Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Quantum chemistry methods”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 451 records · Page 25

Enhancing the accuracy of density functional tight binding models through ChIMES many-body interaction potentials

Semi-empirical quantum models such as Density Functional Tight Binding (DFTB) are attractive methods for obtaining quantum simulation data at longer time and length scales than possible with standard approaches. However, application of these models can require lengthy effort due to the lack of a systematic approach for their development. In this work, we discuss the use of the Chebyshev Interaction Model for Efficient Simulation (ChIMES) to create rapidly parameterized DFTB models, which exhibit strong transferability due to the inclusion of many-body interactions that might otherwise be inaccurate. We apply our modeling approach to silicon polymorphs and review previous work on titanium hydride. We also review the creation of a general purpose DFTB/ChIMES model for organic molecules and compounds that approaches hybrid functional and coupled cluster accuracy with two orders of magnitude fewer parameters than similar neural network approaches. In all cases, DFTB/ChIMES yields similar accuracy to the underlying quantum method with orders of magnitude improvement in computational cost. In conclusion, our developments provide a way to create computationally efficient and highly accurate simulations over varying extreme thermodynamic conditions, where physical and chemical properties can be difficult to interrogate directly, and there is historically a significant reliance on theoretical approaches for interpretation and validation of experimental results.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Computational Nanotechnology Program

The objectives are: (1) development of methodological and computational tool for the quantum chemistry study of carbon nanostructures and (2) development of the fundamental understanding of the bonding, reactivity, and electronic structure of carbon nanostructures. Our calculations have continued to play a central role in understanding the outcome of the carbon nanotube macroscopic production experiment. The calculations on buckyonions offer the resolution of a long controversy between experiment and theory. Our new tight binding method offers increased speed for realistic simulations of large carbon nanostructures.

Scuseria, Gustavo E.↗

Optical Extinction-Based 3D Nano-Imaging of WS 2 on Gold

Broad band nano-extinction images recorded in the tip-enhanced optical spectroscopy geometry track the 3D topography of a single layer of WS 2 on Au substrate. The described nano-optical method is complementary to conventional AFM and offers additional information about the buried material-metal interface that is not accessible using conventional topographic imaging. Beyond 3D optical imaging, we observe large variations in the junction plasmon resonance on the nanoscale. The latter is important to understand and account for in tip-enhanced Raman and photoluminescence studies that target low-dimensional materials specifically. Further, our observations and (coherent) optical scattering-based method are also relevant to emerging efforts aimed at exploring strong coupling and Fano interferences in hybrid plasmonic-low dimensional quantum material systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Controllable growth of interpenetrating or random copolymer networks

Interpenetrating and random copolymer networks are vital in a number of industrial applications, including the fabrication of automotive parts, damping materials, and tissue engineering scaffolds. We develop a theoretical model for a process that enables the controlled growth of interpenetrating network (IPNs), or a random copolymer network (RCN) of specified size and mechanical properties. In this process, a primary gel “seed” is immersed into a solution containing the secondary monomer and crosslinkers. After the latter species are absorbed into the primary network, the absorbed monomers are polymerized to form the secondary polymer chains, which then can undergo further crosslinking to form an IPN, or undergo inter-chain exchange with the existing network to form a RCN. The swelling and elastic properties of the IPN and RCN networks can be tailored by modifying the monomer and crosslinker concentrations in the surrounding solution, or by tuning the enthalpic interactions between the primary polymer, secondary monomer and solvent through a proper choice of chemistry. This process can be used repeatedly to fabricate gels with a range of mechanical properties from stiff, rigid materials to soft, flexible networks, allowing the method to meet the materials requirements of a variety of applications.

