Optimization-based Approaches for Design of Chemical Process Families Using ReLU Surrogates
This conference paper was presented at the 2023 FOCAPO/CPC conference.
Stinchfield, Georgia↗
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Optimization-based Approaches for Design of Chemical Process Families Using ReLU Surrogates.
Abstract not provided.
Stinchfield, Georgia↗
Superior Capacitive Energy-Storage Performance in Pb-Free Relaxors with a Simple Chemical Composition
Chemical design of lead-free relaxors with simultaneously high energy density (W rec ) and high efficiency (η) for capacitive energy-storage has been a big challenge for advanced electronic systems. The current situation indicates that realizing such superior energy-storage properties requires highly complex chemical components. Herein, we demonstrate that, via local structure design, an ultrahigh W rec of 10.1 J/cm 3 , concurrent with a high η of 90%, as well as excellent thermal and frequency stabilities can be achieved in a relaxor with a very simple chemical composition. By introducing 6s 2 lone pair stereochemical active Bi into the classical BaTiO 3 ferroelectric to generate a mismatch between A- and B-site polar displacements, a relaxor state with strong local polar fluctuations can be formed. Through advanced atomic-resolution displacement mapping and 3D reconstructing the nanoscale structure from neutron/X-ray total scattering, it is revealed that the localized Bi enhances the polar length largely at several perovskite unit cells and disrupts the long-range coherent Ti polar displacements, resulting in a slush-like structure with extremely small size polar clusters and strong local polar fluctuations. This favorable relaxor state exhibits substantially enhanced polarization, and minimized hysteresis at a high breakdown strength. This work offers a feasible avenue to chemically design new relaxors with a simple composition for high-performance capacitive energy-storage.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Ion transport through reconfigurable nanoparticle-surfactant stabilized droplet interface bilayers
Despite their adaptability and mechanical stability, Pickering emulsions based on the interfacial assembly of colloidal particles have not found use in iontronics, since the dense interfacial packing of micron-sized particles precludes functional connectivity between two droplets. In this work, we introduce a chemically reconfigurable droplet interface bilayer (DIB) platform based on the interfacial assembly of nanoparticle-surfactants (NPSs) that enables spontaneous or field-induced formation of ion-conducting nanochannels, eliminating the need of ionophores or nanochannel-forming proteins. These nanoscopic channels emerge from packing defects in the jammed interfacial assemblies of the charged NPSs and support size and charge selective, hysteretic ion transport governed by interfacial electrostatics and dimensional constraints. The NPS-DIBs show short-term and long-term plasticity, hallmarks of neuromorphic behavior, that are mediated by the structural and chemical design of the bilayer. These assemblies establish a versatile, chemically tunable platform that couples soft-matter mechanics with interfacial ionic functionality, offering a robust foundation for soft iontronic systems.
36 MATERIALS SCIENCE↗
Human Mars Mission: Weights and Mass Properties
This paper presents a final report on The Human Mars Mission Weights and Measures. The topics included in this report are: 1) Trans-Earth Injection Storage Human Mars Mission (HMM) Chemical Design Reference Mission (DRM) v4.0a Weight Breakout; 2) Ascent Stage HMM Chemical DRM v4.0a Weight Breakout; 3) Descent Stages HMM Chemical DRM v4.0a Weight Breakout; 4) Trans-Mars Injection Stages HMM Chemical DRM v4.0a Weight Breakout; 5) Trans-Earth Injection Stage HMM Solar Electric Propulsion (SEP) DRM v4.0a Weight Breakout; 6) Ascent Stage HMM SEP DRM v4.0a Weight Breakout; 7) Descent Stages HMM SEP DRM v4.0a Weight Breakout; 8) Trans-Mars Injection Stages HMM SEP DRM v4.0a Weight Breakout; 9) Crew Taxi Stage HMM SEP DRM v4.0 Weight Breakout; 10)Trans-Earth Injection Stage HMM Nuclear DRM v4.0a Weight Breakout; 11) Ascent Stage HMM Nuclear DRM v4.0a Weight Breakout; 12) Descent Stages HMM Nuclear DRM v4.0a Weight Breakout; 13) Trans-Mars Injection Stages HMM Nuclear DRM v4.0a Weight Breakout; and 14) HMM Mass Properties Coordinate System.
