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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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32 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Artificial neural network potentials for mechanics and fracture dynamics of two-dimensional crystals **

Understanding the mechanics and failure of materials at the nanoscale is critical for their engineering and applications. The accurate atomistic modeling of brittle failure with crack propagation in covalent crystals requires a quantum mechanics-based description of individual bond-breaking events. Artificial neural network potentials (NNPs) have emerged to overcome the traditional, physics-based modeling tradeoff between accuracy and accessible time and length scales. Previous studies have shown successful applications of NNPs for describing the structure and dynamics of molecular systems and amorphous or liquid phases of materials. However, their application to deformation and failure processes in materials is still uncommon. In this study, we discuss the apparent limitations of NNPs for the description of deformation and fracture under loadings and propose a way to generate and select training data for their employment in simulations of deformation and fracture simulations of crystals. We applied the proposed approach to 2D crystalline graphene, utilizing the density-functional tight-binding method for more efficient and extensive data generation in place of density functional theory. Then, we explored how the data selection affects the accuracy of the developed artificial NNPs. It revealed that NNP’s reliability should not only be measured based on the total energy and atomic force comparisons for reference structures but also utilize comparisons for physical properties, e.g. stress–strain curves and geometric deformation. In sharp contrast to popular reactive bond order potentials, our optimized NNP predicts straight crack propagation in graphene along both armchair and zigzag (ZZ) lattice directions, as well as higher fracture toughness of ZZ edge direction. Our study provides significant insight into crack propagation mechanisms on atomic scales and highlights strategies for NNP developments of broader materials.

2D materials↗
DOE OSTI2024

Scaling Ensembles of Data-Intensive Quantum Chemical Calculations for Millions of Molecules

Deep learning models are efficient computational tools that can accelerate the inverse design of molecules with desired functional properties by generating predictions at a fraction of the time required by traditional quantum chemical approaches. To ensure that a model maintains accuracy and transferability across broad regions of the chemical space explored during the inverse design, it must be trained on massively large volumes of simulation data. This requires running large-scale ensemble quantum chemical calculations on high-performance computing (HPC) systems for data collection. However, the efficient execution of such large ensemble calculations and the management of large volumes of output data require tools that can judiciously utilize computational resources and manage metadata overhead on the file system. Therefore, we present a high-performance, scalable, ensemble management framework for performing data-intensive quantum chemical electronic structure calculations for organic molecules. This framework provides abstractions to plug different ab initio, first principles, and first principles-based semi-empirical methods and executes them efficiently at large scale on HPC systems. It dynamically distributes tasks to resources and uses tiered storage for managing large collections of files. We employed this framework to process over ten million organic molecules and generate open-source datasets that provide UV-vis absorption spectra by running time-dependent density-functional tight-binding calculations. It is the largest database containing molecular optical spectra that were simulated with quantum chemical methods in a consistent manner.

Mehta, Kshitij↗
DOE OSTI2022

Computational Workflow for Accelerated Molecular Design Using Quantum Chemical Simulations and Deep Learning Models

Efficient methods for searching the chemical space of molecular compounds are needed to automate and accelerate the design of new functional molecules such as pharmaceuticals. Given the high cost in both resources and time for experimental efforts, computational approaches play a key role in guiding the selection of promising molecules for further investigation. Here, we construct a workflow to accelerate design by combining approximate quantum chemical methods [i.e. density-functional tight-binding (DFTB)], a graph convolutional neural network (GCNN) surrogate model for chemical property prediction, and a masked language model (MLM) for molecule generation. Property data from the DFTB calculations are used to train the surrogate model; the surrogate model is used to score candidates generated by the MLM. The surrogate reduces computation time by orders of magnitude compared to the DFTB calculations, enabling an increased search of chemical space. Furthermore, the MLM generates a diverse set of chemical modifications based on pre-training from a large compound library. We utilize the workflow to search for near-infrared photoactive molecules by minimizing the predicted HOMO-LUMO gap as the target property. Our results show that the workflow can generate optimized molecules outside of the original training set, which suggests that iterations of the workflow could be useful for searching vast chemical spaces in a wide range of design problems.

Blanchard, Andrew↗
DOE OSTI2023

SQMBox: Interfacing a semiempirical integral library to modular ab initio electronic structure enables new semiempirical methods

