2,823 research outputs found

    Revisiting Solid-solid Phase Transitions in Sodium and Potassium Tetrafluoroborate for Thermal Energy Storage

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    In situ synchrotron powder x-ray diffraction (PXRD) study was conducted on sodium and potassium tetrafluoroborate (NaBF4 and KBF4) to elucidate structural changes across solid-solid phase transitions over multiple heating-cooling cycles. The phase transition temperatures from diffraction measurements are consistent with the differential scanning calorimetry data (~240 °C for NaBF4 and ~290 °C for KBF4). The crystal structure of the high-temperature (HT) NaBF4 phase has been determined from synchrotron PXRD data. The HT disordered phase of NaBF4 crystallizes in the hexagonal, space group P63/mmc (No. 194) with a = 4.98936(2) Å, c = 7.73464(4) Å, V = 166.748(2) Å3, and Z = 2 at 250 °C. Density functional theory molecular dynamics (MD) calculations imply that the P63/mmc is indeed a stable structure for rotational NaBF4. MD simulations reproduce experimental phase sequence upon heating and indicates that F atoms are markedly more mobile than K and B atoms in the disordered state. Thermal expansion coefficients for both phases were determined from high precision lattice parameters at elevated temperatures, as obtained from Rietveld refinement of PXRD data. Interestingly for the HT-phase of NaBF4, the structure (upon heating) contracts slightly in the a-b plane but expands in the c direction such that overall thermal expansion is positive. Thermal conductivity at room temperature were measured and the values are 0.8-1.0 W.m-1K-1 for NaBF4 and 0.55-0.65 W.m-1K-1 for KBF4. The thermal conductivity and diffusivity showed a gradual decrease up to the transition temperature and then rose slightly. Both materials show good thermal and structural stabilities over multiple heating/cooling cycles.<br/

    Efficient Generation of Stable Linear Machine-Learning Force Fields with Uncertainty-Aware Active Learning

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    Machine-learning force fields enable an accurate and universal description of the potential energy surface of molecules and materials on the basis of a training set of ab initio data. However, large-scale applications of these methods rest on the possibility to train accurate machine learning models with a small number of ab initio data. In this respect, active-learning strategies, where the training set is self-generated by the model itself, combined with linear machine-learning models are particularly promising. In this work, we explore an active-learning strategy based on linear regression and able to predict the model's uncertainty on predictions for molecular configurations not sampled by the training set, thus providing a straightforward recipe for the extension of the latter. We apply this strategy to the spectral neighbor analysis potential and show that only tens of ab initio simulations of atomic forces are required to generate stable force fields for room-temperature molecular dynamics at or close to chemical accuracy. Moreover, the method does not necessitate any conformational pre-sampling, thus requiring minimal user intervention and parametrization

    Deep Variational Free Energy Approach to Dense Hydrogen

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    We developed a deep generative model-based variational free energy approach to the equations of state of dense hydrogen. We employ a normalizing flow network to model the proton Boltzmann distribution and a fermionic neural network to model the electron wave function at given proton positions. By jointly optimizing the two neural networks we reached a comparable variational free energy to the previous coupled electron-ion Monte Carlo calculation. The predicted equation of state of dense hydrogen under planetary conditions is denser than the findings of ab initio molecular dynamics calculation and empirical chemical model. Moreover, direct access to the entropy and free energy of dense hydrogen opens new opportunities in planetary modeling and high-pressure physics research.Comment: 7+5 pages, 3+4 figures, code: https://github.com/fermiflow/hydroge

    Nanofluids with optimised thermal properties based on metal chalcogenides with different morphology