Brothers, Bobby↗
Recent trends in all-organic polymer dielectrics for high-temperature electrostatic energy storage capacitors
Electrostatic energy storage (EES) capacitors are critical for renewable energy and high-power systems, driving the search for dielectric materials that combine superior electrical insulation, mechanical flexibility, low density, cost-effectiveness, and processability. Polymer-based dielectrics have emerged as leading candidates, particularly for high electric field applications. However, conventional polymers often fail to meet the demands of high-temperature environments due to increased electrical conductivity and reduced discharged energy density at elevated temperatures, resulting in energy loss and reduced performance. High glass transition temperature (T g) polymers show promise but require further optimization to enhance their energy storage capabilities under thermal and electrical stress. This review provides a comprehensive update on recent advancements in high-T g polymer-based dielectrics for EES capacitors, focusing on both intrinsic polymers and all-organic composites. It outlines key design principles, critical performance parameters, and innovative strategies—such as nanofiller doping, layered architectures, physical blending, and chemical crosslinking—to improve electrical, thermal, and mechanical properties. The review also highlights emerging trends, including the integration of machine learning algorithms to explore novel polymer structures and expand the chemical design space. By bridging the gap between academic research and industrial application, this review aims to accelerate the development of next-generation dielectric materials capable of balancing multiple performance metrics for high-temperature EES capacitors.
Xie, Zongliang↗
Deep learning to generate in silico chemical property libraries and candidate molecules for small molecule identification in complex samples
Comprehensive and unambiguous identification of small molecules in complex samples will revolutionize our understanding of the role of metabolites in biological systems. Existing and emerging technologies have enabled measurement of chemical properties of molecules in complex mixtures and, in concert, are sensitive enough to resolve even stereoisomers. Despite these experimental advances, small molecule identification is inhibited by (i) chemical reference libraries (e.g. mass spectra, collision cross section, and other measurable property libraries) representing <1% of known molecules, limiting the number of possible identifications, and (ii) the lack of a method to generate candidate matches directly from experimental features (i.e. without a library). To this end, we developed a variational autoencoder (VAE) to learn a continuous numerical, or latent, representation of molecular structure to expand reference libraries for small molecule identification. We extended the VAE to include a chemical property decoder, trained as a multitask network, in order to shape the latent representation such that it assembles according to desired chemical properties. The approach is unique in its application to metabolomics and small molecule identification, with its focus on properties that can be obtained from experimental measurements (m/z, CCS) paired with its training paradigm, which involved a cascade of transfer learning iterations. First, molecular representation is learned from a large dataset of structures with m/z labels. Next, in silico property values are used to continue training, as experimental property data is limited. Finally, the network is further refined by being trained with the experimental data. This allows the network to learn as much as possible at each stage, enabling success with progressively smaller datasets without overfitting. Once trained, the network can be used to predict chemical properties directly from structure, as well as generate candidate structures with desired chemical properties. Our approach is orders of magnitude faster than first-principles simulation for CCS property prediction. Additionally, the ability to generate novel molecules along manifolds, defined by chemical property analogues, positions DarkChem as highly useful in a number of application areas, including metabolomics and small molecule identification, drug discovery and design, chemical forensics, and beyond.
Colby, Sean M.↗
Design principles of heterointerfacial redox chemistry for highly reversible lithium metal anode
High electrochemical reversibility is required for the application of high-energy-density lithium (Li) metal batteries; however, inactive Li formation and SEI (solid electrolyte interface)-instability-induced electrolyte consumption cause low Coulombic efficiency (CE). The prior interfacial chemical designs in terms of alloying kinetics have been used to enhance the CE of Li metal anode; however, the role of its redox chemistry at heterointerfaces remains a mystery. Herein, the relationship between heterointerfacial redox chemistry and electrochemical transformation reversibility is investigated. It is demonstrated that the lower redox potential at heterointerface contributes to higher CE, and this enhancement in CE is primarily due to the regulation of redox chemistry to Li deposition behavior rather than the formation of SEI films. Low oxidation potential facilitates the formation of the surface with the highly electrochemical binding feature after Li stripping, and low reduction potential can maintain binding ability well during subsequent Li plating, both of which homogenize Li deposition and thus optimize CE. In particular, Mg hetero-metal with ultra-low redox potential enables Li metal anode with significantly improved CE (99.6%) and stable cycle life for 700 cycles at 3.0 mA cm -2 . This work provides insight into the heterointerfacial design principle of next-generation negative electrodes for highly reversible metal batteries.
