The materials experiment knowledge graph
Graph representations of hierarchical knowledge, including experiment provenances, will help usher in a new era of data-driven materials science.
Statt, Michael J.↗
SEARCH · Engineering Papers
Results for “material science”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Elucidating molecular scale interactions underlying the freezing behavior of salt solutions in silica nanopores
Elucidating the influence of nanoscale confinement on the freezing behavior of salt solutions is of fundamental interest to environmental security and materials science and engineering. Specific structural information such as the coordination environment of ions in confined aqueous medium, effects on the extended hexagonal network of water molecules and their mutual non-bonding interactions in confinement are sparse in the literature along with the change in the dynamical characteristics of water in the presence of ions. To address these knowledge gaps, the current study is focused on investigating the influence of reduced dimensionality arising from nanoscale confinement on the structural evolution of salt solutions on freezing and the associated fluid–surface interactions. In this regard, operando wide-angle X-ray scattering (WAXS) measurements and classical molecular dynamics (MD) simulations are conducted with water and 0.5 M CaCl 2 , MgCl 2 and KCl solutions confined in 4 nm sized SBA-15 silica pores upon cooling from 300 K to 200 K. The freezing point of the salt solutions is depressed to 235 K which is about 10 K lower than that of confined water. The translational dynamics of confined salt solutions indicates shifts in the fragile-to-strong dynamical crossover to a lower temperature compared to pure water. The strong electrostatic attraction between the cations and the surrounding water molecules contributes to the freezing point depression in confined salt solutions. These insights unlock the molecular-scale basis and mechanisms underlying the freezing behavior of confined salt solutions.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Uncertainty quantification for molecular property predictions with graph neural architecture search
Graph Neural Networks (GNNs) have emerged as a prominent class of data-driven methods for molecular property prediction. However, a key limitation of typical GNN models is their inability to quantify uncertainties in the predictions. This capability is crucial for ensuring the trustworthy use and deployment of models in downstream tasks. To that end, we introduce AutoGNNUQ, an automated uncertainty quantification (UQ) approach for molecular property prediction. AutoGNNUQ leverages architecture search to generate an ensemble of high-performing GNNs, enabling the estimation of predictive uncertainties. Our approach employs variance decomposition to separate data (aleatoric) and model (epistemic) uncertainties, providing valuable insights for reducing them. In our computational experiments, we demonstrate that AutoGNNUQ outperforms existing UQ methods in terms of both prediction accuracy and UQ performance on multiple benchmark datasets, and generalizes well to out-of-distribution datasets. Additionally, we utilize t-SNE visualization to explore correlations between molecular features and uncertainty, offering insight for dataset improvement. AutoGNNUQ has broad applicability in domains such as drug discovery and materials science, where accurate uncertainty quantification is crucial for decision-making.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
AI-powered exploration of molecular vibrations, phonons, and spectroscopy
The vibrational dynamics of molecules and solids play a critical role in defining material properties, particularly their thermal behaviors. However, theoretical calculations of these dynamics are often computationally intensive, while experimental approaches can be technically complex and resource-demanding. Recent advancements in data-driven artificial intelligence (AI) methodologies have substantially enhanced the efficiency of these studies. This review explores the latest progress in AI-driven methods for investigating atomic vibrations, emphasizing their role in accelerating computations and enabling rapid predictions of lattice dynamics, phonon behaviors, molecular dynamics, and vibrational spectra. Key developments are discussed, including advancements in databases, structural representations, machine-learning interatomic potentials, graph neural networks, and other emerging approaches. Compared to traditional techniques, AI methods exhibit transformative potential, dramatically improving the efficiency and scope of research in materials science. The review concludes by highlighting the promising future of AI-driven innovations in the study of atomic vibrations.
Han, Bowen [Oak Ridge National Laboratory (ORNL), ↗
Real-space Kohn–Sham density functional theory for complex energy applications
Real-space Kohn-Sham density functional theory (real-space KS-DFT) enables large-scale electronic structure simulations that is particularly well-suited for the modern high-performance computing (HPC) architectures. This feature article reviews its theoretical foundations, highlights the algorithmic advances and recent developments, and showcases applications in complex nano systems. We aim to provide a perspective on the trajectory of real-space KS-DFT as an emerging tool for computational chemistry and materials science in the exascale era.
