Upper-Bound Energy Minimization to Search for Stable Functional Materials with Graph Neural Networks
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36 MATERIALS SCIENCE↗
SEARCH · Engineering Papers
Results for “crystal structure prediction”
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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Structure of HIV-1 RRE stem-loop II identifies two conformational states of the high-affinity Rev binding site
During HIV infection, specific RNA-protein interaction between the Rev response element (RRE) and viral Rev protein is required for nuclear export of intron-containing viral mRNA transcripts. Rev initially binds the high-affinity site in stem-loop II, which promotes oligomerization of additional Rev proteins on RRE. Here, we present the crystal structure of RRE stem-loop II in distinct closed and open conformations. The high-affinity Rev-binding site is located within the three-way junction rather than the predicted stem IIB. The closed and open conformers differ in their non-canonical interactions within the three-way junction, and only the open conformation has the widened major groove conducive to initial Rev interaction. Rev binding assays show that RRE stem-loop II has high- and low-affinity binding sites, each of which binds a Rev dimer. We propose a binding model, wherein Rev-binding sites on RRE are sequentially created through structural rearrangements induced by Rev-RRE interactions.
60 APPLIED LIFE SCIENCES↗
Programmed synthesis of mesoporous protein crystals in cellular reactors
Protein crystals are naturally derived mesoporous materials with versatile structures and physicochemical properties. Here we introduce an intracellular synthesis platform that enables controllable and programmable protein crystallization. In live cells, we show that, after initial nucleation, steady protein expression governs crystal growth, yielding predictable, tunable dynamics in live cells. Exploiting this feature, we combined HaloTag and click chemistries to achieve modular, programmable immobilization of diverse guest materials with spatial patterning down to ~100 nm resolution. We further demonstrated the sequential release of immobilized materials in physiologically relevant fluids. As a proof of concept, we programmed particles to carry human fibroblast growth factors in distinct layers, which elicited designed oscillatory Akt signalling patterns in cell culture. Finally, this work outlines a programmable method for producing mesoporous materials, with possible applications in catalysis and biomedicine.
Yang, Hongru [Johns Hopkins Univ., Baltimore, MD (↗
PECAN2
PECAN2, Pose Classification with 3D Atomic Network 2, presents an innovative approach to pose classification in computational modeling, particularly in the context of molecular docking. Traditional methods like docking, which rely on physics-based calculations, are prone to inaccuracies in predicting binding poses during experimental testing. While machine learning (ML) approaches, such as ML-driven pose classification, have been introduced to address these issues, they often depend on the availability of crystal structure data. In contrast, PECAN2 introduces a novel pose classification method that operates independently of crystal structures. Instead, it utilizes a 3D atomic neural network with Point Cloud Network (PCN) to establish correlations between docking scores and experimental data. This approach marks a departure from previous studies that heavily relied on crystal structures for labeling. The key innovation lies in the ability of PECAN2 to enhance the performance of molecular docking by filtering out false positives and false negatives through the correlation between docking scores and experimental data.
Shim, Heesung↗
Crystal growth and nucleation at metal-organic framework / solution interfaces
Metal-organic frameworks (MOFs) represent an incredibly diverse class of materials with equally diverse applications in catalysis, separations, and gas storage. Despite the prevalence of MOF structures in the literature, in many cases the synthesis of pure, crystalline MOF materials remains a significant challenge, with failed syntheses often yielding mixed crystalline or non-porous amorphous products. Our recent work aims to enable targeted MOF synthesis by developing a comprehensive and predictive model of the fundamental processes occurring at the MOF-solution interface under synthetically-relevant conditions. Our recent accomplishments include: (i) development and implementation of a graphics processor-accelerated approach to simulation the nucleation of weak electrolytes, and the extension of these methods to large/complex solutes such as MOFs; and (ii) studies of the structure of ZIF interfaces under conditions relevant to crystal growth; and (iii) addressing important open questions in the mechanism of MOF solvent-assisted linker exchange, and important synthetic pathway for MOFs.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
A New Sodium Thioborate Fast Ion Conductor: Na 3 B 5 S 9
Abstract We report a new sodium fast‐ion conductor, Na 3 B 5 S 9 , that exhibits a high Na ion total conductivity of 0.80 mS cm −1 (sintered pellet; cold‐pressed pellet=0.21 mS cm −1 ). The structure consists of corner‐sharing B 10 S 20 supertetrahedral clusters, which create a framework that supports 3D Na ion diffusion channels. The Na ions are well‐distributed in the channels and form a disordered sublattice spanning five Na crystallographic sites. The combination of structural elucidation via single crystal X‐ray diffraction and powder synchrotron X‐ray diffraction at variable temperatures, solid‐state nuclear magnetic resonance spectra and ab initio molecular dynamics simulations reveal high Na‐ion mobility (predicted conductivity: 0.96 mS cm −1 ) and the nature of the 3D diffusion pathways. Notably, the Na ion sublattice orders at low temperatures, resulting in isolated Na polyhedra and thus much lower ionic conductivity. This highlights the importance of a disordered Na ion sublattice—and existence of well‐connected Na ion migration pathways formed via face‐sharing polyhedra—in dictating Na ion diffusion.
