer to the ensemble of strategies and processes applied to the development of sustainable product loops with the intent to eliminate waste and instead rethink, reuse and upcycle products. The success of technical cycles requires strengthening our circular approach for product life cycle design by providing guidelines and implementation examples to the developers, designers, policy makers and business managers. REWAS promotes such strategies within a priority sector identified for Circular Economy enablement: raw materials supply and management. REWAS 2022 consists of six symposia, and abstract submissions are expected in summer 2021. Topics include recycling and sustainability within the aluminum industry, specifically on casting technologies, recovery of metals from complex products and systems, decarbonization of the metallurgical and manufacturing industry, sustainable production and development perspectives, as well as automatization and digitalization for advanced manufacturing. REWAS 2022 will also include an honorary symposium for Dr. Diran Apelian, whose contributions in metals processing, aluminum and battery recycling, sustainability, education in materials science and more have shaped the path for sustainable materials processing.

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., Graphical Abstract, Data Analysis; Computing Methodology; Computational Materials Science; Materials Design

Zeolites are versatile catalysts and molecular sieves with large topological diversity, but managing phase competition in zeolite synthesis is an empirical, labor-intensive task. Here, we controlled phase selectivity in templated zeolite synthesis from first principles by combining high-throughput atomistic simulations, literature mining, human-computer interaction, synthesis, and characterization. Proposed binding metrics distilled from over 586,000 zeolite-molecule simulations reproduced the extracted literature and rationalize framework competition in the design of organic structure-directing agents. Energetic, geometric, and electrostatic descriptors of template molecules were found to regulate synthetic accessibility windows and aluminum distributions in pure-phase zeolites. Furthermore, these parameters allowed realizing an intergrowth zeolite through a single bi-selective template. The computation-first approach enabled controlling both zeolite synthesis and structure composition using a priori theoretical descriptors., National Science Foundation (Awards 1922311, 1922372 and 1922090), Office of Naval Research (Contract N00014-20-1-2280)

Climate change will increase the frequency and severity of supply-chain disruptions and large-scale economic crises, also prompting environmentally-protective local policies. Here we use econometric time series analysis, inventory-driven price formation, dynamic material flow analysis, and gate-to-gate life cycle analysis to model the response of each copper supply chain actor to China’s solid waste import ban and the COVID-19 pandemic. We demonstrate that the economic changes associated with China’s solid waste import ban increase primary refining within China, offsetting the environmental benefits of decreased copper scrap refining and generating a cumulative increase in CO2e emissions of up to 35 Mt by 2040. Increasing China’s refined copper imports reverses this trend, decreasing CO2e emissions both in China (up to 300 Mt by 2040) and globally (up to 63 Mt). We test model outcome sensitivity to supply chain disruptions and economic crises using GDP, mining, and refining shocks associated with the COVID-19 pandemic, showing the results maintain impact magnitude alongside disruption effects.

Climate change will increase the frequency and severity of supply chain disruptions and large-scale economic crises, also prompting environmentally protective local policies. Here we use econometric time series analysis, inventory-driven price formation, dynamic material flow analysis, and life cycle assessment to model each copper supply chain actor’s response to China’s solid waste import ban and the COVID-19 pandemic. We demonstrate that the economic changes associated with China’s solid waste import ban increase primary refining within China, offsetting the environmental benefits of decreased copper scrap refining and generating a cumulative increase in CO2-equivalent emissions of up to 13 Mt by 2040. Increasing China’s refined copper imports reverses this trend, decreasing CO2e emissions in China (up to 180 Mt by 2040) and globally (up to 20 Mt). We test sensitivity to supply chain disruptions using GDP, mining, and refining shocks associated with the COVID-19 pandemic, showing the results translate onto disruption effects., Advanced copper supply chain modeling shows China’s new waste trade policy may increase pollution, while limiting other low-value imports reverses this trend. Here the authors show that recycling is vulnerable to supply chain shocks, requiring investment during recoveries to promote a circular economy.

A methodology for calibrating pH meters in highly alkaline solutions such as those relevant to cementitious systems is presented. This methodology uses an extended form of the Debye-Huckel equation to generate a calibration curve of pH vs. the measured electrochemical potential (mV) based on a series of aqueous alkali hydroxide solutions of known concentrations. This methodology is compared with the ‘built-in’ process of calibration based upon pH 4, 7, and 10 standard solutions. The built-in calibration process underestimates the real pH values by up to 0.3 log units, which is attributed to the alkali error. A spreadsheet for determining the calibration curve and its application to pH meter readings is provided as Supporting Information. The implications of improperly calibrated pH meters on interpreting solution chemistry in cementitious systems are discussed.

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.

