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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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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

A generalized deep learning approach for local structure identification in molecular simulations

Identifying local structure in molecular simulations is of utmost importance. The most common existing approach to identify local structure is to calculate some geometrical quantity referred to as an order parameter. In simple cases order parameters are physically intuitive and trivial to develop (e.g., ion-pair distance), however in most cases, order parameter development becomes a much more difficult endeavor (e.g., crystal structure identification). Using ideas from computer vision, we adapt a specific type of neural network called a PointNet to identify local structural environments in molecular simulations. A primary challenge in applying machine learning techniques to simulation is selecting the appropriate input features. This challenge is system-specific and requires significant human input and intuition. In contrast, our approach is a generic framework that requires no system-specific feature engineering and operates on the raw output of the simulations, i.e., atomic positions. We demonstrate the method on crystal structure identification in Lennard-Jones (four different phases), water (eight different phases), and mesophase (six different phases) systems. The method achieves as high as 99.5% accuracy in crystal structure identification. The method is applicable to heterogeneous nucleation and it can even predict the crystal phases of atoms near external interfaces. We demonstrate the versatility of our approach by using our method to identify surface hydrophobicity based solely upon positions and orientations of surrounding water molecules. Our results suggest the approach will be broadly applicable to many types of local structure in simulations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Transfer Learning Meets Embedded Correlated Wavefunction Theory for Chemically Accurate Molecular Simulations: Application to Calcium Carbonate Ion Pairing

Achieving chemical accuracy for molecular simulations remains a central challenge in computational chemistry. Here, we present an embedded correlated wavefunction transfer learning (ECW-TL) framework for accurately simulating molecular dynamics in the condensed phase. ECW-TL incorporates high-level electron exchange and correlation effects in ECW theory while preserving the training and computational efficiency of machine-learned interatomic potentials. We demonstrate the framework on Ca 2+ –CO 3 2– ion pairing in aqueous solution, a key process underlying CO 2 mineralization in seawater. As proof of principle, we first show that fine-tuning a DFT-revPBE-D3(BJ) baseline model with embedded-DFT-SCAN data reproduces the DFT-SCAN free-energy surface within 1 kcal/mol across all solvation states. Extending the framework to embedded MP2 and localized natural-orbital CCSD(T) further refines the free-energy profile, revealing the crucial role of exact electron exchange and correlation in determining ion-pair stability and structure. The computed ion-pair association free energy is in quantitative agreement with experimental measurements, further validating the accuracy of the ECW-TL framework. ECW-TL thus provides a general, data-efficient route for transferring CW accuracy to efficient simulations of complex aqueous and interfacial chemical processes.

cluster chemistry↗
DOE OSTI2014

Temperature dependence of creep compliance of highly cross-linked epoxy: A molecular simulation study

We have used molecular dynamics (MD) simulations to study the effect of temperature on the creep compliance of neat cross-linked epoxy. Experimental studies of mechanical behavior of cross-linked epoxy in literature commonly report creep compliance values, whereas molecular simulations of these systems have primarily focused on the Young’s modulus. In this work, in order to obtain a more direct comparison between experiments and simulations, atomistically detailed models of the cross-linked epoxy are used to study their creep compliance as a function of temperature using MD simulations. The creep tests are performed by applying a constant tensile stress and monitoring the resulting strain in the system. Our results show that simulated values of creep compliance increase with an increase in both time and temperature. We believe that such calculations of the creep compliance, along with the use of time temperature superposition, hold great promise in connecting the molecular insight obtained from molecular simulation at small length- and time-scales with the experimental behavior of such materials. To the best of our knowledge, this work is the first reported effort that investigates the creep compliance behavior of cross-linked epoxy using MD simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Adsorption of Aqueous Crude Oil Components on the Basal Surfaces of Clay Minerals: Molecular Simulations Including Salinity and Temperature Effects