Ab initio and semiempirical electronic structure methods are usually implemented in separate software packages or use entirely different code paths. As a result, it can be time-consuming to transfer an established ab initio electronic structure scheme to a semiempirical Hamiltonian. Here we present an approach to unify ab initio and semiempirical electronic structure code paths based on a separation of the wavefunction ansatz and the needed matrix representations of operators. With this separation, the Hamiltonian can refer to either an ab initio or semiempirical treatment of the resulting integrals. We built a semiempirical integral library and interfaced it to the GPU-accelerated electronic structure code TeraChem. Equivalency between ab initio and semiempirical tight-binding Hamiltonian terms is assigned according to their dependence on the one-electron density matrix. The new library provides semiempirical equivalents of the Hamiltonian matrix and gradient intermediates, corresponding to those provided by the ab initio integral library. This enables the straightforward combination of semiempirical Hamiltonians with the full pre-existing ground and excited state functionality of the ab initio electronic structure code. We demonstrate the capability of this approach by combining the extended tight-binding method GFN1-xTB with both spin-restricted ensemble-referenced Kohn–Sham and complete active space methods. We also present a highly efficient GPU implementation of the semiempirical Mulliken-approximated Fock exchange. The additional computational cost for this term becomes negligible even on consumer-grade GPUs, enabling Mulliken-approximated exchange in tight-binding methods for essentially no additional cost.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Plasmon-induced excitation energy transfer in silver nanoparticle dimers: A real-time TDDFTB investigation

Here, using real-time quantum dynamics calculations, we perform theoretical investigations of light-induced interactions and electronic excitation transfer in a silver nanoparticle dimer. Real-time time-dependent density functional tight-binding (RT-TDDFTB) calculations provide details of the quantum dynamical processes at an electronic/atomistic level with attosecond resolution. The computational efficiency of RT-TDDFTB allows us to examine electronic dynamics up to picosecond time scales. With time scales varying over six orders of magnitude, we provide insight into interactions between the nanoparticle and laser and between nanoparticles. Our results show that the coupling between nanoparticle monomers is dependent on the separation distance between the nanoparticles in the dimer. As the interparticle distance is varied, the dipole–dipole interactions and electronic excitation transfer mechanisms are markedly different. At large distances (from 50 to 20 Å), the energy transfer from NP1 to NP2 becomes more efficient as the interparticle distance decreases. The total dipole moment of the Ag 14 nanoparticle dimer increases linearly at an interparticle distance of 20 Å and reaches its maximum after 1.2 ps. The electronic excitation transfer is also the most efficient at 20 Å. At short distances, back-transfer effects reduce the ability of the dimer and NP1 to accept energy from the incident electric field. We attribute the distance-dependent features of the nanoparticle dimer to the beating between the laser acting on NP1 and the back transfer from NP2 to NP1.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Investigating 3,4-bis(3-nitrofurazan-4-yl)furoxan detonation with a rapidly tuned density functional tight binding model

In this work, we describe a machine learning approach to rapidly tune density functional tight binding models for the description of detonation chemistry in organic molecular materials. Resulting models enable simulations on the several 10s of ps scales characteristic to these processes, with “quantum-accuracy.” We use this approach to investigate early shock chemistry in 3,4-bis(3-nitrofurazan-4-yl)furoxan, a hydrogen-free energetic material known to form onion-like nanocarbon particulates following detonation. We find that the ensuing chemistry is significantly characterized by the formation of large C x N y O z species, which are likely precursors to the experimentally observed carbon condensates. Beyond utility as a means of investigating detonation chemistry, the present approach can be used to generate quantum-based reference data for the development of full machine-learned interatomic potentials capable of simulation on even greater time and length scales, i.e., for applications where characteristic time scales exceed the reach of methods including Kohn–Sham density functional theory, which are commonly used for reference data generation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Erratum: “DFTB+, a software package for efficient approximate density functional theory based atomistic simulations” [J. Chem. Phys. 152, 124101 (2020)]

The implementation of the GPU support in DFTB+, as described in Sec. III C of the original publication,1 was developed based on a previous unpublished implementation by Jacek Jakowski. In order to acknowledge his work on this first implementation, the authors of the original publication wish to include J. Jakowski as co-author. The scientific content of the original publication is not affected.

42 ENGINEERING↗
DOE OSTI2023

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↗
DOE OSTI2024

Laser pulse induced second- and third-harmonic generation of gold nanorods with real-time time-dependent density functional tight binding (RT-TDDFTB) method

In this paper, we investigate second- and third-harmonic generation processes in Au nanorod systems using the real-time time-dependent density functional tight binding method. Our study focuses on the computation of nonlinear signals based on the time dependent dipole response induced by linearly polarized laser pulses interacting with nanoparticles. We systematically explore the influence of various laser parameters, including pump intensity, duration, frequency, and polarization directions, on harmonic generation. We demonstrate all the results using Au nanorod dimer systems arranged in end-to-end configurations, and disrupting the spatial symmetry of regular single nanorod systems is crucial for second-harmonic generation processes. Furthermore, we study the impact of nanorod lengths, which lead to variable plasmon energies, on harmonic generation, and estimates of polarizabilities and hyper-polarizabilities are provided.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