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    Over the last decades, the interest around renewable energies has increased considerably because of the growing energy demand and the environmental problems derived from fossil fuels combustion. In this scenario, concentrating solar power (CSP) is a renewable energy with a high potential to cover the global energy demand. However, improving the efficiency and reducing the cost of technologies based on this type of energy to make it more competitive is still a work in progress. One of the current lines of research is the replacement of the heat transfer fluid used in the absorber tube of parabolic trough collectors with nano-colloidal suspensions of nanomaterials in a base fluid, typically named nanofluids. Nanofluids are considered as a new generation of heat transfer fluids since they exhibit thermophysical properties improvements compared with conventional heat transfer fluids. But there are still some barriers to overcome for the implementation of nanofluids. For example, obtaining nanofluids with high stability is a priority challenge for this kind of system. Also ensuring that nanoparticles will not clog pipes or cause pressure drops. In this Doctoral Thesis, the use of transition metal dichalcogenide-based nanofluids as a heat transfer fluid in solar power plants has been investigated for the first time. Specifically, nanofluids based on one-dimensional, two-dimensional and three-dimensional MoS2 , WS2 and WSe2 nanostructures have been researched. The base fluid used in the preparation of these nanofluids is the eutectic mixture of biphenyl and diphenyl oxide typically employed as heat transfer fluid in concentrating solar power plants. Mainly two preparation methods have been explored: the liquid phase exfoliation method, and the solvothermal synthesis of the nanomaterial and its subsequent dispersion in the thermal oil by ultrasound. Experimental parameters such as surfactant concentration, time and sonication frequency for preparation of nanofluids have also been analysed. The nanofluids have been subjected to an extensive characterisation which includes the study of colloidal stability over time, characterisation of thermal properties such as isobaric specific heat or thermal conductivity, rheological properties and optical properties. The results have revealed that nanofluids based on 1D and 2D nanostructures of transition metal dichalcogenides are colloidally stable over time and exhibit improved thermal properties compared to the typical thermal fluid used in solar power plants. The most promising nanofluids are those based on MoS 2 nanosheets and those based on WSe 2 nanosheets with heat transfer coefficient improvements of 36.2% and 34.1% respectively with respect to thermal oil. Furthermore, the dramatic role of WSe2 nanosheets in enhancing optical extinction of the thermal oil suggests the use of these nanofluids in direct absorption solar collectors. In conclusion, the present work demonstrates the feasibility of using nanofluids based on transition metal dichalcogenide nanostructures as heat transfer fluids in concentrating solar power plants based on parabolic trough collectors.En las últimas décadas, el interés en torno a las energías renovables ha aumentado considerablemente debido a la creciente demanda energética y a los problemas medioambientales derivados de la combustión de combustibles fósiles. En este escenario, la energía solar de concentración (CSP) es una energía renovable con un alto potencial para cubrir la demanda energética mundial. Sin embargo, es necesario trabajar para mejorar la eficiencia y reducir el coste de las tecnologías basadas en este tipo de energía con el objetivo de hacerla más competitiva. Una de las líneas de investigación actuales es la sustitución del fluido caloportador utilizado en el tubo absorbedor de los colectores cilindroparabólicos por suspensiones nanocoloidales de nanomateriales en un fluido base, típicamente denominados nanofluidos. Los nanofluidos se consideran una nueva generación de fluidos de transferencia de calor, ya que presentan mejoras en sus propiedades termofísicas en comparación con los fluidos de transferencia de calor convencionales. Pero aún quedan algunos obstáculos por superar para la aplicación de los nanofluidos. Por ejemplo, obtener nanofluidos con alta estabilidad es un reto prioritario en este tipo de sistemas. También garantizar que las nanopartículas no obstruyan las tuberías ni provoquen caídas de presión. En esta Tesis Doctoral se ha investigado por primera vez el uso de nanofluidos basados en dicalcogenuros de metales de transición como fluido caloportador en centrales solares. En concreto, se han investigado nanofluidos basados en nanoestructuras unidimensionales, bidimensionales y tridimensionales de MoS2, WS2 y WSe2. El fluido base utilizado en la preparación de estos nanofluidos es la mezcla eutéctica de bifenilo y óxido de difenilo empleada habitualmente como fluido de transferencia de calor en las centrales de concentración de energía solar. Se han explorado principalmente dos métodos de preparación: el método de exfoliación en fase líquida y la síntesis solvotermal del nanomaterial y su posterior dispersión en el aceite térmico mediante ultrasonidos. También se han analizado parámetros experimentales como la concentración de surfactante, el tiempo y la frecuencia de sonicación para la preparación de los nanofluidos. Los nanofluidos han sido sometidos a una extensa caracterización que incluye el estudio de la estabilidad coloidal a lo largo del tiempo, la caracterización de propiedades térmicas como el calor específico isobárico o la conductividad térmica, propiedades reológicas y propiedades ópticas. Los resultados han revelado que los nanofluidos basados en nanoestructuras 1D y 2D de dicalcogenuros de metales de transición son coloidalmente estables en el tiempo y presentan propiedades térmicas mejoradas en comparación con el fluido térmico típico utilizado en las centrales solares. Los nanofluidos más prometedores son los basados en nanoláminas de MoS2 y los basados en nanoláminas de WSe2, con mejoras del coeficiente de transferencia térmica del 36,2% y el 34,1%, respectivamente, con respecto al aceite térmico. Además, el espectacular papel de las nanoláminas de WSe2 en la mejora de la extinción óptica del aceite térmico sugiere el uso de estos nanofluidos en colectores solares de absorción directa. En conclusión, el presente trabajo demuestra la viabilidad del uso de nanofluidos basados en nanoestructuras de dicalcogenuros de metales de transición como fluidos de transferencia de calor en centrales solares de concentración basadas en colectores cilindro-parabólicos