Zhou, Laidong↗
A New Sodium Thioborate Fast Ion Conductor: Na 3 B 5 S 9
Abstract We report a new sodium fast‐ion conductor, Na 3 B 5 S 9 , that exhibits a high Na ion total conductivity of 0.80 mS cm −1 (sintered pellet; cold‐pressed pellet=0.21 mS cm −1 ). The structure consists of corner‐sharing B 10 S 20 supertetrahedral clusters, which create a framework that supports 3D Na ion diffusion channels. The Na ions are well‐distributed in the channels and form a disordered sublattice spanning five Na crystallographic sites. The combination of structural elucidation via single crystal X‐ray diffraction and powder synchrotron X‐ray diffraction at variable temperatures, solid‐state nuclear magnetic resonance spectra and ab initio molecular dynamics simulations reveal high Na‐ion mobility (predicted conductivity: 0.96 mS cm −1 ) and the nature of the 3D diffusion pathways. Notably, the Na ion sublattice orders at low temperatures, resulting in isolated Na polyhedra and thus much lower ionic conductivity. This highlights the importance of a disordered Na ion sublattice—and existence of well‐connected Na ion migration pathways formed via face‐sharing polyhedra—in dictating Na ion diffusion.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Hydrogen Adsorption on Ordered and Disordered Pt-Ni Alloys
The bulk properties and chemical reactivity of disordered Pt-Ni alloys in the A1 ( fcc ) structure are investigated herein using different methods: Virtual Crystal Approximation (VCA), Korringa–Kohn–Rostoker Coherent Potential Approximation (KKR-CPA), and large explicit supercells generated using Super-Cell Random Approximates (SCRAPs). While VCA predicts lattice constants that closely follow Vegard’s law, the large supercells and KKR-CPA predict lattice constants that are consistently larger than Vegard’s law. KKR-CPA results closely agree with those from the large supercells for the disordered alloys, producing similar projected density of states and magnetic moment across the composition range. For instance, while VCA predicts the disordered alloys to be non-magnetic at a Pt concentration ( x Pt ) ≥ 0.5, KKR-CPA and SCRAPs predict the disordered alloys to remain ferromagnetic to higher Pt concentrations. As x Pt decreases, the adsorption of H becomes more exothermic on bulk-terminated (111) surfaces but less exothermic on Pt monolayer-terminated (111) surfaces due largely to strain effects. (111) surfaces cut from the large supercells predict average H adsorption energies on the disordered alloys similar to those on the ordered phases of the same compositions, while VCA predicts H adsorption to be more exothermic.
36 MATERIALS SCIENCE↗
Theoretical examination of nuclear spin diffusion in light-induced spin coherences in photosystem I
Microsecond coherence times are predicted in spin dynamics calculations of a donor–acceptor electron spin pair P + A 1A - created upon light excitation of photosystem I (PSI). The effects of nuclear spin diffusion (NSD) due to the anisotropic protein environment on the predicted coherence times T M are studied. Closely positioned pairs and triples of protons located 5–8 Å from the electron spin are shown to largely control T M . Additionally, knowledge of the crystal structure of PSI allows for spin dynamics calculations where specific cofactors and amino acid residues are removed or replaced and the identification of anisotropic environmental features controlling electronic decoherence. Finally, we show that NSD alone cannot explain the >3× shorter experimentally observed coherence times and suggest that methyl groups at key protein sites may explain this discrepancy.