Zhang, Zeyi↗
Enhancing generative molecular design via uncertainty-guided fine-tuning of variational autoencoders
In recent years, deep generative models have been successfully applied to various molecular design tasks, particularly in the life and materials sciences. One critical challenge for pre-trained generative molecular design (GMD) models is to fine-tune them to be better suited for downstream design tasks that aim at optimizing specific molecular properties. However, redesigning and training an existing effective generative model from scratch for each new design task are impractical. Furthermore, the black-box nature of typical downstream tasks that involve property prediction makes it nontrivial to optimize the generative model in a task-specific manner. In this work, we propose an uncertainty-guided fine-tuning strategy that can effectively enhance a pre-trained variational autoencoder (VAE) for GMD through performance feedback in an active learning setting. The strategy begins by quantifying the model uncertainty of the generative model using an efficient active subspace-based UQ (uncertainty quantification) scheme. Next, the decoder diversity within the characterized model uncertainty class is explored to expand the viable space of molecular generation. The low-dimensionality of the active subspace makes this exploration tractable using a black-box optimization scheme, which in turn enables us to identify and leverage a diverse set of high-performing models to generate enhanced molecules. Empirical results across six target molecular properties using multiple VAE-based generative models demonstrate that our uncertainty-guided fine-tuning strategy consistently leads to improved models that outperform the original pre-trained models.
97 MATHEMATICS AND COMPUTING↗
Stereogenic-at-silicon hybrid conjugated polymers
Stereoregularity in carbon-based polymers impacts critical physical properties such as crystallinity, but achieving comparable precision in polysilanes remains challenging. Herein, we report the synthesis of hybrid σ,π-conjugated polysilanes with defined stereoregularity, derived from cis- and trans-cyclosilane building blocks. Using Kumada polycondensation, we accessed a series of atactic, trans-enriched, and cis-enriched polysilanes, enabling a systematic investigation of how tacticity influences thermal and optical properties. Differential scanning calorimetry revealed distinct glass transition behaviors for stereoregular polymers compared to their atactic counterpart, and UV-vis spectroscopy demonstrated enhanced spectral resolution upon annealing of stereoregular films. A linear silane-based analog further underscored the structural impact of cyclic stereogenic units. These findings highlight how stereochemical precision in silicon-containing backbones can modulate macromolecular properties, opening avenues for the rational design of stereoregular polysilanes.
36 MATERIALS SCIENCE↗
Relativistic ultrafast electron diffraction at high repetition rates
The ability to resolve the dynamics of matter on its native temporal and spatial scales constitutes a key challenge and convergent theme across chemistry, biology, and materials science. The last couple of decades have witnessed ultrafast electron diffraction (UED) emerge as one of the forefront techniques with the sensitivity to resolve atomic motions. Increasingly sophisticated UED instruments are being developed that are aimed at increasing the beam brightness in order to observe structural signatures, but so far they have been limited to low average current beams. Here, we present the technical design and capabilities of the HiRES (High Repetition-rate Electron Scattering) instrument, which blends relativistic electrons and high repetition rates to achieve orders of magnitude improvement in average beam current compared to the existing state-of-the-art instruments. The setup utilizes a novel electron source to deliver femtosecond duration electron pulses at up to MHz repetition rates for UED experiments. Instrument response function of sub-500 fs is demonstrated with < 100 fs time resolution targeted in future. We provide example cases of diffraction measurements on solid-state and gas-phase samples, including both micro- and nanodiffraction (featuring 100 nm beam size) modes, which showcase the potential of the instrument for novel UED experiments.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Recent developments in the PySCF program package
PySCF is a Python-based general-purpose electronic structure platform that supports first-principles simulations of molecules and solids as well as accelerates the development of new methodology and complex computational workflows. Here, we explain the design and philosophy behind PySCF that enables it to meet these twin objectives. With several case studies, we show how users can easily implement their own methods using PySCF as a development environment. We then summarize the capabilities of PySCF for molecular and solid-state simulations. Finally, we describe the growing ecosystem of projects that use PySCF across the domains of quantum chemistry, materials science, machine learning, and quantum information science.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Simulating sticky particles: A Monte Carlo method to sample a stratification
Many problems in materials science and biology involve particles interacting with strong, short-ranged bonds that can break and form on experimental timescales. Treating such bonds as constraints can significantly speed up sampling their equilibrium distribution, and there are several methods to sample probability distributions subject to fixed constraints. We introduce a Monte Carlo method to handle the case when constraints can break and form. More generally, the method samples a probability distribution on a stratification: a collection of manifolds of different dimensions, where the lower-dimensional manifolds lie on the boundaries of the higher-dimensional manifolds. We show several applications of the method in polymer physics, self-assembly of colloids, and volume calculation in high dimensions.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
A robust high sensitivity scanning thermal probe for simultaneous microscale thermal and thermoelectric property mapping
Scanning Thermal Microscopy (SThM) provides efficient thermal property measurement with micro- or nanoscale spatial resolution. However, the sensitivity and accuracy of state-of-the-art thermal probes have been limited by excessive thermal contact resistance between the probe and sample. Introduced herein is a robust thermal microprobe that can increase the probe-sample contact force by more than two orders of magnitude, thereby reducing the probe-sample thermal contact resistance by as much as 96% and increasing measurement sensitivity by more than 240% compared to a commercial thermal probe with the same dimensions and measurement principle. The relationship between the probe-sample thermal contact resistance, thermal exchange radius, and sample thermal conductivity is determined experimentally. Simultaneous measurement of thermal conductivity and Seebeck coefficient with unprecedented sensitivity is demonstrated using the enhanced scanning thermal microprobe on samples of an extended range of thermal conductivity up to 18 W/m K, increasing the range of samples applicable to SThM when compared to the conventional commercial probe with diminished measurement sensitivity above ~10 W/m K. The probe is further demonstrated by simultaneously mapping thermal conductivity and Seebeck coefficient as a function of depth from the irradiated surface of an ion-irradiated bulk nanostructured thermoelectric material. In addition to enabling microscale thermal conductivity and Seebeck coefficient measurement of materials previously not applicable to SThM, the probe can also facilitate high-throughput characterization of combinatorial materials to aid the rapid discovery of compositions and processing conditions that yield highly desired thermal and thermoelectric properties.