    TensorMD: Scalable Tensor-Diagram based Machine Learning Interatomic Potential on Heterogeneous Many-Core Processors

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    Molecular dynamics simulations have emerged as a potent tool for investigating the physical properties and kinetic behaviors of materials at the atomic scale, particularly in extreme conditions. Ab initio accuracy is now achievable with machine learning based interatomic potentials. With recent advancements in high-performance computing, highly accurate and large-scale simulations become feasible. This study introduces TensorMD, a new machine learning interatomic potential (MLIP) model that integrates physical principles and tensor diagrams. The tensor formalism provides a more efficient computation and greater flexibility for use with other scientific codes. Additionally, we proposed several portable optimization strategies and developed a highly optimized version for the new Sunway supercomputer. Our optimized TensorMD can achieve unprecedented performance on the new Sunway, enabling simulations of up to 52 billion atoms with a time-to-solution of 31 ps/step/atom, setting new records for HPC + AI + MD

    AB-INITIO INVESTIGATION OF 2D MATERIALS FOR GAS SENSING, ENERGY STORAGE AND SPINTRONIC APPLICATIONS

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    The field of Two Dimensional (2D) materials has been extensively studied since their discovery in 2004, owing to their remarkable combination of properties. My thesis focuses on exploring novel 2D materials such as Graphene Nanoribbon (GNR), holey carbon nitride C2N, and MXenes for energy storage, gas sensing, and spintronic applications, utilizing state-of-the-art techniques that combine Density Functional Theory (DFT) and Non-Equilibrium Greens Functions (NEGF) formalism; namely Vienna Ab-initio Simulation Package (VASP) and Atomistic Toolkit (ATK) package.Firstly, on the side of gas sensing, the burning of fossil fuels raises the level of toxic gas and contributes to global warming, necessitating the development of highly sensitive gas sensors. To start with, the adsorption and gas-sensing properties of bilaterally edge doped (B/N) GNRs were investigated. The transport properties revealed that the bilateral B/N edge-doping of GNR yielded Negative Differential Resistance (NDR) IV-characteristics, due to the electron back-scattering which was beneficial for selective gas sensing applications. Therefore, both GNR: B/N were found to be good sensors for NO2 and SO3 respectively. After that, the catalytic activity of four magnetic transition metal “TM” elements (e.g., Mn, Fe, Co and Ni) embedded in C2N pores, as Single-Atom Catalysts (SAC), was tested towards detecting toxic oxidizing gases. The results of spin-polarized transport properties revealed that Ni- and Fe-embedded C2N are the most efficient in detecting NO/ NO2 and NO2 molecules.Secondly, on the side of energy storage, since the fossil fuels reserves are depleting at an alarming rate, there is an urgent need for alternative forms of energy to meet the ever-growing demand for energy. Hydrogen is a popular form of clean energy. However, its storage and handling are challenging because of its explosive nature. The effect of magnetic moment on the hydrogen adsorption and gas-sensing properties in Mn-embedded in C2N were investigated. Two distinct configurations of embedment were considered: (i) SAC: 1Mn@C2N; and (ii) DAC: Mn2@C2N. Based on the huge changes in electronic and magnetic properties and the low recovery time (i.e., τ ≪ 1 s, τ = 92 μs and 1.8 ms, respectively), we concluded that C2N:Mn is an excellent candidate for (reusable) hydrogen magnetic gas sensor with high sensitivity and selectivity and rapid recovery time. Then, a comparative study of hydrogen storage capabilities on Metal- catalyst embedded (Ca versus Mn) C2N is presented which demonstrated the stability of these metal structures embedded on the C2N substrate. We proposed Ca@C2N and Mn@C2N for dual applications- hydrogen storage