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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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An open-source power system model (Texas2k), running on an open-source dynamic simulator (ANDES), designed to lower barriers for machine-learning studies of power systems.
The rapid increase in computing power, especially with the integration of graphics processing units, has dramatically increased the capabilities of molecular dynamics simulations. To date, these capabilities extend from running very long simulations (tens to hundreds of microseconds) to thousands of short simulations. However, the expansive data generated in these simulations must be made interpretable not only by the investigator who performs them but also by others as well. Here, we demonstrate how integrating learning techniques, such as artificial intelligence, machine learning, and neural networks, into analysis pipelines can reveal the kinetics of Alzheimer's disease (AD) protein aggregation. Finally, we review select AD targets, describe current simulation methods, and introduce learning concepts and their application in AD, highlighting limitations and potential solutions.
New discoveries on polymer and polyelectrolyte brush systems and the corresponding brush-supported ions and water, arising from employing all-atom molecular dynamics simulations, have been thoroughly reviewed.
Further feasibility study on a superconducting linear synchronous motor (LSM) rocket launcher system is presented on the basis of dynamic simulations of electric power, efficiency and power factor as well as the ascending motions of the launcher and rocket. The advantages of attractive-mode operation are found from comparison with repulsive-mode operation. It is made clear that the LSM rocket launcher system, of which the long-stator is divided optimally into 60 sections according to launcher speeds, can obtain high efficiency and power factor.
Understanding carbonate speciation and how it may be modulated is essential for the advancement of carbon dioxide (CO 2 ) capture and storage technologies, which often rely on the transformation of CO 2 into carbonate, e.g. via the formation of carbonate minerals. To date, few atomic-level, quantum-mechanics-based simulations have been carried out to characterize how carbonic acid (H 2 CO 3 ) and bicarbonate ($HCO^{-}_{3}$) form in aqueous solution, and how pH affects this process. Recently, Martirez and Carter utilized rare-event sampling density functional theory molecular dynamics simulations in combination with multi-level embedded correlated wavefunction theory, thus accounting for both solvent dynamics and electron correlation accurately, to elucidate the mechanism of H 2 CO 3 formation in neutral solution (J. Am. Chem. Soc., 145, 12561, 2023). Here, we perform a complementary simulation using the same method to map out the energetics of $HCO^{-}_{3}$ formation from dissolved CO 2 in basic solution. We find that, as in H 2 CO 3 formation, including water dynamics is important to obtain an accurate prediction of the energetics for the aforementioned reaction. Furthermore, only with MD did we identify the correct pathway for the reaction, in which water – not hydroxide – acts as the initial nucleophile and only at the transition state does it lose a proton.
Here, the equations of state for the H-, L- and T-Nb 2 O 5 polymorphs were investigated with density functional theory. The shock Hugoniot curves were predicted from ab initio molecular dynamics simulations along 300–2500 K isotherms. Enthalpies derived from simulations at 300 K show that the H → L transition occurs at 6.2 GPa, in agreement with the measured value of ~5.9 GPa, and is more favorable than the H → T transition. Pressure-induced amorphization (PIA) of L-Nb 2 O 5 is in the range ~18-25 GPa, consistent with experiments reporting PIA above 21.4 GPa. The simulated PIA of T-Nb 2 O 5 between ~16 and 26 GPa reproduces PIA observed above 19.2 GPa.
The advection-diffusion equation is simulated via several quantum algorithms. Three formulations are considered: (1) Trotterization, (2) variational quantum time evolution (VarQTE), and (3) adaptive variational quantum dynamics simulation (AVQDS). These schemes were originally developed for the Hamiltonian simulation of many-body quantum systems. The finite-difference discretized operator of the transport equation is formulated as a Hamiltonian and solved without the need for ancillary qubits. Computations are conducted on a quantum simulator (IBM Qiskit Aer) and a superconducting quantum hardware (IBM Fez). The former emulates the latter without the noise. The actual hardware implementation experiences significant noise. The results of the quantum simulator are compared with data from direct numerical simulation (DNS) with infidelities of the order 10 −5 . In the quantum simulator, Trotterization is observed to have the lowest infidelity and is suitable for fault-tolerant computation. The AVQDS algorithm requires the lowest gate count and circuit depth. The VarQTE algorithm is the next best in terms of gate counts, but the number of its optimization variables is directly proportional to the number of qubits. Due to current hardware limitations, Trotterization cannot be implemented, as it has an overwhelmingly large number of operations. Meanwhile, AVQDS and VarQTE can be executed at the hardware level. These algorithms present a new paradigm for computational transport phenomena on quantum computers.
