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At least 127 records · Page 7

Building Toward the Future in Chemical and Materials Simulation with Accessible and Intelligently Designed Web Applications

Over the last few decades, significant progress has been made in the development and use of electronic structure and other molecular simulation methods. As these methods become more mature and are able to simulate larger and more complex chemical simulations, the need for improvement in scientific visualization, molecular builders, simplified input to simulation methods, and the development of new approaches and languages to describe simulations, along with workflows to carry them out, becomes more apparent. In this chapter, we describe our recent efforts in developing a prototype open-source computational tool called Arrows that combines NWChem, SQL and NoSQL databases, email, web APIs, and web applications in a way that make molecular and materials modeling accessible to all scientists and engineers. At the same time, because of its simplified input, it provides a framework for expert users to carry out large numbers of calculations and run complex workflows.

Advances in complex oxide quantum materials through new approaches to molecular beam epitaxy

Abstract Molecular beam epitaxy (MBE), a workhorse of the semiconductor industry, has progressed rapidly in the last few decades in the development of novel materials. Recent developments in condensed matter and materials physics have seen the rise of many novel quantum materials that require ultra-clean and high-quality samples for fundamental studies and applications. Novel oxide-based quantum materials synthesized using MBE have advanced the development of the field and materials. In this review, we discuss the recent progress in new MBE techniques that have enabled synthesis of complex oxides that exhibit ‘quantum’ phenomena, including superconductivity and topological electronic states. We show how these techniques have produced breakthroughs in the synthesis of 4d and 5d oxide films and heterostructures that are of particular interest as quantum materials. These new techniques in MBE offer a bright future for the synthesis of ultra-high quality oxide quantum materials.

Physics↗

Analysis of vibronic coupling in a 4f molecular magnet with FIRMS

Vibronic coupling, the interaction between molecular vibrations and electronic states, is a fundamental effect that profoundly affects chemical processes. In the case of molecular magnetic materials, vibronic, or spin-phonon, coupling leads to magnetic relaxation, which equates to loss of magnetic memory and loss of phase coherence in molecular magnets and qubits, respectively. The study of vibronic coupling is challenging, and most experimental evidence is indirect. Here we employ far-infrared magnetospectroscopy to directly probe vibronic transitions in [Yb(trensal)] (where H 3 trensal = 2,2,2-tris(salicylideneimino)trimethylamine). We find intense signals near electronic states, which we show arise due to an “envelope effect” in the vibronic coupling Hamiltonian, which we calculate fully ab initio to simulate the spectra. We subsequently show that vibronic coupling is strongest for vibrational modes that simultaneously distort the first coordination sphere and break the C 3 symmetry of the molecule. With this knowledge, vibrational modes could be identified and engineered to shift their energy towards or away from particular electronic states to alter their impact. Hence, these findings provide new insights towards developing general guidelines for the control of vibronic coupling in molecules.

36 MATERIALS SCIENCE↗

Proton-controlled molecular ionic ferroelectrics

Abstract Molecular ferroelectric materials consist of organic and inorganic ions held together by hydrogen bonds, electrostatic forces, and van der Waals interactions. However, ionically tailored multifunctionality in molecular ferroelectrics has been a missing component despite of their peculiar stimuli-responsive structure and building blocks. Here we report molecular ionic ferroelectrics exhibiting the coexistence of room-temperature ionic conductivity (6.1 × 10 −5 S/cm) and ferroelectricity, which triggers the ionic-coupled ferroelectric properties. Such ionic ferroelectrics with the absorbed water molecules further present the controlled tunability in polarization from 0.68 to 1.39 μC/cm 2 , thermal conductivity by 13% and electrical resistivity by 86% due to the proton transfer in an ionic lattice under external stimuli. These findings enlighten the development of molecular ionic ferroelectrics towards multifunctionality.