25 ENERGY STORAGE↗
The Role of Water Volume Fraction on Water Adsorption in Anion Exchange Membranes
Water absorption plays a key role in the performance of polymeric anion exchange membranes. It influences important properties such as ionic conductivity and mechanical strength and alters their performance as solid electrolytes in hydrogen electrochemical devices for energy conversion. However, computational approaches that address the relationship between the polymer design and the absorption process are scarce. In this work, we introduce a simple thermodynamic model to predict the water absorption isotherms of polyelectrolyte membranes in contact with a water vapor reservoir that incorporates the specific chemical design of the polymers. The model accurately predicts the water content and macrostructural properties of polynorbornene membranes as a function of the water activity and successfully captures the effect of various polymer design parameters. The energy of pairwise attractive interactions predicted by our model provides a means to interpret the absorption process at the molecular level. The model also reveals the most significant favorable and unfavorable contributions to the free energy and indicates that their balance is solely governed by the water volume fraction, regardless of the polymer design. This universal behavior leads to important implications in the search for better ion exchange membranes.
Zaldivar, Gervasio [New York University, Brooklyn,↗
Discovery of unconventional and nonintuitive self-assembling peptide materials using experiment-driven machine learning
Prediction of peptide secondary structure is challenging because of complex molecular interactions, sequence-specific behavior, and environmental factors. Traditional design strategies, based on hydrophobicity and structural propensity, can be biased and could indeed prevent discovery of interesting, diverse, and unconventional peptides with desired nanostructure assembly. Using β sheet formation in pentapeptides as a case study, we used an integrated high-throughput experimental workflow and an artificial intelligence–driven active learning framework to improve prediction accuracy of self-assembly. By focusing on sequences where machine learning (ML) predictions deviate from conventional design strategies, we synthesized and tested 268 pentapeptides, successfully finding 96 forming β sheet assemblies, including unconventional sequences (e.g., ILFSM, LMISI, MITIY, MISIW, and WKIYI) not predicted by traditional methods. Our ML models outperformed conventional β sheet propensity tables, revealing useful chemical design rules. A web interface is provided to facilitate community access to these models. This work highlights the value of ML-driven approaches in overcoming the limitations of current peptide design strategies.
Talluri, Y. Nissi [Indian Inst. of Technology (IIT↗
Multiscale Characterization of the Influence of the Organic–Inorganic Interface on the Dielectric Breakdown of Nanocomposites
Nanoscale engineered materials such as nanocomposites can display or be designed to enhance their material properties through control of the internal interfaces. In this report we unveil the nanoscale origin and important characteristics of the enhanced dielectric breakdown capabilities of gold nanoparticle/polymer nanocomposites. Our multiscale approach spans from the study of a single chemically designed organic/inorganic interface to micrometer-thick films. At the nanoscale, we relate the improved breakdown strength to the interfacial charge retention capability by combining scanning probe measurements and density functional theory calculations. At the meso- and macroscales, our findings highlight the relevance of the nanoparticle concentration and distribution in determining and enhancing the dielectric properties, as well as identifying this as a crucial limiting factor for the achievable sample size.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Carbon-Assisted, Continuous Syngas Production in a Chemical Looping Scheme
In the current energy and environment scenario, it is imperative to develop energy efficient routes for chemical manufacturing that also pave the way for mitigation of greenhouse gas emissions. This work presents an efficient pathway for continuous syngas production via a chemical looping conversion of the two most potent greenhouse gases—CH 4 , and CO 2 . The well-known dry-reforming process of converting CH 4 , and CO 2 to syngas is energy-intensive and suffers from catalyst deactivation. The chemical looping approach, on the other hand, provides avenues for mitigating catalyst deactivation and enabling improved energy efficiency. The key to such process enhancements lies in the intricate structure–function relationships of the catalyst and its correlation to the process variables. We present the reduction and oxidation characteristics of 5 wt.% Ni/Ce 1-x Zr x O 2 -based catalysts (x = 0, 0.4, and 0.625). We demonstrate low temperature CH4 activation over Ni-promoted samples as opposed to pure Ce 1-x Zr x O 2 . Moreover, our results depict an optimum regeneration of these catalysts when oxidized by CO 2 , and H 2 O, which allows for chemical looping operation of steam reforming of methane as well. Process variables were tuned to optimize the CH 4 conversion (over 80%), and H 2 /CO ratio at 650 °C. The critical surface reactions—carbon accumulation and gasification, and thermocatalytic CO 2 splitting were investigated to elucidate the dynamic nature of the catalyst surface. In conclusion, the impact of this work lies in showcasing the opportunities to design chemical looping reactors for energy efficient syngas production from waste greenhouse gases.
