Projects

Basic Energy Sciences (BES) for Clean Energy Manufacturing

Although they support the storage of renewable energy, current generation batteries are not 'clean' because they rely on extremely inefficient mineral extraction from scarce and controversial resources. More sustainable, earth-abundant battery materials are needed, and this materials design challenge should be informed by the availability of precursor minerals. We demonstrate the scientific basis for 'separation-by-synthesis' by synthesizing a new class of impurity-tolerant high entropy disordered rocksalt (DRX) lithium cathode materials directly from lithium ores. This novel approach to battery material synthesis reactions will bypass nearly all the purification steps that contribute to the current carbon footprint of conventional battery cathode manufacturing. Project personnel:

• discover new battery materials and synthesis pathways using the Materials Project database

• develop and apply deep learning approaches for predicting material properties and reactivity

• optimize materials performance through high entropy engineering

Critical Materials Innovation Hub

Lithium is essential for battery technologies that will sustain the transition to renewable energy sources. We will need 40x more lithium than we've ever extracted to satisfy the global demand for lithium through 2050. Thus, many new sources of lithium will need to be identified, and sustainable methods for extraction and integration into battery supply chains developed. Project personnel:

• discover, characterize, and separate lithium mineral resources

• develop novel methods of lithium extraction

• apply machine learning to increase efficiency of extractive processes

• assess the impact of extractive technologies

Natural Language Processing (NLP) is a powerful machine learning tool for understanding written words in context. When those words describe minerals, there is latent information about the chemistry, structure, and history of the mineral encoded within the context. This general information augments local physical information about the mineral identity and properties that can be leveraged to make decisions about the mineral's value and the impact of extracting or separating critical elements from it. Project personnel:

• develop NLP algorithms to build and analyze large text corpuses of mineralogical literature

• predict chemical properties from written words and test these hypotheses in the laboratory

• associate physical quantities with subjective quantities such as 'value' and 'impact' to help inform critical mineral policies

We develop state-of-the-art methods for the acquisition, processing, and analysis of cryoET data that push the fundamental limits of 3D imaging with machine learning. Project personnel:

• push the limits of 3D imaging resolution in environmentally relevant conditions

• develop new algorithms, routines, and pipelines to increase tomogram quality, throughput, and distribution

• inosculate machine learning with custom reconstruction methods

Crystal formation and assembly are fundamental processes throughout the universe that define the order, and therefore the properties,  of solids. We investigate the mechanisms of crystal nucleation, assembly and growth. Examples include the formation carbonate minerals, nature's reservoir of atmospheric CO2. Project personnel:

• use chemostatic reaction conditions to carefully control driving forces

• apply multimodal characterization including light, X-ray, neutron, and electron scattering and spectroscopy

• develop high-throughput calorimetry and thermochemical modeling approaches to derive fundamental insights into the mechanistic pathways of crystal formation and transformation.

Cryo electron tomography (cryoET) is a powerful technique for imaging hydrated systems in their natural state at atomic and molecular scales. Insights from cryoET reveal mechanisms underlying metabolic processes in cells, including those responsible for controlling the flow of mineral nutrients and influencing extracellular mineralogy. Project personnel:

• apply cryoET to novel microbe mineral systems

• develop methods for cryoET segmentation and 3D visualization to understand microbial behavior in new ways

•  discover new biomineralization phenomena and pathways at the molecular scale