74 ATOMIC AND MOLECULAR PHYSICS↗
CsV 3 Sb 5 : A $\mathbb{Z}_2$ Topological Kagome Metal with a Superconducting Ground State
Recently discovered alongside its sister compounds KV 3 Sb 5 and RbV 3 Sb 5 , CsV 3 Sb 5 crystallizes with an ideal kagome network of vanadium and antimonene layers separated by alkali metal ions. This work presents the electronic properties of CsV 3 Sb 5 , demonstrating bulk superconductivity in single crystals with a T c = 2.5 K. The normal state electronic structure is studied via angle-resolved photoemission spectroscopy and density-functional theory, which categorize CsV 3 Sb 5 as a Z 2 topological metal. Multiple protected Dirac crossings are predicted in close proximity to the Fermi level (E F ), and signatures of normal state correlation effects are also suggested by a high-temperature charge density wavelike instability. Finally, the implications for the formation of unconventional superconductivity in this material are discussed.
36 MATERIALS SCIENCE↗
Structural and biochemical basis for regiospecificity of the flavonoid glycosyltransferase UGT95A1
Glycosylation is a predominant strategy plants use to fine-tune the properties of small molecule metabolites to affect their bioactivity, transport, and storage. It is also important in biotechnology and medicine as many glycosides are utilized in human health. Small molecule glycosylation is largely carried out by family 1 glycosyltransferases. Here, we report a structural and biochemical investigation of UGT95A1, a family 1 GT enzyme from Pilosella officinarum that exhibits a strong, unusual regiospecificity for the 3'-O position of flavonoid acceptor substrate luteolin. We obtained an apo crystal structure to help drive the analyses of a series of binding site mutants, revealing that while most residues are tolerant to mutations, key residues M145 and D464 are important for overall glycosylation activity. Interestingly, E347 is crucial for maintaining the strong preference for 3'-O glycosylation, while R462 can be mutated to increase regioselectivity. The structural determinants of regioselectivity were further confirmed in homologous enzymes. Our study also suggests that the enzyme contains large, highly dynamic, disordered regions. We showed that while most disordered regions of the protein have little to no implication in catalysis, the disordered regions conserved among investigated homologs are important to both the overall efficiency and regiospecificity of the enzyme. This report represents a comprehensive in-depth analysis of a family 1 GT enzyme with a unique substrate regiospecificity and may provide a basis for enzyme functional prediction and engineering.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Clathrate BaNi 2 P 4 : An Interplay of Heat and Charge Transport Due to Strong Host–Guest Interactions
Heat and charge transport properties of the metallic unconventional clathrate BaNi 2 P 4 , hosting Ba cations in oversized Ni 8 P 16 cages, are investigated. A novel method of single-crystal growth was developed, yielding 2–3 mm sized crystals of BaNi 2 P 4 . We also developed a setup to accurately measure thermal conductivity and electrical resistivity of the synthesized single crystals in a wide temperature range avoiding crystal remounting. BaNi 2 P 4 has a metallic temperature dependence of its electrical resistivity (decreasing with decreasing temperature) and manifests an unconventional T 2 power law for 50 K < T < 300 K; below 50 K, the power-law exponent increases gradually such that below 10 K the power law is T 5 , a predicted but extremely rarely experimentally observed dependence for peculiar electron–phonon interactions. Furthermore, electronic band structure calculations, consistent with measurements of de Haas–van Alphen oscillations, show large band dispersions with significant contributions of Ba orbitals to states near the Fermi level, which is atypical for clathrates. The thermal properties of BaNi 2 P 4 were probed using a combination of variable-temperature single-crystal X-ray diffraction experiments, heat capacity measurements, first-principles phonon dispersion calculations, and inelastic neutron scattering measurements. BaNi 2 P 4 exhibits significant hybridization of the Ba-guest and Ni–P-framework vibrational modes, which may be enhanced via the detected split of the Ba position, which results in strong Ba-framework interactions.