Molecular simulations of the adsorption of representative organic molecules onto the basal surfaces of various clay minerals were used to assess the mechanisms of enhanced oil recovery associated with salinity changes and water flooding. Simulations at the density functional theory (DFT) and classical levels provide insights into the molecular structure, binding energy, and interfacial behavior of saturate, aromatic, and resin molecules near clay mineral surfaces. Periodic DFT calculations reveal binding geometries and ion pairing mechanisms at mineral surfaces while also providing a basis for validating the classical force field approach. Through classical molecular dynamics simulations, the influence of aqueous cations at the interface and the role of water solvation are examined to better evaluate the dynamical nature of cation-organic complexes and their co-adsorption onto the clay surfaces. The extent of adsorption is controlled by the hydrophilic nature and layer charge of the clay mineral. All organic species studied showed preferential adsorption on hydrophobic mineral surfaces. However, the anionic form of the resin (decahydro-2-naphthoic acid)—expected to be prevalent at near-neutral pH conditions in petroleum reservoirs—readily adsorbs to the hydrophilic kaolinite surface through a combination of cation pairing and hydrogen bonding with surface hydroxyl groups. Analysis of cation-organic pairing in both the adsorbed and desorbed states reveals a strong preference for organic anions to coordinate with divalent calcium ions rather than monovalent sodium ions, lending support to current theories regarding low-salinity water flooding.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Geochemical Reaction Mechanism Discovery from Molecular Simulation

Methods to explore reactions using computer simulation are becoming increasingly quantitative, versatile, and robust. In this review, a rationale for how molecular simulation can help build better geochemical kinetics models is first given. We summarize some common methods that geochemists use to simulate reaction mechanisms, specifically classical molecular dynamics and quantum chemical methods and discuss their strengths and weaknesses. Useful tools such as umbrella sampling and metadynamics that enable one to explore reactions are discussed. Several case studies wherein geochemists have used these tools to understand reaction mechanisms are presented, including water exchange and sorption on aqueous species and mineral surfaces, surface charging, crystal growth and dissolution, and electron transfer. The impact that molecular simulation has had on our understanding of geochemical reactivity are highlighted in each case. In the future, it is anticipated that molecular simulation of geochemical reaction mechanisms will become more commonplace as a tool to validate and interpret experimental data, and provide a check on the plausibility of geochemical kinetic models.

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

Improving scalability of electronic structure code for molecular simulations in the presence of environment

A scalable density functional electronic code with Gaussian basis set, called UTEP-NRLMOL, is developed to perform simulations of molecular systems in the presence of the environment with particular attention to the memory requirements. In the electronic structure calculations, the memory and computation time are proportional to the number of atoms. Memory requirements for density functional calculations scale as N*N, where N is the number of atoms. While the recent advances in HPC offer platforms with large numbers of cores, the limited amount of memory available on a given node and poor scalability of the electronic structure codes hinder their efficient usage of these platforms. We have introduced new scaling and parallelization paradigms using MPI-3 shared-memory functionality combined with usage of sparse algebra and storage of matrices in sparse format. This extends the range of applicability of the UTEP-NRLMOL code to large systems over 10,000 atoms, or using up to 67,000 basis functions, and making use of HPC architectures using over 6,000 processors utilizing all available cores. We have also interfaced code with effective fragment potential and polarizable continuum model libraries. The code was used in simulations of several applications which are published in reputed scientific journals.

74 ATOMIC AND MOLECULAR PHYSICS↗
NASA NTRS2017

Dissociation and Internal Excitation of Molecular Nitrogen Due to N + N2 Collisions Using Direct Molecular Simulation