42 ENGINEERING↗

Foundations of Molecular 'Isotomics'

The naturally occurring rare isotopes are versions of common elements, such as hydrogen, carbon and oxygen, that contain a larger than usual number of neutrons in their atomic nuclei and therefore are higher in mass than the common atoms of that element. Isotopes exist for most elements and are found in most natural and synthetic materials, but are uneven in their distribution because chemical and physical processes are isotope-selective (e.g., a chemical reaction may proceed more rapidly for one isotope than for another). For this reason, abundances of isotopes in a material of interest can provide a record, or ‘signature’ of various features of that material’s origin and history. These signatures have been used in the geo, life, chemical and physical sciences in a wide variety of ways over close to 8 decades. However, many such applications struggle to reach unique interpretations of isotopic data because multiple factors combine to control a given sample’s overall isotopic content. That is, the factors controlling isotopic content are too numerous and complex to fully constrain from a simple measurement of a material’s isotope abundances. However, the distribution of isotopes within materials, at molecular scales potentially provides a vastly larger number and diversity of constraints on the chemical and physical processes that comprise a material’s history. The rare isotopes may be concentrated into one atomic position in a molecule relative to another, some proportion of molecules in a sample may contain two or more rare isotopes, and those multiply-isotope-substituted forms of molecules may also have uneven distributions of those isotopes across individual atomic sites. For these reasons, even small, seemingly simple molecules, such as sugars, amino acids or drug compounds, actually exist in a vast number of isotopically unique forms (often millions or more), and each one of those forms is in some sense an independent ‘vote’ on that sample’s history. This project has focused on opening this rich archive of information by enabling the creation of routinely and widely applicable ways of measuring and interpreting isotopic structures of molecules. This work has included the development of core technologies and analytical methods, advancing fundamental understanding of the physical and chemical properties of isotopic versions of molecules, and conducting proof of concept studies of illustrative geochemical, cosmochemical and forensic problems in order to show how these technologies, methods and principles come together to solve problems in new ways. A key to the success of this project was the adaptation of ‘Fourier transform mass spectrometry’ (FTMS) to the task of precisely measuring proportions of the rare, naturally occurring isotopic forms of molecules. FTMS is a highly specialized form of mass spectrometry that traps ions within magnetic or electrostatic cavities and, effectively, ‘listens’ (through registering of subtle electrical signals) to the harmonic signals they make while rapidly orbiting within those cavities. These signals have periods that are a function of their mass and strength (or ‘loudness’) that is proportional to their abundances. Thus, these signals constrain relative amounts of molecules that differ in their mass due to various isotopic substitutions. This technology has been essential to the identification of organic molecules in the life, chemical and environmental sciences for over 4 decades, but generally has lacked the control, stability and precision to meaningfully measure rare isotope forms of molecules. This project’s most fundamental contribution has been to modify FTMS, both in terms of hardware and methods, to enable such measurements. The raw data of molecular isotopic structure is tremendously voluminous and complex, so another important activity of this project has been developing the theoretical and data-science tools needed to interpret the data generated by this new form of isotopic measurement. A particularly challenging part of this task has been predicting molecular isotopic structure, as only through the comparison of measurements with predictions can we make progress on hypothesis driven research questions. We have attacked this this prediction task through a combination of first-principles chemical-physics models of the effects of isotope substitution on molecule properties and data-science models that permit us to generalize that chemical physics to cases that have not yet been studied by detailed chemical physics theory. The proof of concept applications we have pursued over the course of this study include biological reactions of amino acids and other biomolecules, non-biological synthesis of organic molecules in extra-terrestrial settings such as meteorites, petroleum geoscience questions concerning the origin and evolution of natural gas, oil and kerogen compounds, and forensic questions such as the sourcing of chemical weapons. The successes of these applications have laid the groundwork for the next phase of this field’s development, which will include larger scale and more ambitious studies of molecular isotopic structure as a means of diagnosing human diseases, such as cancer, and reconstructing detailed interpretations of the origin and evolution of organic molecules in modern and geological environments.