36 MATERIALS SCIENCE↗

Enhanced electro-osmosis in propylene carbonate salt solutions

Properties of solid–liquid interfaces and surface charge characteristics mediate ionic and molecular transport through porous systems, affecting many processes such as separations. Herein, we report experiments designed to probe the electrochemical properties of solid–liquid interfaces using a model system of a single polyethylene terephthalate (PET) pore in contact with aqueous and propylene carbonate solutions of LiClO 4 . First, the existence and polarity of surface charges were inferred from current–voltage curves recorded when a pore was placed in contact with a LiClO4 concentration gradient. Second, the electro-osmotic transport of uncharged polystyrene particles through the PET pore provided information on the polarity and the magnitude of the pore walls’ zeta potential. Our experiments show that the PET pores become effectively positively charged when in contact with LiClO 4 solutions in propylene carbonate, even though in aqueous LiClO 4 , the same pores are negatively charged. Additionally, the electro-osmotic velocity of the particles revealed a significantly higher magnitude of the positive zeta potential of the pores in propylene carbonate compared to the magnitude of the negative zeta potential in water. The presented methods of probing the properties of solid–liquid interfaces are expected to be applicable to a wide variety of solid and liquid systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Research in Computational Astrobiology

We present results from several projects in the new field of computational astrobiology, which is devoted to advancing our understanding of the origin, evolution and distribution of life in the Universe using theoretical and computational tools. We have developed a procedure for calculating long-range effects in molecular dynamics using a plane wave expansion of the electrostatic potential. This method is expected to be highly efficient for simulating biological systems on massively parallel supercomputers. We have perform genomics analysis on a family of actin binding proteins. We have performed quantum mechanical calculations on carbon nanotubes and nucleic acids, which simulations will allow us to investigate possible sources of organic material on the early earth. Finally, we have developed a model of protobiological chemistry using neural networks.

Chaban, Galina↗

A fast, dense Chebyshev solver for electronic structure on GPUs

Matrix diagonalization is almost always involved in computing the density matrix needed in quantum chemistry calculations. In the case of modest matrix sizes (≲4000), performance of traditional dense diagonalization algorithms on modern GPUs is underwhelming compared to the peak performance of these devices. This motivates the exploration of alternative algorithms better suited to these types of architectures. We newly derive, and present in detail, an existing Chebyshev expansion algorithm whose number of required matrix multiplications scales with the square root of the number of terms in the expansion. Focusing on dense matrices of modest size, our implementation on GPUs results in large speed ups when compared to diagonalization. Additionally, we improve upon this existing method by capitalizing on the inherent task parallelism and concurrency in the algorithm. Furthermore, this improvement is implemented on GPUs by using CUDA and HIP streams via the MAGMA library and leads to a significant speed up over the serial-only approach for smaller (≲1000) matrix sizes. Finally, we apply our technique to a model system with a high density of states around the Fermi level, which typically presents significant challenges.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Localized Active Space Pair-Density Functional Theory

Accurate quantum chemical methods for the prediction of spin-state energy gaps for strongly correlated systems are computationally expensive and scale poorly with the size of the system. This makes calculations for many experimentally interesting molecules impractical even with abundant computational resources. Previous work has shown that the localized active space (LAS) self-consistent field (SCF) method can be an efficient way to obtain multiconfiguration SCF wave functions of comparable quality to the corresponding complete active space (CAS) ones. To obtain quantitative results, a post-SCF method is needed to estimate the complete correlation energy. One such method is multiconfiguration pair-density functional theory (PDFT), which calculates the energy based on the density and on-top pair density obtained from a multiconfiguration wave function. In this work, we introduce localized-active-space PDFT, which uses a LAS wave function for subsequent PDFT calculations. The method is tested by computing spin-state energies and gaps in conjugated organic molecules and a bimetallic compound and comparing to the corresponding CAS-PDFT values.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

ChIMES: A Machine-Learned Interatomic Model Targeting Improved Description of Condensed Phase Chemistry in Energetic Materials

