Combinatorial and High-Throughput Materials Synthesis to Advanced Nuclear Materials Discovery
Poster for end of LDRD project
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Poster for end of LDRD project
A general-purpose, featurization-agnostic toolkit, MatFold, is provided to automate reproducible generation of cross-validation splits to assess out-of-distribution generalization and encourage further community use in materials model benchmarking.
The past 50 years of discovery in organic electronics have been driven in large part by the donor–acceptor design principle, wherein electron-rich and electron-poor units are assembled in conjugation with each other to produce small band gap materials. While the utility of this design strategy is undoubtable, it has been largely exhausted as a frontier of new avenues to produce and tune novel functional materials to meet the needs of the ever-increasing world of organic electronics applications. Its sister strategy of joining quinoidal and aromatic groups in conjugation has, by comparison, received much less attention, to a great extent due to the categorically poor stability of quinoidal conjugated motifs. In 2017 though, the p-azaquinodimethane (AQM) motif was first unveiled, which showed a remarkable level of stability despite being a close structural analogue to p-quinodimethane, a notably reactive compound. In contrast, dialkoxy AQM small molecules and polymers are stable even under harsh conditions and could thus be incorporated into conjugated polymers. When polymerized with aromatic subunits, these AQM-based polymers show notably reduced band gaps that follow reversed structure–property trends to some of their donor–acceptor polymer counterparts and yield organic field-effect transistor (OFET) hole mobilities above 5 cm 2 V –1 s –1 . Additionally, in an ongoing study, these AQM-based compounds are also showing promise as singlet fission (SF) active materials due to their mild diradicaloid character. An expanded world of AQMs was accessed through their ditriflate derivatives, which were first used to produce ionic AQMs (iAQMs) sporting two directly attached cationic groups that significantly affect the AQM motif’s electronics, producing strongly electron-withdrawing quinoidal building blocks. Conjugated polyelectrolytes (CPEs) created with these iAQM building blocks exhibit optical band gaps stretching into the near-infrared I (NIR-I) region and showed exemplary behavior as photothermal therapy agents. In contrast to these stable AQM examples, the synthetic exploration of AQMs also produced examples of more typical diradicaloid reactivity but in forms that were controllable and produced intriguing and high-value products. With certain substitution patterns, AQMs were found to dimerize to form highly substituted [2.2]paracyclophanes in distinctly more appreciable yields than typical cyclophane formation reactions. Certain AQM ditriflates, when crystallized, undergo light-induced topochemical polymerization to form ultrahigh molecular weight (>10 6 Da) polymers that showed excellent performances as dielectric energy storage materials. These same AQM ditriflates could be used to produce the strongly electron-donating redox-active pentacyclic structure: pyrazino[2,3-b:5,6-b']diindolizine (PDIz). Importantly, the PDIz motif allowed for the synthesis of exceedingly small band gap (0.7 eV) polymers with absorbances reaching all the way into the NIR-II region that were also found to produce strong photothermal effects. Both as stable quinoidal building blocks and through their controllable diradicaloid reactivity, AQMs have already proven to be versatile and effective as functional organic electronics materials.
Reversible solid oxide cells (R-SOCs) are highly efficient devices for energy conversion and storage, capable of operating for both hydrogen utilization and production. In fuel cell mode, an R-SOC consumes hydrogen or natural gas to generate electricity, while in electrolysis mode, it produces hydrogen from steam. The discover of new materials with rapid oxygen surface exchange kinetics and enduring stability is crucial for the economically viable commercialization of R-SOCs. To facilitate this pursuit, we conducted extensive Density Functional Theory (DFT) calculations and developed Machine Learning (ML) models to predict critical catalytic properties essential for R-SOCs, such as oxygen surface exchange/diffusivity, and area-specific resistance (ASR). BaCoxFeyZrzO3-d(BFCZ)(x+y+z=1) emerged as a promising family of electrode materials with high activity and stability, validated through systematic experimental study. Moreover, a robust numerical multiphysics model was developed to optimize materials and microstructure parameters, providing the ability to predict the performance of functional R-SOCs.