and a novel electrode for prospective metal-ion battery applications owing to its high irreversible uptake capacity 200 mAhg-1.Thirdly, on the side of data storage, spintronics is an emerging field for the next generation nanoelectronics devices to reduce their power consumption and to increase their memory and processing capabilities. Designing 2D-materials that exhibit half-metallic properties is important in spintronic devices that are used in low-power high-density logic circuits. We tested samples comprising of SAC and DAC of Mn embedded in a C2N sample size 2×2 primitive cells as well as their combinations in neighboring large pores. Many other TM catalysts were screened, and the results show the existence of half metallicity in just five cases: (a) C2N:Mn (DAC, SAC-SAC, and SAC-DAC); (b) C2N:Fe (DAC); and (c) C2N:Ni (SAC-DAC). Our results further showed the origins of half-metallicity to be attributed to both FMC and synergetic interactions between the catalysts with the six mirror images, formed by the periodic-boundary conditions.Lastly, on the side of batteries, sodium-sulfur batteries show great potential for storing large amounts of energy due to their ability to undergo a double electron- redox process, as well as the plentiful abundance of sodium and sulfur resources. However, the shuttle effect caused by intermediate sodium polysulfides (Na2Sn) limits their performance and lifespan. To address this issue, we proposed two functionalized MXenes Hf3C2T2 and Zr3C2T2 (T= F, O), as cathode additives to suppress the shuttle effect. We found that both Hf3C2T2 and Zr3C2T2 systems inhibit the shuttle effect by binding to Na2Sn with a binding energy higher than the electrolyte solvents. The decomposition barrier for Na2Sn on the O functionalized MXenes gets reduced which enhances the electrochemical process. Overall, our findings show that the tuning of 2D materials can lead to promising applications in various fields, including energy storage, gas sensing, and spintronics

    A universal interatomic potential for perovskite oxides

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    With their celebrated structural and chemical flexibility, perovskite oxides have served as a highly adaptable material platform for exploring emergent phenomena arising from the interplay between different degrees of freedom. Molecular dynamics (MD) simulations leveraging classical force fields, commonly depicted as parameterized analytical functions, have made significant contributions in elucidating the atomistic dynamics and structural properties of crystalline solids including perovskite oxides. However, the force fields currently available for solids are rather specific and offer limited transferability, making it time-consuming to use MD to study new materials systems since a new force field must be parameterized and tested first. The lack of a generalized force field applicable to a broad spectrum of solid materials hinders the facile deployment of MD in computer-aided materials discovery (CAMD). Here, by utilizing a deep-neural network with a self-attention scheme, we have developed a unified force field that enables MD simulations of perovskite oxides involving 14 metal elements and conceivably their solid solutions with arbitrary compositions. Notably, isobaric-isothermal ensemble MD simulations with this model potential accurately predict the experimental phase transition sequences for several markedly different ferroelectric oxides, including a 6-element ternary solid solution, Pb(In1/2_{1/2}Nb1/2_{1/2})O3_3--Pb(Mg1/3_{1/3}Nb2/3_{2/3})O3_3--PbTiO3_3. We believe the universal interatomic potential along with the training database, proposed regression tests, and the auto-testing workflow, all released publicly, will pave the way for a systematic improvement and extension of a unified force field for solids, potentially heralding a new era in CAMD.Comment: 18 pages, 4 figure

    Complexity of Many-Body Interactions in Transition Metals via Machine-Learned Force Fields from the TM23 Data Set