In this report we detail completion of a Physics and Engineering Model Level Two Milestone targeting improved reactive interatomic potentials (IAPs) for energetic materials (EM) through machine learning. The specific goals of this milestone were to develop, validate, and document a new reactive molecular dynamics method for EM, based on machine learning by (1) generating databases of first-principles-derived forces, stresses, and energies for HN3 and 3,4-bis(3-nitrofurazan- 4-yl)furoxan (DNTF) (2) generate atomistic force fields from these databases via ML, and (3) benchmark model performance against first principles calculations. These goals were achieved by (1) further developing a machine learned reactive IAP and generation approach (i.e. the Chebyshev Interaction Model for Efficient Simulation or “ChIMES”), for which resulting IAPs can approach the predictive power of quantum-mechanical approaches at a fraction of the computational expense, and (2) applying the ChIMES framework to develop models for HN3 and DNTF. We find that for simple energetic materials like HN3, high accuracy ChIMES models can be obtained through application of a fitting approach that does not use active machine learning. We demonstrate the suitability of ChIMES models for simulations involving EM by using the HN3 model in multiscale shock technique simulations to predict the HN3 Chapman-Jouguet detonation state and investigate chemical evolution out to 1 ns following shock compression. This model is then used in larger direct shock (DS) simulations for a preliminary investigation of how bubbles (i.e. voids) influence material response under shock compression. We find that more complex EM (i.e. DNTF) necessitate a more sophisticated fitting approach, and develop a new active learning method and python tool to meet this challenge. We demonstrate that this fitting approach yields ChIMES models that out-perform commonly used standard reactive IAPs as well as semi-empirical quantum methods, and discuss the systematic improvability of these actively learned ChIMES models. We also describe challenges related to model development for EM such as DNTF, for which few experimental or previous simulation data are available (e.g. which could otherwise inform generation of training data). To overcome this issue, we establish a semi-empirical quantum ChIMES capability which can be used to efficiently map out relevant thermodynamic and configurational space, and generate ChIMES-IAP training data in a multiscale manner. We also show that these semi-empirical quantum ChIMES models can be used to generate predictions for the shock Hugoniot (the Hugoniot is the locus of thermodynamic states found in a shocked material) equation of state, investigate related thermochemistry, and explore carbon condensation following shock compression. This work represents a substantial advance in our atomistic modeling capability for EM that will provide much needed information on the chemistry of detonation for continued development of continuum models based on the Cheetah thermochemical code.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

On the Efficient Evaluation of the Exchange Correlation Potential on Graphics Processing Unit Clusters

The predominance of Kohn–Sham density functional theory (KS-DFT) for the theoretical treatment of large experimentally relevant systems in molecular chemistry and materials science relies primarily on the existence of efficient software implementations which are capable of leveraging the latest advances in modern high-performance computing (HPC). With recent trends in HPC leading toward increasing reliance on heterogeneous accelerator-based architectures such as graphics processing units (GPU), existing code bases must embrace these architectural advances to maintain the high levels of performance that have come to be expected for these methods. In this work, we purpose a three-level parallelism scheme for the distributed numerical integration of the exchange-correlation (XC) potential in the Gaussian basis set discretization of the Kohn–Sham equations on large computing clusters consisting of multiple GPUs per compute node. In addition, we purpose and demonstrate the efficacy of the use of batched kernels, including batched level-3 BLAS operations, in achieving high levels of performance on the GPU. We demonstrate the performance and scalability of the implementation of the purposed method in the NWChemEx software package by comparing to the existing scalable CPU XC integration in NWChem.

97 MATHEMATICS AND COMPUTING↗

The Future of Bio-technology

Hosts of technologies, most notably in electronics, have been on the path of miniaturization for decades and in 2005 they have crossed the threshold of the nano-scale. Crossing the nano-scale threshold is a milestone in miniaturization, setting impressive new standards for component-packing densities. It also brings technology to a scale at which quantum effects and fault tolerance play significant roles and approaches the feasible physical limit form many conventional "top-down" manufacturing methods. I will suggest that the most formidable manufacturing problems in nanotechnology will be overcome and major breakthroughs will occur in a host of technologies, when nanotechnology converges with bio-technology; i.e. I will argue that the future of bio-technology is in nanotechnology. In 2005, methods in molecular biology, microscopy, bioinformatics, biochemistry, and genetic engineering have focused considerable attention on the nano-scale. On this scale, biology is a kind of recursive chemistry in which molecular recognition, self-assembly, self-organization and self-referencing context-control lead to the emergence of the complexity of structures and processes that are fundamental to all life forms. While we are still far from understanding this complexity, we are on the threshold of being able to use at least some of these biological properties for .technology. I will discuss the use of biomolecules, such as DNA, RNA, and proteins as "tools" for the bio-technologist of the future. More specifically, I will present in some detail an example of how we are using a genetically engineered 60-kDa protein (HSP60) from an organism living in near boiling sulfuric acid to build nano-scale templates for arranging metallic nanoparticles. These "extremophile" HSP60s self-assemble into robust double-ring structures called "chaperonins," which further assemble into filaments and arrays with nanometer accuracy. I will discuss our efforts to use chaperonins to organize quantum dots, electronic and magnetic nano-particles for electronic and photonic applications.