Life cycle assessments (LCAs) provide valuable guidance regarding environmental implications of design and manufacturing choice; however, they remain resource intensive and time consuming. Streamlining approaches have been developed to address these issues. One of these streamlining approaches, structured under-specification and probabilistic triage, develops a high level assessment of the product or service in question, and only obtains more data on those parameters that contribute most both to the uncertainty and the total impact. In this paper, we build upon the structured under-specification and probabilistic triage methodology and include metrics to determine when sufficient data has been collected. This method enables significant reduction in effort to conduct an LCA while still preserving the ability to make resolvable conclusions around environmental choice related to reducing the impact of manufacturing these devices. We demonstrate the efficiency and effectiveness of this methodology on a case study of tablets for which we determine the burden of the product with 30% of the total effort required in a traditional LCA approach, thereby more readily focusing efforts for impact mitigation. We find that the life cycle environmental impact of the product is driven by the materials and manufacturing phase, more specifically the manufacturing of electronic components such as integrated circuits and printed wiring boards.

Optimization of manufacturing processes and practices requires multiple tradeoffs among often competing priorities. This is especially the case for green manufacturing, where meeting sustainability goals often requires the use of more expensive materials and technologies with uncertain effects on product performance. Not only are decisions regarding such trade-offs difficult to make, these decisions often need to be made with incomplete and uncertain information. These scenarios often result in requests for more information, some of which may be irrelevant for the decision at hand. Value of information (VoI), a decision analytic method for quantifying the expected benefit of acquiring additional information, can be used to improve a wide range of manufacturing decisions. By identifying the contribution of specific model parameter uncertainty to total product or decision uncertainty, VoI can prioritize additional data collection and research strategies to optimally reduce uncertainty and support decisions, i.e., identifying the greatest “bang for the buck.” VoI has been used in many fields including medicine, ecology, and economics, but is rarely used in manufacturing and has never been applied within life cycle assessment (LCA), e.g., to address uncertainty in product development decisions. This paper discusses the use of VoI with LCA in manufacturing and details a case study in which we calculate VoI related to the lifecycle environmental impact of electronic tablet production. We found that LCA-VoI can be successfully used to triage the data gathering process in electronic tablets, to more accurately describe lifecycle environmental impact. We anticipate future applications of LCA-VoI to lead to more cost-effective and sustainable production.

Kevin Huang is a postdoctoral associate in the Department of Materials Science and Engineering at the Massachusetts Institute of Technology (MIT). He studies methods for assessing and enhancing the manufacturing scalability of laboratory-derived clean energy technologies. He has a PhD from MIT, an MS from the University of Illinois, Urbana-Champaign, and a BS from Cornell University, all in materials science and engineering. Liang Li is a graduate student in the Technology and Policy Program at the Massachusetts Institute of Technology. Her current research is focused on the manufacturing scalability of materials for clean energy applications. She holds a bachelor's degree in chemical engineering from the National University of Singapore. Elsa Olivetti is the Atlantic Richfield Associate Professor of Energy Studies in the Department of Materials Science and Engineering at the Massachusetts Institute of Technology. Her research focuses on improving the environmental and economic sustainability of materials using methods informed by materials economics, machine learning, experimental inquiry, and techno-economic analysis. Dr. Olivetti received her BS degree in engineering science from the University of Virginia. Her PhD in materials science and engineering from MIT focused on the development of cathode materials for lithium ion batteries.

Many advanced energy and environmentally relevant technologies rely on metals that have been identified as critical, or whose availability may be limited. Several of these elements are produced mostly as by‐products of mining other base metals (carriers). This by‐product dependence has been proposed as a significant supply‐risk indicator by the materials criticality community. This article provides new quantitative evidence that, in several cases, by‐product metals’ availability may not be directly limited by carrier supply. We perform an assessment based on characteristics essential to by‐product metals, including physical concentration, market value of metals, and extraction technology efficiency. We analyze 40 carrier/by‐product pairs and identify five ‘high‐by‐product’ pairs. We assess the supply responsiveness of these metals. Our analysis suggests that rather than limited primary production of carrier, lack of incentive for improving recovery efficiency may limit availability of the by‐product. This behavior is found in the zinc‐indium and copper‐selenium systems. For germanium, on the other hand, we instead propose influence from the by‐product market itself leading to price inelasticity of supply. As a complement to other quantitative methods developed for material systems, such as material flow analysis, we provide an essential technoeconomic analysis of the by‐product metals problem by employing cluster analysis and econometric modeling. This approach provides insight into supply‐risk mitigation strategies related to extraction efficiency and supply‐chain structure.

Life cycle thinking plays an important role in sustainable development in the building sector. However, the complexity of data collection and scope definition limits life cycle assessment (LCA) applications. Even if the inventory data have already been collected, tabulated, and indexed, the method is still time‐consuming, which may be discouraging for designers. This study demonstrates how the LCA of buildings can be robustly streamlined using structured underspecification of impact data combined with an effective and efficient triage of the data collection. Tests were conducted with a series of building typologies that were analyzed with a cradle‐to‐gate approach. The probabilistic triage approach was tested to identify selected activities requiring detailed specification because they contribute most to total impact, thereby reducing data gathering effort. Impacts such as global warming, acidification, eutrophication, and smog creation were assessed, and results showed that 40% to 46% of the bill of materials components represent 75% of total impacts of single‐family houses and multifamily buildings. By specifying only a prioritized subset of the bill of materials to the highest level of specificity, results proved to be reasonably accurate and obtainable with less effort.