Cesar, Jaime↗

Structural organization of space polymers

Extraterrestrial polymers of glycine with iron have been characterized by mass spectrometry to have a core mass of 1494 Da with dominant rod-like variants at mass to charge ratios of 1567 and 1639 [McGeoch et al., “Meteoritic proteins with glycine, iron and lithium,” arXiv:2102.10700 (2021)]. Several principal macro-structural morphologies are observed in solvent extracts from a chondritic Vigarano class alteration type 3 meteoritic material. The first is an extended sheet of linked (three-legged) triskelia containing the 1494 Da core entity that encloses gas bubbles in the solvent. A second is of fiber-like crystals found here, via x-ray diffraction, to be multiple-walled nanotubes made from a square lattice of the 1494 Da polymer. A third is a dispersion of floating phantom-like short tubes of up to 100 μm length with characteristic angled bends that suggest the influence of a specific underlying protein structure. Here, it is proposed that the angled tubes are the observable result of a space-filling superpolymerization of 1638 Da polymer subunits guided by the tetragonal symmetry of linking silicon bonds. Distorted hexagonal sheets are linked by perpendicular subunits in a three-dimensional hexagonal diamond structure to fill the largest possible volume. This extended very low-density structure is conjectured to have dominated in a process of chemical selection because it captured a maximum amount of molecular raw material in the ultra-low density of molecular clouds or of the proto-solar nebula. This could have led ultimately to the accretion of the earliest planetary bodies.

59 BASIC BIOLOGICAL SCIENCES↗

A study of real-world micrograph data quality and machine learning model robustness

Abstract Machine-learning (ML) techniques hold the potential of enabling efficient quantitative micrograph analysis, but the robustness of ML models with respect to real-world micrograph quality variations has not been carefully evaluated. We collected thousands of scanning electron microscopy (SEM) micrographs for molecular solid materials, in which image pixel intensities vary due to both the microstructure content and microscope instrument conditions. We then built ML models to predict the ultimate compressive strength (UCS) of consolidated molecular solids, by encoding micrographs with different image feature descriptors and training a random forest regressor, and by training an end-to-end deep-learning (DL) model. Results show that instrument-induced pixel intensity signals can affect ML model predictions in a consistently negative way. As a remedy, we explored intensity normalization techniques. It is seen that intensity normalization helps to improve micrograph data quality and ML model robustness, but microscope-induced intensity variations can be difficult to eliminate.

36 MATERIALS SCIENCE↗

Harnessing Quantum Capacitance in 2D Material/Molecular Layer Junctions for Novel Electronic Device Functionality

Two-dimensional (2D) materials promise advances in electronic devices beyond Moore’s scaling law through extended functionality, such as non-monotonic dependence of device parameters on input parameters. However, the robustness and performance of effects like negative differential resistance (NDR) and anti-ambipolar behavior have been limited in scale and robustness by relying on atomic defects and complex heterojunctions. In this paper, we introduce a novel device concept that utilizes the quantum capacitance of junctions between 2D materials and molecular layers. We realized a variable capacitance 2D molecular junction (vc2Dmj) diode through the scalable integration of graphene and single layers of stearic acid. The vc2Dmj exhibits NDR with a substantial peak-to-valley ratio even at room temperature and an active negative resistance region. The origin of this unique behavior was identified through thermoelectric measurements and ab initio calculations to be a hybridization effect between graphene and the molecular layer. The enhancement of device parameters through morphology optimization highlights the potential of our approach toward new functionalities that advance the landscape of future electronics.

2D materials↗

Reduced Energetic Disorders in Dion–Jacobson Perovskites for Efficient and Spectral Stable Blue LEDs

Metal halide perovskites have witnessed great success in green, red, and near-infrared light-emitting diodes (LEDs), yet blue LEDs still lag behind. Reducing undesired energetic disorders – broad n -phases and halide segregation – is considered as the most critical strategy to further improve the performances. Here, the study reports a newly designed and synthesized di-ammonium ligand with rigid π -conjugated rings and additional methyl groups to construct Dion–Jacobson (DJ) structure. Augmented coordination from the extra ammonium site and increased effective bulkiness from methyl groups lead to better distribution control over conventional mono-ammonium ligands. This enhances the radiative recombination of blue emissions in the film with homogeneous energy landscape and improved surface morphology, as evidenced by a series of imaging and mapping techniques. As a result, it demonstrates DJ perovskite LEDs (PeLEDs) with peak external quantum efficiencies of ≈4% at 484 nm and ≈11% at 494 nm, which are among the top reported for pure DJ phase-based PeLEDs in the corresponding wavelength regions. The results deepen the understanding of regulating energetic disorders in perovskite materials via molecular engineering.