Trent, Jonathan↗

A Quantum-Based Approach to Predict Primary Radiation Damage in Polymeric Networks

Initial atomistic-level radiation damage in chemically reactive materials is thought to induce reaction cascades that can result in undesirable degradation of macroscale properties. Ensembles of quantum-based molecular dynamics (QMD) simulations can accurately predict these cascades, but extracting chemical insights from the many underlying trajectories is a labor-intensive process that can require substantial a priori intuition. We develop here a general and automated graph-based approach to extract all chemically distinct structures sampled in QMD simulations and apply our approach to predict primary radiation damage of polydimethylsiloxane (PDMS), the main constituent of silicones. A postprocessing protocol is developed to identify underlying polymer backbone structures as connected components in QMD trajectories. Furthermore, these backbones form a repository of radiation-damaged structures. A scheme for extracting and updating a library of isomorphically distinct structures is proposed to identify the spanning set and aid chemical interpretation of the repository. The analyses are applied to ensembles of cascade QMD simulations in which the four element types in PDMS are selectively excited in primary knock-on atom events. Our approach reveals a much higher degree of combinatorial complexity in this system than was inferred through radiolysis experiments. Probabilities are extracted for radiation-induced network changes including formation of branch points, carbon linkages, cycles, bond scissions, and carbon uptake into the Si–O siloxane backbone network. The general analysis framework presented here is readily extendable to modeling chemical degradation of other polymers and molecular materials and provides a basis for future quantum-informed multiscale modeling of radiation damage.

36 MATERIALS SCIENCE↗

Quantum Monte Carlo benchmarking of large noncovalent complexes in the L7 benchmark set

We have used diffusion Monte Carlo (DMC) to perform calculations on the L7 benchmark set. DMC is a stochastic numerical integration scheme in real-space and part of a larger set of quantum Monte Carlo methods. The L7 set was designed to test the ability of electronic structure methods to include dispersive interactions. While the agreement between DMC and quantum-chemical state-of-the-art methods is excellent for some of the structures, there are significant differences in others. In contrast to wavefunction-based quantum chemical methods, DMC is a first-principle many-body method with the many-body wavefunction evolving in real space. It includes explicitly all electron–electron interactions and is relatively insensitive to the size of the basis set.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

High Accuracy Transition Metal Effective Cores for the Many-Body Diffusion Monte Carlo Method

Practical applications of the real-space diffusion Monte Carlo (DMC) method require the removal of core electrons, where currently localization approximations of semilocal potentials are generally used in the projector. Accurate calculations of complex solids and large molecules demand minimizing the impact of approximated atomic cores. Prior works have shown that the errors from such approximations can be sizable in both finite and periodic systems. In this work, we show that a class of differential pseudopotentials, known as pseudo-Hamiltonians, can be constructed for the 3d transition metal atoms, entirely removing the need for any localization scheme in the DMC projector. As a proof of principle, we demonstrate the approach for the case of Co. In order to minimize errors in the pseudo-Hamiltonian at the many-body level, we generalize the recently proposed correlation-consistent pseudopotential generation scheme to successively close semilocal representations of the differential potentials. Our generation scheme successfully produces potentials tailored specifically for real space projector quantum Monte Carlo methods with low error at the many-body level, i.e., with many-body scattering properties very close to relativistic all-electron results. In particular, we show that the agreement with respect to atomic and molecular quantities reach chemical accuracy in many cases-on par with the most accurate semilocal pseudopotentials available. Further, our pseudo-Hamiltonian generation scheme utilizes standard quantum chemistry codes designed only to work with semilocal pseudopotentials, enabling straightforward generation of pseudo-Hamiltonians for additional elements in future works.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Ab initio methods for polariton chemistry