In this work we present a molecular level study of N2+N collisions, focusing on excitation of internal energy modes and non-equilibrium dissociation. The computation technique used here is the direct molecular simulation (DMS) method and the molecular interactions have been modeled using an ab−initio potential energy surface (PES) developed at NASA's Ames Research Center. We carried out vibrational excitation calculations between 5000K and 30000K and found that the characteristic vibrational excitation time for the N + N2 process was an order of magnitude lower than that predicted by the Millikan and White correlation. It is observed that during vibrational excitation the high energy tail of the vibrational energy distribution gets over populated first and the lower energy levels get populated as the system evolves. It is found that the non-equilibrium dissociation rate coefficients for the N + N2 process are larger than those for the N2 + N2 process. This is attributed to the non-equilibrium vibrational energy distributions for the N + N2 process being less depleted than that for the N2 +N2 process. For an isothermal simulation we find that the probability of dissociation goes as 1/T(sub tr) for molecules with internal energy (epsilon(sub int)) less than approximately 9.9eV, while for molecules with epsilon (sub int) greater than 9.9eV the dissociation probability was weakly dependent on translational temperature of the system. We compared non-equilibrium dissociation rate coefficients and characteristic vibrational excitation times obtained by using the ab-initio PES developed at NASA's Ames Research Center to those obtained by using an ab-initio PES developed at the University of Minnesota. Good agreement was found between the macroscopic properties and molecular level description of the system obtained by using the two PESs.

Grover, Maninder S.↗
DOE OSTI2022

Combining Physics and Machine Learning for the Next Generation of Molecular Simulation

Simulating molecules and atomic systems at quantum accuracy is a grand challenge for science in the 21 st century. Quantum-accurate simulations would enable the design of new medicines and the discovery of new materials. The defining problem in this challenge is that quantum calculations on large molecules, like proteins or DNA, are fundamentally impossible with current algorithms. In this work, we explore a range of different methods that aim to make large, quantum-accurate simulations possible. We show that using advanced classical models, we can accurately simulate ion channels, an important biomolecular system. We show how advanced classical models can be implemented in an exascale-ready software package. Lastly, we show how machine learning can learn the laws of quantum mechanics from data and enable quantum electronic structure calculations on thousands of atoms, a feat that is impossible for current algorithms. Altogether, this work shows that combining advances in physics models, computing, and machine learning, we are moving closer to the reality of accurately simulating our molecular world.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2014

Accelerating Monte Carlo molecular simulations by reweighting and reconstructing Markov chains: Extrapolation of canonical ensemble averages and second derivatives to different temperature and density conditions

Accurate determination of thermodynamic properties of petroleum reservoir fluids is of great interest to many applications, especially in petroleum engineering and chemical engineering. Molecular simulation has many appealing features, especially its requirement of fewer tuned parameters but yet better predicting capability; however it is well known that molecular simulation is very CPU expensive, as compared to equation of state approaches. We have recently introduced an efficient thermodynamically consistent technique to regenerate rapidly Monte Carlo Markov Chains (MCMCs) at different thermodynamic conditions from the existing data points that have been pre-computed with expensive classical simulation. This technique can speed up the simulation more than a million times, making the regenerated molecular simulation almost as fast as equation of state approaches. In this paper, this technique is first briefly reviewed and then numerically investigated in its capability of predicting ensemble averages of primary quantities at different neighboring thermodynamic conditions to the original simulated MCMCs. Moreover, this extrapolation technique is extended to predict second derivative properties (e.g. heat capacity and fluid compressibility). The method works by reweighting and reconstructing generated MCMCs in canonical ensemble for Lennard-Jones particles. In this paper, system's potential energy, pressure, isochoric heat capacity and isothermal compressibility along isochors, isotherms and paths of changing temperature and density from the original simulated points were extrapolated. Finally, an optimized set of Lennard-Jones parameters (ε, σ) for single site models were proposed for methane, nitrogen and carbon monoxide.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Sampling Rare Events in Aqueous Systems Using Molecular Simulations