36 MATERIALS SCIENCE↗

A finite viscoelastic constitutive model for low to high strain rate response of elastomers with application of strain rate-induced glass transition

Amorphous elastomers exhibit significant rate-stiffening and unique viscous flow characteristics across a wide range of strain rates, often undergoing glass transition above a strain rate threshold. We have developed a thermodynamically-consistent and micromechanically-inspired constitutive model for soft elastomeric materials to capture the rate-dependent stress-strain behavior and hysteresis when subjected to low to high strain rates. Here, our proposed constitutive model encapsulates the viscous flow of materials through molecular motion at low strain rates and local rearrangement and alignment of the molecules trying to overcome the intermolecular resistance at high strain rates, essentially covering the glass transition. We applied our constitutive model to uniaxial compression experiments performed at low and high strain rates for polyborosiloxane (PBS) to identify the material parameters, and subsequently, performed numerical simulations of single and multi-cycle compression, stress relaxation, and small amplitude oscillatory tension-compression. Our analyses indicate that the model predicts higher total energy dissipation with increasing strain rate; however, dissipation associated with molecular relaxation decreases (forming a cusp) because, beyond a crossover strain rate, intermolecular rearrangement and alignment become dominant, which is consistent with the onset of the glass transition. For cyclic loading-unloading, we observed that dissipation over a cycle remains constant at low strain rates but decreases non-monotonically at high strain rates before becoming constant, with the peak stress over the cycle becoming higher, which can be interpreted as more loading being carried elastically by the polymer network as the intermolecular rearrangement process occurs. Additionally, our model was able to predict the qualitative nature of the storage modulus and loss modulus in the limit of small strain over a wide range of frequency sweeps.

36 MATERIALS SCIENCE↗

Accurate noncovalent interactions in atomistic systems via quantum Drude oscillators

Accurately modeling polarization and van der Waals (vdW) interactions in atomistic systems typically requires high-level quantum-mechanical methods that are computationally expensive, hence limited in applicability. To address this challenge, efficient yet physically grounded models are needed—ones that not only enable accurate predictions but also provide insight into how noncovalent interactions scale in complex molecular and material systems. This review highlights the quantum Drude oscillator (QDO) model, a physically motivated and computationally efficient framework that captures the essential features of electronic response, including polarization and dispersion forces, across a wide range of chemical and material systems. We discuss how the QDO model quantitatively reproduces the polarization response of many-electron atoms and how key components of noncovalent interactions—exchange-repulsion, polarization, and dispersion—emerge naturally in QDO dimers. Furthermore, the model provides predictive scaling laws that elucidate trends in polarizability and dispersion across the periodic table and in molecular assemblies. By uniting interpretability, accuracy, and efficiency, the QDO model offers a versatile approach for modeling noncovalent interactions in systems ranging from isolated molecules to complex condensed phases and nanostructured materials.

Khabibrakhmanov, Almaz [Univ. of Luxembourg, Luxem↗

Tailoring Crosslinks through Time─A Paradigm for Tough Hydrogels

The multidisciplinary applications of hydrogels have motivated a thorough exploration of the relationship between their structural and mechanical properties, i.e., spatial structure–function relationships, with particular regard to strength and toughness. While this approach has driven fundamental advancements in the design of robust hydrogel structures, further complementary perspectives are needed to enable holistic, rational design schemes that integrate considerations such as fabrication and advanced functions like response and adaptation. To these ends, this review focuses on the dynamics of temporal-function relationships and their fundamental bases in order to highlight how the dynamic regulation of polymer interactions programs: 1) polymer assembly and material structure; 2) response to deformation and fracture behavior; 3) dynamic modulation of properties and structural remodeling/self-healing. Here, by exploring this intersection of hydrogel formation, function, and remodeling, this review seeks to shed light on the fundamental relationship between molecular structure, material assembly, and performance in order to connect the emerging area of bioinspired materials processing with tough hydrogel design, and further provides a lasting inspiration and impetus for future hydrogel development that enables valuable scientific and technological advancements.