Polariton chemistry exploits the strong interaction between quantized excitations in molecules and quantized photon states in optical cavities to affect chemical reactivity. Molecular polaritons have been experimentally realized by the coupling of electronic, vibrational, and rovibrational transitions to photon modes, which has spurred a tremendous theoretical effort to model and explain how polariton formation can influence chemistry. This tutorial review focuses on computational approaches for the electronic strong coupling problem through the combination of familiar techniques from ab initio electronic structure theory and cavity quantum electrodynamics, toward the goal of supplying predictive theories for polariton chemistry. Our aim is to emphasize the relevant theoretical details with enough clarity for newcomers to the field to follow, and to present simple and practical code examples to catalyze further development work.

Chemistry↗

Simulations of nonradiative processes in semiconductor nanocrystals

The description of carrier dynamics in spatially confined semiconductor nanocrystals (NCs), which have enhanced electron–hole and exciton–phonon interactions, is a great challenge for modern computational science. These NCs typically contain thousands of atoms and tens of thousands of valence electrons with discrete spectra at low excitation energies, similar to atoms and molecules, that converge to the continuum bulk limit at higher energies. Computational methods developed for molecules are limited to very small nanoclusters, and methods for bulk systems with periodic boundary conditions are not suitable due to the lack of translational symmetry in NCs. This perspective focuses on our recent efforts in developing a unified atomistic model based on the semiempirical pseudopotential approach, which is parameterized by first-principle calculations and validated against experimental measurements, to describe two of the main nonradiative relaxation processes of quantum confined excitons: exciton cooling and Auger recombination. Here, we focus on the description of both electron–hole and exciton–phonon interactions in our approach and discuss the role of size, shape, and interfacing on the electronic properties and dynamics for II–VI and III–V semiconductor NCs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

F and N Rich Solid Electrolyte for Stable All-Solid-State Battery

We report the instability of sulfide solid electrolytes to Li anode and high-voltage LiNi 0.8 Mn 0.1 Co 0.1 O 2 (NMC811) cathodes limits the cyclic performance of all-solid-state lithium battery (ASSLB). Herein, the stability of Li 6 PS 5 Cl against Li anode is enhanced by mixing a small amount (0.32 wt%) of CuF 2 -LiNO 3 (CL) into Li 6 PS 5 Cl electrolyte layer to in-situ form a mixed-conductive-lithiophobic and self-healing LiF-Li 3 N-Cu solid electrolyte interphase (SEI) at Li 6 PS 5 Cl-CL/Li interface. The critical current density (CCD) of Li 6 PS 5 Cl-CuF 2 -LiNO 3 increases to 1.4 mA cm –2 /1.4 mAh cm –2 at room temperature, which is much higher than that of pristine Li 6 PS 5 Cl (0.4 mA cm –2 /0.4 mAh cm –2 ) even though mixing 0.32 wt% CL into Li 6 PS 5 Cl slightly reduces the ionic conductivity from 2.9 × 10 –3 to 1.5 × 10 –3 S cm –1 . The compatibility of Li 6 PS 5 Cl-CL electrolyte to single-crystalline NMC811 (S-NMC811) is further enhanced by adding a small amount (0.02 wt%) of AlF 3 into Li 6 PS 5 Cl-CL forming Li 6 PS 5 Cl-CuF 2- LiNO 3 -AlF 3 (Li 6 >PS 5 Cl-CLA) as a cathode electrolyte and by doing Cl – on S-NMC811 (Cl@S-NMC811) surface. The Cl@S-NMC811-Li 6 PS 5 Cl-CLA|Li 6 PS 5 Cl-CL|Li cells with areal capacity of 2.55 mAh cm -2 achieve a capacity retention of 69.4% after 100 cycles at 1C (1C = 200 mAh g -1 ). Adding a small amount of SEI and cathode/electrolyte interphase (CEI) former into the sulfide electrolytes with minimal reduction (48.3%) of ionic conductivity is an effective method to enhance the performance of ASSLB.

36 MATERIALS SCIENCE↗