Birth of a new distinct phase is a phenomenon encountered in a myriad of processes, and has wide ranging consequences in material processing, biological self-assembly, separations and several other processes. Several phase transitions are nucleation driven. The nucleation events occur over nanosecond timescales and involve hundreds to thousands of molecules. These length and timescales are difficult to access in experiments, thereby making experimental studies of nucleation challenging. On the other hand, molecular simulations sample the nanosecond and nanometer scales making them ideal to study nucleation. However, nucleation is a rare event, meaning that the waiting time to observe one nucleation event is significant. This makes simulation studies of rare events challenging. The project focused on a multi-pronged approach to address such challenges to develop the next generation rare event sampling methods for molecular simulations. The key outcomes of our work include developing more effective methods for sampling rare events, utilizing machine learning to better elucidate nucleation mechanisms, development of software for easy implementation of the methodologies, and applications of the methods to realistic systems to push the method applicability beyond model systems. Overall, this work has enabled pushing the frontiers of molecular simulations to study rare events with a focus on nucleation in aqueous solutions.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Scalable and fast heterogeneous molecular simulation with predictive parallelization schemes

Multiscale and inhomogeneous molecular systems are challenging topics in the field of molecular simulation. In particular, modeling biological systems in the context of multiscale simulations and exploring material properties are driving a permanent development of new simulation methods and optimization algorithms. In computational terms, those methods require parallelization schemes that make a productive use of computational resources for each simulation and from its genesis. Here, we introduce the heterogeneous domain decomposition approach, which is a combination of an heterogeneity-sensitive spatial domain decomposition with an a priori rearrangement of subdomain walls. Within this approach and paper, the theoretical modeling and scaling laws for the force computation time are proposed and studied as a function of the number of particles and the spatial resolution ratio. We also show the new approach capabilities, by comparing it to both static domain decomposition algorithms and dynamic load-balancing schemes. Specifically, two representative molecular systems have been simulated and compared to the heterogeneous domain decomposition proposed in this work. Finally, these two systems comprise an adaptive resolution simulation of a biomolecule solvated in water and a phase-separated binary Lennard-Jones fluid.

36 MATERIALS SCIENCE↗
DOE OSTI2021

BETO 2021 Peer Review - Inverse Bioproduct Design Through Machine Learning and Molecular Simulation

This work aims to identify performance advantaged bioproducts (PABPs) through property prediction, which will guide experimental synthesis. The impact of this work will be faster market adoption of bioproducts with greater performance relative to incumbent products. We have identified >106 bioproduct candidates, but only some will have superior performance to create a market pull. High-throughput property prediction, enabled by machine learning, and elucidation of structure-function relationships, enabled by molecular simulation, provide a hypothesis driven approach for down selection of candidate biomolecules to pursue experimentally. To enable machine learning and molecular simulation for bioproduct discovery, automated structure generation and embedding must capture relevant features for prediction, databases must cover domains applicable to biobased products, and best practices for simulation of polymer systems must be developed. To address these challenges, we have established bioproduct relevant datasets, developed high-throughput polymer structure generation, and built end-to-end neural networks that have predicted 8 properties for >1.4 x 106 biopolymers. A molecular simulation pipeline for building, running, and analyzing polymers and polymer additives is being used to predict performance and develop design principles of biobased products. In collaboration with the PABP synthesis project, these computational tools are guiding synthesis and informing design of PABPs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2016

Single-site Lennard-Jones models via polynomial chaos surrogates of Monte Carlo molecular simulation

In this work, two Polynomial Chaos (PC) surrogates were generated to reproduce Monte Carlo (MC) molecular simulation results of the canonical (single-phase) and the NVT-Gibbs (two-phase) ensembles for a system of normalized structureless Lennard-Jones (LJ) particles. The main advantage of such surrogates, once generated, is the capability of accurately computing the needed thermodynamic quantities in a few seconds, thus efficiently replacing the computationally expensive MC molecular simulations. Benefiting from the tremendous computational time reduction, the PC surrogates were used to conduct large-scale optimization in order to propose single-site LJ models for several simple molecules. Experimental data, a set of supercritical isotherms, and part of the two-phase envelope, of several pure components were used for tuning the LJ parameters (ε, σ). Based on the conducted optimization, excellent fit was obtained for different noble gases (Ar, Kr, and Xe) and other small molecules (CH{sub 4}, N{sub 2}, and CO). On the other hand, due to the simplicity of the LJ model used, dramatic deviations between simulation and experimental data were observed, especially in the two-phase region, for more complex molecules such as CO{sub 2} and C{sub 2} H{sub 6}.