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    This work examines challenges associated with the accuracy of machine-learned force fields (MLFFs) for bulk solid and liquid phases of d-block elements. In exhaustive detail, we contrast the performance of force, energy, and stress predictions across the transition metals for two leading MLFF models: a kernel-based atomic cluster expansion method implemented using sparse Gaussian processes (FLARE), and an equivariant message-passing neural network (NequIP). Early transition metals present higher relative errors and are more difficult to learn relative to late platinum- and coinage-group elements, and this trend persists across model architectures. Trends in complexity of interatomic interactions for different metals are revealed via comparison of the performance of representations with different many-body order and angular resolution. Using arguments based on perturbation theory on the occupied and unoccupied d states near the Fermi level, we determine that the large, sharp d density of states both above and below the Fermi level in early transition metals leads to a more complex, harder-to-learn potential energy surface for these metals. Increasing the fictitious electronic temperature (smearing) modifies the angular sensitivity of forces and makes the early transition metal forces easier to learn. This work illustrates challenges in capturing intricate properties of metallic bonding with current leading MLFFs and provides a reference data set for transition metals, aimed at benchmarking the accuracy and improving the development of emerging machine-learned approximations.Comment: main text: 21 pages, 9 figures, 2 tables. supplementary information: 57 pages, 83 figures, 20 table

    Cluster expansion constructed over Jacobi-Legendre polynomials for accurate force fields

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    We introduce a compact cluster expansion method, constructed over Jacobi and Legendre polynomials, to generate highly accurate and flexible machine-learning force fields. The constituent many-body contributions are separated, interpretable and adaptable to replicate the physical knowledge of the system. In fact, the flexibility introduced by the use of the Jacobi polynomials allows us to impose, in a natural way, constrains and symmetries to the cluster expansion. This has the effect of reducing the number of parameters needed for the fit and of enforcing desired behaviours of the potential. For instance, we show that our Jacobi-Legendre cluster expansion can be designed to generate potentials with a repulsive tail at short inter-atomic distances, without the need of imposing any external function. Our method is here continuously compared with available machine-learning potential schemes, such as the atomic cluster expansion and potentials built over the bispectrum. As an example we construct a Jacobi-Legendre potential for carbon, by training a slim and accurate model capable of describing crystalline graphite and diamond, as well as liquid and amorphous elemental carbon.Comment: 16 Pages, 8 figures, 6 Page supplementary materia

    Computing and Compressing Electron Repulsion Integrals on FPGAs

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    The computation of electron repulsion integrals (ERIs) over Gaussian-type orbitals (GTOs) is a challenging problem in quantum-mechanics-based atomistic simulations. In practical simulations, several trillions of ERIs may have to be computed for every time step. In this work, we investigate FPGAs as accelerators for the ERI computation. We use template parameters, here within the Intel oneAPI tool flow, to create customized designs for 256 different ERI quartet classes, based on their orbitals. To maximize data reuse, all intermediates are buffered in FPGA on-chip memory with customized layout. The pre-calculation of intermediates also helps to overcome data dependencies caused by multi-dimensional recurrence relations. The involved loop structures are partially or even fully unrolled for high throughput of FPGA kernels. Furthermore, a lossy compression algorithm utilizing arbitrary bitwidth integers is integrated in the FPGA kernels. To our best knowledge, this is the first work on ERI computation on FPGAs that supports more than just the single most basic quartet class. Also, the integration of ERI computation and compression it a novelty that is not even covered by CPU or GPU libraries so far. Our evaluation shows that using 16-bit integer for the ERI compression, the fastest FPGA kernels exceed the performance of 10 GERIS (10×10910 \times 10^9 ERIs per second) on one Intel Stratix 10 GX 2800 FPGA, with maximum absolute errors around 10710^{-7} - 10510^{-5} Hartree. The measured throughput can be accurately explained by a performance model. The FPGA kernels deployed on 2 FPGAs outperform similar computations using the widely used libint reference on a two-socket server with 40 Xeon Gold 6148 CPU cores of the same process technology by factors up to 6.0x and on a new two-socket server with 128 EPYC 7713 CPU cores by up to 1.9x
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