A new algorithm for molecular dynamics (MD) simulations is developed to optimize plasma particle distributions at given initial temperatures. By combining velocity scaling and reassignment, the method effectively eliminates the initial rise and oscillation in temperatures observed with randomly distributed positions. These rises and oscillations are undesired numerical artifacts observed in conventional plasma MD simulations, arising from unoptimized particle positions. The algorithm demonstrates temperature relaxation without initial rises or oscillations, as well as precise flow velocity relaxation, enabling accurate measurement of relaxation times. The code is accelerated using graphics processing units for parallel processing, enhancing the study of plasma dynamics. The proposed method for distributing physically valid particles in MD simulations enables accurate studies of intrinsic collision processes in plasmas, including the dynamics of strongly coupled plasmas, plasma–wave interactions, and transport phenomena in magnetized plasmas. The paper concludes with a discussion of potential applications and future enhancements to the algorithm.
As the main components of Portland cement, calcium silicates show substantial differences in their hydration reactivity which have not been fully explained. A comprehensive comparison of the initial hydration process of calcium silicates, namely dicalcium silicate (C{sub 2}S) and tricalcium silicate (C{sub 3}S), was conducted using molecular dynamics simulations. The initial hydration process was divided into three stages using cut-off times of 0.001 ns and 3 ns. The hydration of M{sub 3}-C{sub 3}S (010) was more evident than that of β-C{sub 2}S (100), supported by the hydroxylation degree, radial distribution function, atomic density profile, etc. The coordination number of the surface Ca atoms might be the underlying reason for such a difference. Interactions between cement surfaces and water molecules were mainly characterised by solid OH bonding and Ca-water O bonding. Dissolution of Ca atoms was observed, although quite scarce, while no dissolution of Si atoms was observed.
In this investigation, force field-based molecular dynamics (MD) simulations have been employed to generate detailed structural representations for a range of amorphous quaternary CaO-MgO-Al{sub 2}O{sub 3}-SiO{sub 2} (CMAS) and ternary CaO-Al{sub 2}O{sub 3}-SiO{sub 2} (CAS) glasses. Comparison of the simulation results with select experimental X-ray and neutron total scattering and literature data reveals that the MD-generated structures have captured the key structural features of these CMAS and CAS glasses. Based on the MD-generated structural representations, we have developed two structural descriptors, specifically (i) average metal oxide dissociation energy (AMODE) and (ii) average self-diffusion coefficient (ASDC) of all the atoms at melting. Both structural descriptors are seen to more accurately predict the relative glass reactivity than the commonly used degree of depolymerization parameter, especially for the eight synthetic CAS glasses that span a wide compositional range. Hence these descriptors hold great promise for predicting CMAS and CAS glass reactivity in alkaline environments from compositional information.
A partial charge empirical potential for the Nb-O pair has been developed based on existing parameters of other pairs in a Teter potential to enable molecular dynamics simulations of both lithium niobate crystal and lithium niobosilicate glasses. The developed potential is capable of describing structural features of lithium niobosilicate glasses in a wide composition range, including niobium coordination number and bond length, density, bond angle distribution, polyhedral distribution and fraction of bridging oxygen. Furthermore, the results obtained using this new potential show good agreement with experimental data of density and structure from Raman spectroscopy of lithium niobosilicate glasses and X-ray absorption spectroscopy near Nb K-edge.
Abstract The challenge of targeting RNA with small molecules necessitates a better understanding of RNA–ligand interaction mechanisms. However, the dynamic nature of nucleic acids, their ligand‐induced stabilization, and how conformational changes influence gene expression pose significant difficulties for experimental investigation. This work employs a combination of computational and experimental methods to address these challenges. By integrating structure‐informed design, crystallography, and machine learning‐augmented all‐atom molecular dynamics simulations (MD), we synthesized, biophysically and biochemically characterized, and studied the dissociation of a library of small molecule activators of the 5‐aminoimidazole–4–carboxamide ribonucleotide triphosphate (ZTP) riboswitch, a ligand‐binding RNA motif that regulates bacterial gene expression. We uncovered key interaction mechanisms, revealing valuable insights into the role of ligand binding kinetics on riboswitch activation. Further, we established that ligand on‐rates determine activation potency as opposed to binding affinity and elucidated RNA structural differences, which provide mechanistic insights into the interplay of RNA structure on riboswitch activation.
Battery capacity is highly related to ion-pairing mechanisms in electrolytes, since a cluster formation can lead to dead Li formation, reducing the number of charge carriers and leading to capacity fading. We use molecular dynamics simulations to model an electrolyte comprising trimethyl phosphate (TMP) solvent and a lithium bis(fluorosulfonyl)imide (LiFSI) salt, exploring effects of salt concentration on solvation and ion-transport. We simulate the LiFSI-TMP electrolyte for salt concentrations of 0.7, 1.43 and 3.82 molar. A statistical analysis was performed to study ion-pairing, clustering, diffusivity, conductivity, and coordination of Li-ions, providing insights into relations between molecular structures and transport properties. Molecular structure of ionic components changes as concentration increases, from a predominant solvent separated ion pair (SSIP) and contact ion pair (CIP) to aggregate salt (AGG) and ionic cluster formation. Given the formation of the ionic cluster, the diffusion mechanism followed by Li-ions changes from a hopping/exchange to a vehicular mechanism as concentration increases; this is reflected in a decrease of ionic conductivities. Ionicity was also calculated to reveal how the ionic motion changes from an uncorrelated to a correlated one as the salt concentration increases. Furthermore, we also compared our results with experimental calculations performed for similar electrolyte systems
Previous error analyses conducted by the Guidance and Dynamics Branch of NASA have used the Guidance Analysis Program (GAP) as the trajectory simulation tool. Plans are made to conduct all future error analyses using the Space Vehicle Dynamics Simulation (SVDS) program. A study was conducted to compare the inertial measurement unit (IMU) error simulations of the two programs. Results of the GAP/SVDS comparison are presented and problem areas encountered while attempting to simulate IMU errors, vehicle performance uncertainties and environmental uncertainties using SVDS are defined. An evaluation of the SVDS linear error analysis capability is also included.