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

Using molecular simulation to understand the skin barrier

Skin's effectiveness as a barrier to permeation of water and other chemicals rests almost entirely in the outermost layer of the epidermis, the stratum corneum (SC), which consists of layers of corneocytes surrounded by highly organized lipid lamellae. As the only continuous path through the SC, transdermal permeation necessarily involves diffusion through these lipid layers. The role of the SC as a protective barrier is supported by its exceptional lipid composition consisting of ceramides (CERs), cholesterol (CHOL), and free fatty acids (FFAs) and the complete absence of phospholipids, which are present in most biological membranes. Molecular simulation, which provides molecular level detail of lipid configurations that can be connected with barrier function, has become a popular tool for studying SC lipid systems. We review this ever-increasing body of literature with the goals of (1) enabling the experimental skin community to understand, interpret and use the information generated from the simulations, (2) providing simulation experts with a solid background in the chemistry of SC lipids including the composition, structure and organization, and barrier function, and (3) presenting a state of the art picture of the field of SC lipid simulations, highlighting the difficulties and best practices for studying these systems, to encourage the generation of robust reproducible studies in the future. This review describes molecular simulation methodology and then critically examines results derived from simulations using atomistic and then coarse-grained models.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Combining Molecular Simulations and Machine Learning to Comprehensively Explore Adsorption Space (Final Report)

This is the final report for the project "Combining Molecular Simulations and Machine Learning to Comprehensively Explore Adsorption Space". The central aim of this project was to develop modeling tools that vastly increase the diversity of molecules for which reliable adsorption isotherms in nanoporous materials are available. Within the project, molecular simulation data for a diverse set of examples was combined with machine-learning (ML) methods to rapidly predict adsorption equilibria in crystalline nanoporous materials (primarily MOFs).

36 MATERIALS SCIENCE↗
DOE OSTI2015

Specific interactions between DNA and regulatory protein controlled by ligand-binding: Ab initio molecular simulation

The catabolite activator protein (CAP) is one of the regulatory proteins controlling the transcription mechanism of gene. Biochemical experiments elucidated that the complex of CAP with cyclic AMP (cAMP) is indispensable for controlling the mechanism, while previous molecular simulations for the monomer of CAP+cAMP complex revealed the specific interactions between CAP and cAMP. However, the effect of cAMP-binding to CAP on the specific interactions between CAP and DNA is not elucidated at atomic and electronic levels. We here considered the ternary complex of CAP, cAMP and DNA in solvating water molecules and investigated the specific interactions between them at atomic and electronic levels using ab initio molecular simulations based on classical molecular dynamics and ab initio fragment molecular orbital methods. The results highlight the important amino acid residues of CAP for the interactions between CAP and cAMP and between CAP and DNA.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Molecular Simulation Results on Charged Carbon Nanotube Forest-Based Supercapacitors

In this paper, electrochemical double-layer capacitances of charged carbon nanotube (CNT) forests with tetraethyl ammonium tetrafluoro borate electrolyte in propylene carbonate are studied on the basis of molecular dynamics simulation. Direct molecular simulation of the filling of pore spaces of the forest is feasible even with realistic, small CNT spacings. The numerical solution of the Poisson equation based on the extracted average charge densities then yields a regular experimental dependence on the width of the pore spaces, in contrast to the anomalous pattern observed in experiments on other carbon materials and also in simulations on planar slot-like pores. The capacitances obtained have realistic magnitudes but are insensitive to electric potential differences between the electrodes in this model. This agrees with previous calculations on CNT forest supercapacitors, but not with experiments which have suggested electrochemical doping for these systems. Finally, those phenomena remain for further theory/modeling work.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