36 MATERIALS SCIENCE↗
Unraveling Electronic Trends in O* and OH* Surface Adsorption in the MO 2 Transition-Metal Oxide Series
Understanding the bond strength of O* and OH* intermediates to metal-oxide surfaces is key to predicting the catalytic activity in oxygen-based electrochemistry. Here, we uncover highly non-linear trends in O* and OH* adsorption energies across the 3d, 4d, and 5d series of MO 2 transition-metal (TM) oxide surfaces computed within Hubbard- U corrected density functional theory (DFT + U ). Investigating the electronic structure with crystal orbital Hamiltonian populations (COHP) of the relevant metal–oxygen bonds reveals that the spin-dependent coupling strength between metal-d and oxygen-2p atomic orbitals together with the extent of filling of bonding and anti-bonding orbitals are the primary contributors to the adsorption energy. Importantly, we show that the integrated COHP obtained purely from bulk calculations is a highly accurate descriptor for surface adsorption energetics that captures trends across the group 5–12 TM oxide series within 0.19–0.36 eV. Our results suggest a pathway to prediction of adsorption energies for an arbitrary metal–ligand catalyst system.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Thermodynamics–Structure–Dynamics Correlations and Nonuniversal Effects in the Elastically Collective Activated Hopping Theory of Glass-Forming Liquids
We employ the microscopic Elastically Collective Nonlinear Langevin Equation (ECNLE) theory of activated dynamics in combination with crystal-avoiding simulations to study four inter-related questions for metastable monodisperse hard sphere fluids. The first is how significantly improved integral equation theory structural input (Modified-Verlet (MV) closure) changes the dynamical predictions of ECNLE theory. The main consequence is a modest enhancement of the importance of the collective elastic barrier relative to its local cage contribution, which increases the alpha relaxation time and fragility relative to prior results based on the Percus–Yevick closure. Second, ECNLE-MV theory predictions for the alpha time and self-diffusion constant in the metastable regime are quantitatively compared to our new simulations. The small adjustment of a numerical prefactor that enters the collective elastic barrier leads to quantitative agreement over three decades. Third, using the more accurate MV structural input, ECNLE theory is shown to predict thermodynamics–structure–dynamics “correlations” based on various long and short wavelength scalar properties all related to static two-point collective density fluctuations. The logarithm of the alpha relaxation time scales as a power law with these scalar metrics with an exponent that is significantly lower in the less dense noncooperative activated regime compared to the very dense highly cooperative regime. However, the discovered correlation of activated relaxation with a thermodynamic property (dimensionless compressibility) is not causal in ECNLE theory, but rather reflects a strong connection between the local structural quantities that quantify kinetic constraints in the theory with the amplitude of long wavelength density fluctuations. Fourth, the consequences of chemically specific nonuniversalities associated with the onset condition and relative importance of collective elasticity are studied. In conclusion, the predicted thermodynamics–structure–dynamics correlations are found to be robust, albeit with nontrivial shifts of the onset condition.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Improving the Representation of Snow Crystal Properties Within a Single-Moment Microphysics Scheme
As computational resources continue their expansion, weather forecast models are transitioning to the use of parameterizations that predict the evolution of hydrometeors and their microphysical processes, rather than estimating the bulk effects of clouds and precipitation that occur on a sub-grid scale. These parameterizations are referred to as single-moment, bulk water microphysics schemes, as they predict the total water mass among hydrometeors in a limited number of classes. Although the development of single moment microphysics schemes have often been driven by the need to predict the structure of convective storms, they may also provide value in predicting accumulations of snowfall. Predicting the accumulation of snowfall presents unique challenges to forecasters and microphysics schemes. In cases where surface temperatures are near freezing, accumulated depth often depends upon the snowfall rate and the ability to overcome an initial warm layer. Precipitation efficiency relates to the dominant ice crystal habit, as dendrites and plates have relatively large surface areas for the accretion of cloud water and ice, but are only favored within a narrow range of ice supersaturation and temperature. Forecast models and their parameterizations must accurately represent the characteristics of snow crystal populations, such as their size distribution, bulk density and fall speed. These properties relate to the vertical distribution of ice within simulated clouds, the temperature profile through latent heat release, and the eventual precipitation rate measured at the surface. The NASA Goddard, single-moment microphysics scheme is available to the operational forecast community as an option within the Weather Research and Forecasting (WRF) model. The NASA Goddard scheme predicts the occurrence of up to six classes of water mass: vapor, cloud ice, cloud water, rain, snow and either graupel or hail.