Supplementary Material for ORNL_AISD-Ex

This dataset provides supplementary material for the previously published dataset ORNL_AISD-Ex (1), which is available at the following website: https://www.osti.gov/biblio/1907919 The dates comprises one compressed folder called ornl_aisd_ex.zip. The compressed folder ornl_aisd_ex.zip contains 1,000 CSV files, each of them titled ornl_aisd_ex_ID.csv, where ID is a number that ranges between 1 and 1,000. The information contained in ornl_aisd_ex_ID.csv corresponds to the information of the molecules compresses inside the ornl_aisd_ex_ID.tar.gz of the dataset ORNL_AISD-Ex. Each row in each .CSV file is associated with a molecule, and the columns contain the following information: 1) molecules ID 2) SMILES string representation 3) DFTB-PE (eV): formation energy 4) first 50 electronic excitation modes 5) oscillators strengths of the first 50 electronic excitation modes The compression of this information into CSV files will allow a more agile extraction and management of information to the users that do not have access to large scale HPC platforms. REFERENCES (1) Lupo Pasini, Massimiliano, Mehta, Kshitij, Yoo, Pilsun, and Irle, Stephan. ORNL_AISD-Ex: Quantum chemical prediction of UV/Vis absorption spectra for over 10 million organic molecules. United States: N. p., 2023. Web. doi:10.13139/OLCF/1907919.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

ORNL_AISD_DL-HLgap

This dataset provides supplementary molecular dataset of Deep Learning Workflow for the Inverse Design of Molecules with Specific Optoelectronic Properties. The dataset comprises three main directories such as GDB-9_dataset, Low_HL_Gap_dataset, and High_HL_Gap_dataset which individually has csv files, smiles_txt files, pdb files and xyz files containing information of molecular structures, properties and coordinates generated from deep learning workflow using generative model, surrogate model and DFTB calculation results. GDB-9_dataset contains the molecular data extracted from the original GDB-9 dataset with additional data of DFTB HL gap, surrogate HL gap and molecular property analysis. (the number of atoms, aromaticity and double bond equivalent) Low_HL_Gap_dataset and High_HL_Gap_dataset contains series of dataset for different generations with further split to train and test dataset that were obtained from the iterative workflow described in the manuscript. Additional directory Chemiscope_visualization in Low_HL_Gap_dataset directory contains compressed json files to visualize molecules using chemiscope.org page or application to help readers examine generated molecules.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Accurate tight-binding model for twisted bilayer graphene describes topological flat bands without geometric relaxation

A major hurdle in understanding the phase diagram of twisted bilayer graphene is the roles of lattice relaxation and electronic structure on isolated band flattening near magic twist angles. Here in this work, the authors develop an accurate local environment tight-binding model fit to tight-binding parameters computed from ab initio density-functional theory calculations across many atomic configurations. With the accurate parametrization, it is found that the magic angle shifts to slightly lower angles than often quoted, from around 1.05° to around 0.99°, and that isolated flat bands appear for rigidly rotated graphene layers, with enhancement of the flat bands when the layers are allowed to distort. Study of the orbital localization supports the emergence of fragile topology in the isolated flat bands without the need for lattice relaxation.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A p -orbital honeycomb-Kagome lattice realized in a two-dimensional metal-organic framework

The experimental realization of p-orbital systems is desirable because p-orbital lattices have been proposed theoretically to host strongly correlated electrons that exhibit exotic quantum phases. Here, we synthesize a two-dimensional Fe-coordinated bimolecular metal-organic framework which constitutes a honeycomb lattice of 1,4,5,8,9,12-hexaazatriphenylene molecules and a Kagome lattice of 5,15-di(4-pyridyl)-10,20-diphenylporphyrin molecules on a Au(111) substrate. Density-functional theory calculations show that the framework features multiple well-separated spin-polarized Kagome bands, namely Dirac cone bands and Chern flat bands, near the Fermi level. Using tight-binding modelling, we reveal that these bands are originated from two effects: the low-lying molecular orbitals that exhibit p-orbital characteristics and the honeycomb-Kagome lattice. This study demonstrates that p-orbital Kagome bands can be realized in metal-organic frameworks by using molecules with molecular orbitals of p-orbital like symmetry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Structures and localized vibrational states of defects in graphite by tight-binding calculations

The structural and vibrational properties of pristine graphite and point defects in graphite are studied by tight-binding (TB) calculations using a three-center TB potential model. We showed that the three-center TB potential without ‘‘ad hoc’’ van der Waals interaction corrections can accurately describe the inter-layer distance of graphite and the lowest-energy structures and stabilities of typical point defects in graphite. The results from our TB calculations are in good agreement with those from density-functional theory calculations with van der Waals interaction corrections. We also investigated the vibrational properties to gain better understanding on the localization of vibrational states induced by the point defects. Our calculation results show that although localized or quasi-localized vibrational modes can be found in all defected graphite, the localization induced by Frenkel pair, dual-vacancy, and dual-interstitial defects is much stronger. Atomic displacements associated with the localized vibrational modes induced by these three point defects are also analyzed

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