36 MATERIALS SCIENCE↗
Synthesis of La 2–x Sr x CuO 4 films via atomic layer-by-layer molecular beam epitaxy
Atomic layer-by-layer molecular beam epitaxy (ALL-MBE) is a sophisticated technique to synthesize high-temperature superconductor (HTS) materials. ALL-MBE produces single-crystal HTS films with atomically smooth surfaces and interfaces, as well as precise multilayer heterostructures engineered down to a single atomic layer level. This enables the fabrication of tunnel junctions, nanowires, nanorings, and other HTS devices of interest. Our group has focused on ALL-MBE synthesis and materials science of La 2–x Sr x CuO 4 (LSCO), a representative HTS cuprate. In the past two decades, we have synthesized over three thousand LSCO thin films and characterized them by a range of analytical techniques. Here, we present in full detail a systematic process for the synthesis and engineering of atomically perfect LSCO films. The procedure includes the preparation of substrates, calibration of the elemental sources, the recipe for ALL growth of LSCO films without any secondary-phase precipitates, post-growth annealing of the films, and ex situ film characterization. This report should aid replication and dissemination of this technique of synthesizing single-crystal LSCO films for basic research as well as for HTS electronic applications.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Optimization of gold germanium (Au 0.17 Ge 0.83 ) thin films for high sensitivity resistance thermometry
Gold–germanium (Au x Ge 1–x ) solid solutions have been demonstrated as highly sensitive thin film thermometers for cryogenic applications. However, little is known regarding the performance of the films for thicknesses less than 100 nm. In response, here we report on the resistivity and temperature coefficient of resistance (TCR) for sputtered films with thicknesses ranging from 10 to 100 nm and annealed at temperatures from 22 to 200°C. The analysis is focused upon composition x=0.17, which demonstrates a strong temperature sensitivity over a broad range. The thinnest films are found to provide an enhancement in TCR, which approaches 20% K –1 at 10 K. Furthermore, reduced anneal temperatures are required to crystallize the Ge matrix and achieve a maximum TCR for films of reduced thickness. These features favor the application of ultra-thin films as high-sensitivity, on-device thermometers in micro- and nanolectromechanical systems.
59 BASIC BIOLOGICAL SCIENCES↗
A review on recent machine learning applications for imaging mass spectrometry studies
Imaging mass spectrometry (IMS) is a powerful analytical technique widely used in biology, chemistry, and materials science fields that continue to expand. IMS provides a qualitative compositional analysis and spatial mapping with high chemical specificity. The spatial mapping information can be 2D or 3D depending on the analysis technique employed. Due to the combination of complex mass spectra coupled with spatial information, large high-dimensional datasets (hyperspectral) are often produced. Therefore, the use of automated computational methods for an exploratory analysis is highly beneficial. The fast-paced development of artificial intelligence (AI) and machine learning (ML) tools has received significant attention in recent years. These tools, in principle, can enable the unification of data collection and analysis into a single pipeline to make sampling and analysis decisions on the go. There are various ML approaches that have been applied to IMS data over the last decade. Here, in this review, we discuss recent examples of the common unsupervised (principal component analysis, non-negative matrix factorization, k-means clustering, uniform manifold approximation and projection), supervised (random forest, logistic regression, XGboost, support vector machine), and other methods applied to various IMS datasets in the past five years. The information from this review will be useful for specialists from both IMS and ML fields since it summarizes current and representative studies of computational ML-based exploratory methods for IMS.
