Zero-Shot Discovery of High-Performance, Low-Cost Organic Battery Materials Using Machine Learning.

Organic electrode materials (OEMs), composed of abundant elements such as carbon, nitrogen, and oxygen, offer sustainable alternatives to conventional electrode materials that depend on finite metal resources. The vast structural diversity of organic compounds provides a virtually unlimited design space; however, exploring this space through Edisonian trial-and-error approaches is costly and time-consuming. In this work, we develop a new framework, SPARKLE, that combines computational chemistry, molecular generation, and machine learning to achieve zero-shot predictions of OEMs that simultaneously balance reward (specific energy), risk (solubility), and cost (synthesizability).

Understanding Rapid PET Degradation via Reactive Molecular Dynamics Simulation and Kinetic Modeling.

As the demand for PET plastic products continues to grow, developing effective processes to reduce their pollution is of critical importance. Pyrolysis, a promising technology to produce lighter and recyclable components from wasted plastic products, has therefore received considerable attention. In this work, the rapid pyrolysis of PET was studied by using reactive molecular dynamics (MD) simulations.

Predicting the Solubility of Organic Energy Storage Materials Based on Functional Group Identity and Substitution Pattern.

Organic electrode materials (OEMs) provide sustainable alternatives to conventional electrode materials based on transition metals. However, the application of OEMs in lithium-ion and redox flow batteries requires either low or high solubility. Currently, the identification of new OEM candidates relies on chemical intuition and trial-and-error experimental testing, which is costly and time intensive.

Deep learning for characterizing the self-assembly of three-dimensional colloidal systems.

Creating a systematic framework to characterize the structural states of colloidal self-assembly systems is crucial for unraveling the fundamental understanding of these systems' stochastic and non-linear behavior. The most accurate characterization methods create high-dimensional neighborhood graphs that may not provide useful information about structures unless these are well-defined reference crystalline structures. Dimensionality reduction methods are thus required to translate the neighborhood graphs into a low-dimensional space that can be easily interpreted and used to characterize non-reference structures.

Ultrasensitive and Selective Detection of SARS-CoV-2 Using Thermotropic Liquid Crystals and Image-Based Machine Learning.

Rapid, robust virus-detection techniques with ultrahigh sensitivity and selectivity are required for the outbreak of the pandemic coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). Here, we report that the femtomolar concentrations of single-stranded ribonucleic acid (ssRNA) of SARS-CoV-2 trigger ordering transitions in liquid crystal (LC) films decorated with cationic surfactant and complementary 15-mer single-stranded deoxyribonucleic acid (ssDNA) probe. More importantly, the sensitivity of the LC to the SARS ssRNA, with a 3-bp mismatch compared to the SARS-CoV-2 ssRNA, is measured to decrease by seven orders of magnitude, suggesting that the LC ordering transitions depend strongly on the targeted oligonucleotide sequence.

Fast uncertainty quantification for dynamic flux balance analysis using non-smooth polynomial chaos expansions.

We present a novel surrogate modeling method that can be used to accelerate the solution of uncertainty quantification (UQ) problems arising in nonlinear and non-smooth models of biological systems. In particular, we focus on dynamic flux balance analysis (DFBA) models that couple intracellular fluxes, found from the solution of a constrained metabolic network model of the cellular metabolism, to the time-varying nature of the extracellular substrate and product concentrations. DFBA models are generally computationally expensive and present unique challenges to UQ, as they entail dynamic simulations with discrete events that correspond to switches in the active set of the solution of the constrained intracellular model.

An Analytical Solution for Exciton Generation, Reaction, and Diffusion in Nanotube and Nanowire-Based Solar Cells.

Excitonic solar cells based on aligned or unaligned networks of nanotubes or nanowires offer advantages with respect of optical absorption, and control of excition and electrical carrier transport; however, there is a lack of predictive models of the optimal orientation and packing density of such devices to maximize efficiency. Here-in, we develop a concise analytical framework that describes the orientation and density trade-off on exciton collection computed from a deterministic model of a carbon nanotube (CNT) photovoltaic device under steady-state operation that incorporates single- and aggregate-nanotube photophysics published earlier (Energy Environ Sci, 2014, 7, 3769). We show that the maximal film efficiency is determined by a parameter grouping, α, representing the product of the network density and the effective exciton diffusion length, reflecting a cooperativity between the rate of exciton generation and the rate of exciton transport.

Layer number dependence of MoS2 photoconductivity using photocurrent spectral atomic force microscopic imaging.

Atomically thin MoS2 is of great interest for electronic and optoelectronic applications because of its unique two-dimensional (2D) quantum confinement; however, the scaling of optoelectronic properties of MoS2 and its junctions with metals as a function of layer number as well the spatial variation of these properties remain unaddressed. In this work, we use photocurrent spectral atomic force microscopy (PCS-AFM) to image the current (in the dark) and photocurrent (under illumination) generated between a biased PtIr tip and MoS2 nanosheets with thickness ranging between n = 1 to 20 layers. Dark current measurements in both forward and reverse bias reveal characteristic diode behavior well-described by Fowler-Nordheim tunneling with a monolayer barrier energy of 0.