25 ENERGY STORAGE↗
Light-Driven Expansion of Spiropyran Hydrogels
The incorporation of molecular switches in organic structures is of great interest in the chemical design of stimuli-responsive materials that mimic the complex functions of living systems. Merocyanine dyes that convert to spiropyran moieties upon exposure to visible light have been extensively studied as they can be incorporated in hydrated covalent networks that will expel water when this conversion occurs and induce a volumetric shrinkage. We report here on a sulfonatebased water soluble photoswitch that in contrast to the well-known system triggers a volumetric expansion in hydrogels upon exposure to photons. Contraction is in turn observed under dark conditions in a highly reversible manner. The novel behavior of the photoswitch incorporated in the covalent network was also predicted by coarse grain simulations of the system’s chemical structure. Using pH control and polymeric structures that differ in lower critical solution temperature we were able to develop hydrogels with highly tunable volumetric expansion. Furthermore, the novel molecular function of the systems developed here led to materials with the negative phototaxis observed in plants, and can expand the potential use of hydrogels as sensors, soft robots, and actuators.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Precise Control of Noncovalent Interactions in Semiconducting Polymers for High-Performance Organic Field-Effect Transistors
Performed through side-chain engineering or by incorporating intramolecular locking units, the directionality and dynamic nature of noncovalent interactions are particularly attractive for the design of novel semiconducting materials in a wide variety of applications. This work investigates the nature and position of hydrogen bonding (intra- versus intermolecular), with the objective of developing a rational approach to the design of new semiconducting materials with improved properties in the solid state. To control the polymer chains’ self-assembly, a π-conjugated polymer incorporating a moiety capable of generating intramolecular hydrogen bonding is evaluated against a polymer that allows for intermolecular hydrogen bonding. Characterization through various techniques, optical spectroscopies, grazing incidence wide-angle x-ray scattering, and solution small-angle neutron scattering showed that intramolecular hydrogen bonds resulted in materials with improved crystallinity and higher effective conjugation in the solid state. Additionally, the effect of the noncovalent interaction configuration on the optoelectronic properties was analyzed in organic field-effect transistor fabrication. Devices prepared from the materials with intramolecular hydrogen bonds showed significantly higher performance, with three orders of magnitude higher charge mobility than their counterparts fabricated from polymers with intermolecular hydrogen bonds. These results confirm the importance of chemical design on polymer structures and offer a novel route for the design of high-efficiency semiconducting polymers for next-generation electronics.
36 MATERIALS SCIENCE↗
Tunable Solid‐State Properties and Anisotropic Charge Mobility in Hydrogen‐Bonded Diketopyrrolopyrrole Polymers via Automated Device Fabrication and Characterization
The optoelectronic properties of semiconducting polymers and device performance rely on a delicate interplay of design and processing conditions. However, screening and optimizing the relationships between these parameters for reliably fabricating organic electronics can be an arduous task requiring significant time and resources. To overcome this challenge, Polybot is developed—a robotic platform within a self‐driving lab that can efficiently produce organic field‐effect transistors (OFETs) from various semiconducting polymers via high‐throughput blade coating deposition. Polybot not only handles the fabrication process but also can conduct characterization tests on the devices and autonomously analyze the data gathered, thus facilitating the rapid acquisition of data on a large scale. This work leverages the capabilities of this platform to investigate the fabrication of OFETs using hydrogen bonding‐containing semiconducting polymers. Through high‐throughput fabrication and characterization, various data trends are analyzed, and large extents of anisotropic charge mobility are observed in devices. The materials are thoroughly characterized to understand the role of processing conditions in solid state and electronic properties of these organic semiconductors. The findings demonstrate the effectiveness of automated fabrication and characterization platforms in uncovering novel structure–property relationships, facilitating refinement of rational chemical design, and processing conditions, ultimately leading to new semiconducting materials.