47 OTHER INSTRUMENTATION↗
Toward implementing autonomous adaptive data acquisition for scanning hyperspectral imaging of biological systems
Autonomous experimentation is an emerging area of research, primarily related to autonomous vehicles, scientific combinatorial discovery approaches in materials science and drug discovery, and iterative research loops of planning, experimentation, and analysis. However, autonomous approaches developed in these contexts are difficult to apply to high-dimensional mapping technologies, such as scanning hyperspectral imaging of biological systems, due to sample complexity and heterogeneity. We briefly cover the history of adaptive sampling algorithms and surrogate modeling in order to define autonomous adaptive data acquisition as an objective-based, flexible building block for future biological imaging experimentation driven by intelligent infrastructure. We subsequently summarize the recent implementations of autonomous adaptive data acquisition (AADA) for scanning hyperspectral imaging, assess how these address the difficulties of autonomous approaches in hyperspectral imaging, and highlight the AADA design variation from a goal-oriented perspective. Finally, we present a modular AADA architecture that embeds AADA-driven flexible building blocks to address the challenge of time resolution for high-dimensional scanning hyperspectral imaging of nonequilibrium dynamical systems. In our example research-driven experimental design case, we propose an AADA infrastructure for time-resolved, noninvasive, and label-free scanning hyperspectral imaging of living biological systems. This AADA infrastructure can accurately target the correct state of the system for experimental workflows that utilize subsequent expensive, high-information-content analytical techniques.
59 BASIC BIOLOGICAL SCIENCES↗
Invited article: X-ray phase contrast imaging in inertial confinement fusion and high energy density research
X-ray phase contrast imaging (XPCI) provides enhanced image contrast beyond absorption-based x-ray imaging alone due to refraction and diffraction from gradients in the object material density. It is sensitive to small variations in density, such as internal voids, cracks, grains, defects, and material flow, as well as to stronger density variations such as from a shock wave. Beyond its initial use in biology and materials science, XPCI is now routinely used in inertial confinement fusion (ICF) and high energy density (HED) research, first to characterize ICF capsules and targets, and later applied in dynamic experiments, where coherent x-ray sources, ultrafast x-ray pulses, and high temporal and spatial resolution are required. In this Review article, XPCI image formation theory is presented, its diverse use in ICF and HED research is discussed, the unique requirements for ultrafast XPCI imaging are given, as well as current challenges and issues in its use.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Toward achieving cost-effective hexagonal BN semi-bulk crystals and BN neutron detectors via halide vapor phase epitaxy
Presently, thermal neutron detectors fabricated from boron-10 enriched hexagonal boron nitride (h-10BN) ultrawide bandgap semiconductor grown by metal organic chemical vapor deposition (MOCVD) hold the record high detection efficiency among all solid-state detectors at 59%. To overcome the short comings of MOCVD growth, including inherently low growth rate and unavoidable impurities such as carbon in metal organic source, we demonstrate here the growth of natural hexagonal boron nitride (h-BN) semi-bulk wafers using halide vapor phase epitaxy (HVPE), which is an established technique for producing GaN semi-bulk crystals at a high growth rate. Electrical transport characterization results revealed that these HVPE grown materials possess an electrical resistivity of 1 × 1013 Ω cm, and a charge carrier mobility and lifetime product of 2 × 10−4 cm2/V s. Detectors fabricated from a 100 μm thick h-BN wafer have demonstrated a thermal neutron detection efficiency of 20%, corresponding to a charge collection efficiency of ∼60% at an operating voltage of 500 V. This initial demonstration opens the door for mass producing high efficiency h-BN semiconductor neutron detectors at a reduced cost, which could create unprecedented applications in nuclear energy, national security, nuclear waste monitoring and management, the health care industry, and material sciences.
Physics↗
Controlling quantum phases of electrons and excitons in moiré superlattices
Moiré lattices formed in twisted and lattice-mismatched van der Waals heterostructures have emerged as a platform to engineer the novel electronic and excitonic states at the nanoscale. This Perspective reviews the materials science of moiré heterostructures with a focus on the structural properties of the interface and its structural–property relationships. We first review the studies of the atomic relaxation and domain structures in moiré superlattices and how these structural studies provide critical insights into understanding the behaviors of quantum-confined electrons and excitons. We discuss the general frameworks to manipulate moiré structures and how such control can be harnessed for engineering new phases of matter and simulating various quantum phenomena. Lastly, we discuss routes toward large-scale moiré heterostructures and give an outlook on their applications in quantum electronics and optoelectronics. Special emphasis will be placed on the challenges and opportunities of the reliable fabrication and dynamical manipulation of moiré heterostructures.
77 NANOSCIENCE AND NANOTECHNOLOGY↗