Lithium-ion batteries are essential for portable technology and are now poised to disrupt a century of combustion-based transportation. The electrification revolution could eliminate our reliance on fossil fuels and enable a clean energy future; advanced batteries would facilitate this transition. However, owing to the demanding performance, cost, and safety requirements, it is challenging to translate new materials from laboratory prototypes to commercial products. This Perspective describes that journey toward commercialization for a new lithium-ion battery anode material, TiNb2O7 (TNO). TNO is intended as an alternative to graphite or Li4Ti5O12 with better rate and safety characteristics than the former, and higher energy density than the latter. The high capacity of TNO stems from multielectron redox of Nb5+ to Nb3+, its operating voltage window well above Li+/Li reduction potential prevents lithium dendrite formation, and its open crystal structure leads to high-power performance. Nevertheless, the creation of a practical TNO anode was non-linear and non-trivial. Its history is built on 30 years on fundamental science that preceded its application as a battery, and its battery development included a nearly 30-year gap. The insights and lessons 2 contained in this Perspective, many of them acquired firsthand, serve two purposes: (i) to unite the disparate studies of TiNb2O7 into a coherent modern understanding relevant to its application as a battery material and (ii) to highlight some of the considerations of commercializing a new material that affect TiNb2O7 as well as new electrode candidates more generally.
Abstract Aqueous organic redox flow batteries offer an environmentally benign, tunable, and safe route to large-scale energy storage. The energy density is one of the key performance parameters of organic redox flow batteries, which critically depends on the solubility of the redox-active molecule in water. Prediction of aqueous solubility remains a challenge in chemistry. Recently, machine learning models have been developed for molecular properties prediction in chemistry and material science. The fidelity of a machine learning model critically depends on the diversity, accuracy, and abundancy of the training datasets. We build a comprehensive open access organic molecular database “Solubility of Organic Molecules in Aqueous Solution” (SOMAS) containing about 12,000 molecules that covers wider chemical and solubility regimes suitable for aqueous organic redox flow battery development efforts. In addition to experimental solubility, we also provide eight distinctive quantum descriptors including optimized geometry derived from high-throughput density functional theory calculations along with six molecular descriptors for each molecule. SOMAS builds a critical foundation for future efforts in artificial intelligence-based solubility prediction models.
Machine learning can be used to screen millions of compounds and unearth relationships between electronic structure, chemistry, thermodynamic stability, formability and band gap. We know how to calculate thermodynamic stability and calculate approximate band gap, but it is very complicated to estimate synthesizability.
Here, we report the design, construction, and automation of a flat plate sample loading, alignment, and data acquisition system for X-ray diffraction measurements in reflection geometry implemented at the Stanford Synchrotron Radiation Lightsource. The system is built onto a single platform, enabling facile transferability, and is compartmentalized into sample storage, sample transfer, and sample position/alignment segments. The core feature of this system is a six-axis robotic arm that offers a large range of highly reproducible and programable movements. The degrees of freedom of the robot arm enable adaptability in which movements can be modified to fit various beamline environments and sample configurations. Samples are housed on 3D printed sample mounts, which are arranged onto a 6 × 2 array of sample cassettes capable of holding 7 samples. Using sample mounts designed for solid oxide electrolysis button cells (SOECs), the maximum tray capacity is 84 samples, which can be aligned and run in ~ 24 hours with long exposure scans. The sample array is additionally capable of accommodating a range of sample sizes and geometries due to the rapid 3D printed fabrication. The components of the setup will be described in detail and performance will be demonstrated with a set of representative SOEC and XRD standard samples. Opportunities for future developments and integration with the automated setup are summarized.
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Glass-crystallisation synthesis is harnessed to probe structure prediction in a search for new inorganic compounds, yielding a new ribbon silicate Sr 2 Si 3 O 8 .
Abstract not provided
Machine learning library for developing scientific transformer models, particularly chemical language models, at scale
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