Machine-learning models for analysis of biomass reactions and prediction of reaction energies

Biomass and derived compounds have the potential to form the basis of a sustainable economy by providing a renewable source of many chemicals. The selective synthesis and conversion of biomass compounds are often catalyzed by transition metal catalysts. Computational screening has emerged as a promising tool for discovery and optimization of active and selective catalysts, but most existing examples focus on small molecule reactions. In this study, the density functional theory (DFT) approach is first validated by comparing computational results to experiments for ethanol conversion over molybdenum oxide. Subsequently, DFT is combined with machine-learning approaches to identify and overcome challenges associated with computational screening of biomass catalysts. A recursive algorithm is used to elucidate possible intermediates and chemical bond cleavage reactions are for linear biomass molecules containing up to six carbons. Machine-learning algorithms based on the Mol2Vec embedding are applied to classify reaction types and predict gas-phase reaction energies and adsorption energies on Rh(111) (MAE ~0.4 eV). With the workflow, we are able to combine the physics-based density functional tight binding method with the machine learning model to identify the stable binding geometries of biomass intermediates on the Rh (111) surface. Finally, we show preliminary results toward the development of a neural network force field based on the Gaussian multipole feature approach. The results indicate that this strategy is a promising route toward fast and accurate predictions of both energies and forces of hydrocarbons on a range of transition-metal surfaces. The results of this thesis demonstrate the utility of machine-learning techniques for studying biomass reactions, and indicate the potential for further developments in this field.Ph.D

An efficient graph generative model for navigating ultra-large combinatorial synthesis libraries

Virtual, make-on-demand chemical libraries have transformed early-stage drug discovery by unlocking vast, synthetically accessible regions of chemical space. Recent years have witnessed rapid growth in these libraries from millions to trillions of compounds, hiding undiscovered, potent hits for a variety of therapeutic targets. However, they are quickly approaching a size beyond that which permits explicit enumeration, presenting new challenges for virtual screening. To overcome these challenges, we propose the Combinatorial Synthesis Library Variational Auto-Encoder (CSLVAE). The proposed generative model represents such libraries as a differentiable, hierarchically-organized database. Given a compound from the library, the molecular encoder constructs a query for retrieval, which is utilized by the molecular decoder to reconstruct the compound by first decoding its chemical reaction and subsequently decoding its reactants. Our design minimizes autoregression in the decoder, facilitating the generation of large, valid molecular graphs. Our method performs fast and parallel batch inference for ultra-large synthesis libraries, enabling a number of important applications in early-stage drug discovery. Compounds proposed by our method are guaranteed to be in the library, and thus synthetically and cost-effectively accessible. Importantly, CSLVAE can encode out-of-library compounds and search for in-library analogues. In experiments, we demonstrate the capabilities of the proposed method in the navigation of massive combinatorial synthesis libraries.Comment: 36th Conference on Neural Information Processing Systems (NeurIPS 2022

An Interview on Anita Chang's Calligraphy Courses

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Engineered M13 phage as a novel therapeutic bionanomaterial for clinical applications: From tissue regeneration to cancer therapy

Bacteriophages (phages) are nanostructured viruses with highly selective antibacterial properties that have gained attention beyond eliminating bacteria. Specifically, M13 phages are filamentous phages that have recently been studied in various aspects of nanomedicine due to their biological advantages and more compliant engineering capabilities over other phages. Having nanofiber-like morphology, M13 phages can reach varied target sites and self-assemble into multidimensional scaffolds in a relatively safe and stable way. In addition, genetic modification of the coat proteins enables specific display of peptides and antibodies on the phages, allowing for precise and individualized medicine. M13 phages have also been subjected to novel engineering approaches, including phage-based bionanomaterial engineering and phage-directed nanomaterial combinations that enhance the bionanomaterial properties of M13 phages. In view of these features, researchers have been able to utilize M13 phages for therapeutic applications such as drug delivery, biodetection, tissue regeneration, and targeted cancer therapy. In particular, M13 phages have been utilized as a novel bionanomaterial for precisely mimicking natural tissue environment in order to overcome the shortage in tissue and organ donors. Hence, in this review, we address the recent studies and advances of using M13 phages in the field of nanomedicine as therapeutic agents based upon their characteristics as novel bionanomaterial with biomolecules displayed. This paper also emphasizes the novel engineering approach that enhances M13 phage's bionanomaterial capabilities. Current limitations and future approaches are also discussed to provide insight in further progress for M13 phage-based clinical applications

Highly Hydroxide-Conductive Nanostructured Solid Electrolyte via Predesigned Ionic Nanoaggregates

The creation of interconnected ionic nanoaggregates within solid electrolytes is a crucial yet challenging task for fabricating high-performance alkaline fuel cells. Herein, we present a facile and generic approach to embedding ionic nanoaggregates via predesigned hybrid core–shell nanoarchitecture within nonionic polymer membranes as follows: (i) synthesizing core–shell nanoparticles composed of SiO₂/densely quaternary ammonium-functionalized polystyrene. Because of the spatial confinement effect of the SiO₂ “core”, the abundant hydroxide-conducting groups are locally aggregated in the functionalized polystyrene “shell”, forming ionic nanoaggregates bearing intrinsic continuous ion channels; (ii) embedding these ionic nanoaggregates (20–70 wt %) into the polysulfone matrix to construct interconnected hydroxide-conducting channels. The chemical composition, physical morphology, amount, and distribution of the ionic nanoaggregates are facilely regulated, leading to highly connected ion channels with high effective ion mobility comparable to that of the state-of-the-art Nafion. The resulting membranes display strikingly high hydroxide conductivity (188.1 mS cm^–1 at 80 °C), which is one of the highest results to date. The membranes also exhibit good mechanical properties. The independent manipulation of the conduction function and nonconduction function by the ionic nanoaggregates and nonionic polymer matrix, respectively, opens a new avenue, free of microphase separation, for designing high-performance solid electrolytes for diverse application realms