Accurate Force Fields (FFs) are essential for Molecular Dynamics (MD) simulations of the dynamics of realistic materials in terms of atomic-level interactions. The FF parameters of short-range valence interactions can be derived through Quantum Mechanical (QM) calculations on model systems practical for QM (<300 atoms). Similarly, the dynamic electrostatic interactions can be described with methods such as QEq or PQEq that allow charges and polarization to adjust dynamically. However, accurately extracting long-range van der Waals (vdW) interactions from QM calculations poses challenges due to the absence of a definitive method to distinguish between the different energetic components of electrostatics, polarization, vdW, hydrogen bonding, and valence interactions. To do this we use the Perdew–Burke–Ernzerhof flavor of Density Functional Theory, including empirical D3 vdW corrections, to predict the Equation of State for each element (keeping any covalent bonds fixed), from which we obtain the two-body vdW nonbond potential. Here, we extend these calculations to include non-bonded parameters for the N and O columns of the periodic table so that we now describe columns 15 (N), 16 (O), 17 (F), and 18 (Ne) of the periodic table. For these 20 elements, we find that the two-body vdW potentials can all be mapped to a single universal two-body curve, with just three scaling parameters: Re, De, and L. We refer to this as the Universal NonBond (UNB) potential. We expect this to be useful for new MD simulations and a helpful starting point to obtain UNB parameters for the remainder of the periodic table.
The identity of metal ions surrounding DNA is key to its biological function and materials applications. In this work, we compare atomistic molecular dynamics simulations of double strand DNA (dsDNA) with four alkaline earth metal ions (Mg 2+ , Ca 2+ , Sr 2+ , and Ba 2+ ) to elucidate the physical interactions that govern DNA–ion binding. Simulations accurately model the ion–phosphate distance of Mg 2+ and reproduce ion counting experiments for Ca 2+ , Sr 2+ , and Ba 2+ . Our analysis shows that alkaline earth metal ions prefer to bind at the phosphate backbone compared to the major groove and negligible binding occurs in the minor groove. Larger alkaline earth metal ions with variable first solvation shells (Ca 2+ , Sr 2+ , and Ba 2+ ) show both direct and indirect binding, where indirect binding increases with ion size. Mg 2+ does not fit this trend because the strength of its first solvation shell predicts indirect binding only. Ions bound to the phosphate backbone form fewer contacts per ion compared to the major groove. Within the major groove, metal ions preferentially bind to guanine–cystosine base pairs and form simultaneous contacts with the N7 and O6 atoms of guanine. Overall, we find that the interplay among ion size, DNA–ion interaction, and the size and flexibility of the first solvation shell are key to predicting how alkaline earth metal ions interact with DNA.
Fluorine and other halides commonly exist in nuclear waste forms, and due to their volatile nature, halide retention poses an issue affecting waste loading during vitrification. The compositional effect on fluorine incorporation in aluminosilicate glasses is investigated through molecular dynamics simulations. Oxygen and fluorine coordination numbers around glass former and modifier cations, bond angle distributions, and medium range structure features such as Q n distributions, ring size distributions and neutron diffraction structure factors were calculated. It was found that fluorine has higher preference to bond to Ca 2+ than to Na + , both in the melt and the glass, and there is no Si-F bond formation in the glass but they do exist in the melt. Consequently, CaO for Na 2 O substitution can be an effective way to help fluorine retention without significantly changing the glass chemistry. Furthermore, these results thus provide insights on fluorine incorporation in the aluminosilicate nuclear waste glasses and the strategy on how to improve fluorine retention both in the glass and the melt.
An improved technique for the identification and extraction of the exact quantities associated with the degrees of freedom at the element as well as the flexible body level is presented. It is implemented in the dynamic equations of motions based on the recursive formulation of Kane et al. (1987) and presented in a matrix form, integrating the concepts of strain energy, the finite-element approach, modal analysis, and reduction of equations. This technique eliminates the CPU intensive matrix multiplication operations in the code's hot spots for the dynamic simulation of the interconnected rigid and flexible bodies. A study of a simple robot with flexible links is presented by comparing the execution times on a scalar machine and a vector-processor with and without vector options. Performance figures demonstrating the substantial gains achieved by the technique are plotted.