automated fabrication and characterization↗
Electronic Spin Qubit Candidates Arrayed within Layered Two-Dimensional Polymers
Molecular electronic spin qubits are promising candidates for quantum information science applications because they can be reliably produced and engineered via chemical design. Embedding electronic spin qubits within two-dimensional polymers (2DPs) offers the possibility to systematically engineer inter-qubit interactions while maintaining long coherence times, both of which are prerequisites to their technological utility. Here, we introduce electronic spin qubits into a diamagnetic 2DP by n-doping naphthalene diimide subunits with varying amounts of CoCp 2 and analyze their spin densities by quantitative electronic paramagnetic resonance spectroscopy. Low spin densities (e.g., 6.0 × 10 12 spins mm –3 ) enable lengthy spin–lattice (T 1 ) and spin–spin relaxation (T 2 ) times across a range of temperatures, ranging from T 1 values of 164 ms at 10 K to 30.2 μs at 296 K and T 2 values of 2.36 μs at 10 K to 0.49 μs at 296 K for the lowest spin density sample examined. Higher spin densities and temperatures were both found to diminish T 1 times, which we attribute to detrimental cross-relaxation from spin–spin dipolar interactions and spin–phonon coupling, respectively. Higher spin densities decreased T 2 times and modulated the T 2 temperature dependence. Here, we attribute these differences to the competition between hyperfine and dipolar interactions for electron spin decoherence, with the dominant interaction transitioning from the former to the latter as spin density and temperature increase. Overall, this investigation demonstrates that dispersing electronic spin qubits within layered 2DPs enables chemical control of their inter-qubit interactions and spin decoherence times.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Intrinsic Ion Transport Properties of Block Copolymer Electrolytes
Knowledge of intrinsic properties is of central importance for materials design and assessing suitability for specific applications. Self-assembling block copolymer electrolytes (BCEs) are of great interest for applications in solid-state energy storage devices. A fundamental understanding of ion transport properties, however, is hindered by the difficulty in deconvoluting extrinsic factors, such as defects, from intrinsic factors, such as the presence of interfaces between the domains. Here, we quantify the intrinsic ion transport properties of a model BCE system consisting of poly(styrene-block-ethylene oxide) (SEO) and lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) salt using a generalizable strategy of depositing thin films on interdigitated electrodes and self-assembling fully connected parallel lamellar structures throughout the films. Comparison between conductivity in homopolymer poly(ethylene oxide) (PEO)-LiTFSI electrolytes and the analogous conducting material in SEO over a range of salt concentrations (r, molar ratio of lithium ion to ethylene oxide repeat units) and temperatures reveals that between 20% and 50% of the PEO in SEO is inactive. Using mean-field theory calculations of the domain structure and monomer concentration profiles at domain interfaces-both of which vary substantially with salt concentration-the fraction of inactive PEO in the SEO, as derived from conductivity measurements, can be quantitatively reconciled with the fraction of PEO that is mixed with greater than a few volume percent of polystyrene. Despite the detrimental interfacial effects for ion transport in BCEs, the intrinsic conductivity of the SEO studied here (ca. 10 -3 S/cm at 90 degrees C, r = 0.085) is an order of magnitude higher than reported values from bulk samples of similar molecular weight SEO (ca. 10 -4 S/cm at 90 degrees C, r = 0.085). Finally, this work provides motivation and methods for pursuing improved BCE chemical design, interfacial engineering, and processing.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Computational Workflow for Accelerated Molecular Design Using Quantum Chemical Simulations and Deep Learning Models
Efficient methods for searching the chemical space of molecular compounds are needed to automate and accelerate the design of new functional molecules such as pharmaceuticals. Given the high cost in both resources and time for experimental efforts, computational approaches play a key role in guiding the selection of promising molecules for further investigation. Here, we construct a workflow to accelerate design by combining approximate quantum chemical methods [i.e. density-functional tight-binding (DFTB)], a graph convolutional neural network (GCNN) surrogate model for chemical property prediction, and a masked language model (MLM) for molecule generation. Property data from the DFTB calculations are used to train the surrogate model; the surrogate model is used to score candidates generated by the MLM. The surrogate reduces computation time by orders of magnitude compared to the DFTB calculations, enabling an increased search of chemical space. Furthermore, the MLM generates a diverse set of chemical modifications based on pre-training from a large compound library. We utilize the workflow to search for near-infrared photoactive molecules by minimizing the predicted HOMO-LUMO gap as the target property. Our results show that the workflow can generate optimized molecules outside of the original training set, which suggests that iterations of the workflow could be useful for searching vast chemical spaces in a wide range of design problems.
Blanchard, Andrew↗