36 MATERIALS SCIENCE↗

Systematic modification of functionality in disordered elastic networks through free energy surface tailoring

A combined machine learning–physics–based approach is explored for molecular and materials engineering. Specifically, collective variables, akin to those used in enhanced sampled simulations, are constructed using a machine learning model trained on data gathered from a single system. Through the constructed collective variables, it becomes possible to identify critical molecular interactions in the considered system, the modulation of which enables a systematic tailoring of the system’s free energy landscape. To explore the efficacy of the proposed approach, we use it to engineer allosteric regulation and uniaxial strain fluctuations in a complex disordered elastic network. Its successful application in these two cases provides insights regarding how functionality is governed in systems characterized by extensive connectivity and points to its potential for design of complex molecular systems.

42 ENGINEERING↗

Modeling graphene sheet growth and dynamical matrix calculations using molecular dynamics

Molecular dynamics (MD) has been an incredibly useful tool to model physical processes that were synthesized experimentally but not fully understood. MD, through the use of semi-empirical inter-atomic potentials, has allowed understanding of different physical processes in materials science. Yet as well as providing useful insights into materials science, molecular dynamics has a wider range of usability. In this report, I will be detailing how MD can be used to study graphene formation from a carbon liquid which requires high temperatures and pressures. Beyond this, I will describe the usefulness of MD for understanding the physics for phonon transport quantum sensors. To do this, MD was employed to determine the dynamical matrix by treating atoms as coupled oscillators. An accurate understanding of the dynamical matrix of a system is required to calculate the non-equilibrium Green’s function used to describe the phonon transport within phonon wave-guides. I found that, across multiple pressures and temperatures, randomly placed carbon atoms will show evidence of pent-first formation with semi-empirical models. Density functional theory (DFT), on the other hand, was too computationally expensive to use for full scale MD simulations, but we have the possibility of training a machine learned interatomic potential to approximate DFT for carbon in the environments being studied for pent-first graphene sheet formation.

36 MATERIALS SCIENCE↗

Bayesian optimization for chemical products and functional materials

The design of chemical-based products and functional materials is vital to modern technologies, yet remains expensive and slow. Artificial intelligence and machine learning offer new approaches to leverage data to overcome these challenges. Herein this review focuses on recent applications of Bayesian optimization (BO) to chemical products and materials including molecular design, drug discovery, molecular modeling, electrolyte design, and additive manufacturing. Numerous examples show how BO often requires an order of magnitude fewer experiments than Edisonian search. The essential equations for BO are introduced in a self-contained primer specifically written for chemical engineers and others new to the area. Finally, the review discusses four current research directions for BO and their relevance to product and materials design.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Trinuclear Copper(I) and Silver(I) Complexes of a Pentafluorosulfanyl-Decorated Pyrazolate

Here, this study reports the synthesis and characterization of the first fluorinated coinage metal pyrazolates with SF 5 groups, specifically {[3-(SF 5 )­Pz]­M} 3 (M = Cu, Ag), and compares them to trifluoromethylated analogs. X-ray analysis reveals different pyrazolyl ligand orientations and metallophilic interactions, as well as supramolecular structures influenced by fluorinated substituents and metals. The {[3-(SF 5 )­Pz]­M} 3 complexes form face-to-face sandwich adducts with benzene. Photophysical investigations show that copper complexes exhibit long-lived phosphorescence, while silver analogs display fast fluorescence, with emission properties modulated by substituents, solvent environment, and intermolecular interactions. Computational studies confirm the experimental geometries and demonstrate that SF 5 substituents increase the π-acidity and enhance arene binding over the analogs featuring the smaller and less-electron-withdrawing CF 3 groups and provide insights into metallophilic interactions and photoluminescence. These newly uncovered SF 5 decorated materials, with enhanced π-acidity and arene-binding ability, are promising materials for molecular sensing and tunable luminescence.

Vanga, Mukundam [University of Texas, Arlington, T↗