Molthan, Andrew L.↗
Defect Complexes in CrSBr Revealed Through Electron Microscopy and Deep Learning
Atomic defects underpin the properties of van der Waals materials, and their understanding is essential for advancing quantum and energy technologies. Scanning transmission electron microscopy is a powerful tool for defect identification in atomically thin materials, and extending it to multilayer and beam-sensitive materials would accelerate their exploration. Here, we establish a comprehensive defect library in a bilayer of the magnetic quasi-1D semiconductor CrSBr by combining atomic-resolution imaging, deep learning, and calculations. We apply a custom-developed machine learning work flow to detect, classify, and average point vacancy defects. This classification enables us to uncover several distinct Cr interstitial defect complexes, combined Cr and Br vacancy defect complexes, and lines of vacancy defects that extend over many unit cells. We show that their occurrence is in agreement with our computed structures and binding energy densities, reflecting the intriguing layer interlocked crystal structure of CrSBr. Our ab initio calculations show that the interstitial defect complexes give rise to highly localized electronic states. These states are of particular interest due to the reduced electronic dimensionality and magnetic properties of CrSBr and are, furthermore, predicted to be optically active. Our results broaden the scope of defect studies in challenging materials and reveal new defect types in bilayer CrSBr that can be extrapolated to the bulk and to over 20 materials belonging to the same FeOCl structural family.
deep learning↗
LeaPP: Learning Pathways to Polymorphs through Machine Learning Analysis of Atomic Trajectories
Understanding the mechanisms underlying crystal nucleation and growth is crucial for many technological applications. Due to the short length and time scales involved, crystal nucleation is often studied using molecular simulations. Most existing approaches to extract the nucleation mechanism from simulations focus on the analysis of static snapshots of the configurations, potentially overlooking subtle local fluctuations and the history of the particles involved in the formation of solid nuclei. Here, in this work, we propose a novel methodology called LeaPP that categorizes nucleation trajectories based on the temporal information of their constituent particles. We leverage the time evolution of the local environment of the crystallizing particles to encapsulate the relationship between the structure and dynamics and distinguish between different evolving particle paths. Identification of the distinct particle paths further enables characterizing the nucleation trajectories into different pathways. Collectively, LeaPP provides a more nuanced understanding of nucleation through an unsupervised approach with lesser dependence on traditional order parameters. Furthermore, the pathways identified by LeaPP are predictive of the resulting polymorph. We demonstrate LeaPP on three different systems─Lennard-Jones-like particles, Ni 3 Al, and water on surfaces. The general methodology underlying LeaPP─considering the time evolution of the building blocks─applies to a wide range of self-assembly problems.
36 MATERIALS SCIENCE↗
Million-scale data integrated deep neural network for phonon properties of heuslers spanning the periodic table
Existing machine learning potentials for predicting phonon properties of crystals are typically limited on a material-to-material basis, primarily due to the exponential scaling of model complexity with the number of atomic species. We address this bottleneck with the developed Elemental Spatial Density Neural Network Force Field, namely Elemental-SDNNFF. The effectiveness and precision of our Elemental-SDNNFF approach are demonstrated on 11,866 full, half, and quaternary Heusler structures spanning 55 elements in the periodic table by prediction of complete phonon properties. Self-improvement schemes including active learning and data augmentation techniques provide an abundant 9.4 million atomic data for training. Deep insight into predicted ultralow lattice thermal conductivity (<1 Wm –1 K –1 ) of 774 Heusler structures is gained by p–d orbital hybridization analysis. Additionally, a class of two-band charge-2 Weyl points, referred to as “double Weyl points”, are found in 68% and 87% of 1662 half and 1550 quaternary Heuslers, respectively.
36 MATERIALS SCIENCE↗