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Results for “computational molecular design”

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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Logic-based analysis of gene expression data predicts association between TNF, TGFB1 and EGF pathways in basal-like breast cancer

For breast cancer, clinically important subtypes are well characterized at the molecular level in terms of gene expression profiles. In addition, signaling pathways in breast cancer have been extensively studied as therapeutic targets due to their roles in tumor growth and metastasis. However, it is challenging to put signaling pathways and gene expression profiles together to characterize biological mechanisms of breast cancer subtypes since many signaling events result from post-translational modifications, rather than gene expression differences. We designed a logic-based computational framework to explain the differences in gene expression profiles among breast cancer subtypes using Pathway Logic and transcriptional network information. Pathway Logic is a rewriting-logic-based formal system for modeling biological pathways including post-translational modifications. Our method demonstrated its utility by constructing subtype-specific path from key receptors (TNFR, TGFBR1 and EGFR) to key transcription factor (TF) regulators (RELA, ATF2, SMAD3 and ELK1) and identifying potential association between pathways via TFs in basal-specific paths, which could provide a novel insight on aggressive breast cancer subtypes. Lastly, codes and results are available at http://epigenomics.snu.ac.kr/PL/.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Using Metadynamics to Reveal Extractant Conformational Free Energy Landscapes

Understanding the impact of extractant functionalization on metal-binding energetics in liquid-liquid extraction is essential to guide the development of better separation processes. Traditionally, computational extractant design uses electronic structure calculations on metal-ligand clusters to determine the metal-binding energy of the lowest energy state. Although highly accurate, this approach does not account for all of the relevant physics encountered under experimental conditions. Such methodologies often neglect entropic contributions such as temperature effects and ligand flexibility, in addition to approximating solvent-extractant interactions with implicit solvent models. In this study, we use classical molecular dynamics simulations with an advanced sampling method, metadynamics, to map out extractant molecule conformational free energies in the condensed phase. Here we generate the complete conformational landscape in solution for a family of bidentate malonamide-based extractants with different functionalizations of the headgroup and the side chains. In particular, we show how such alkyl functionalization reshapes the free energy landscape, affecting the free energy penalty of organizing the extractant into the cis-like metal-binding conformation from the trans-like conformation of the free extractant in solution. Specifically, functionalizing alkyl tails to the center of the headgroup has a greater influence on increasing molecular rigidity and disfavoring the binding conformation than functionalizing side chains. These findings are consistent with trends in metal-binding energetics based on experimentally reported distribution ratios. We also consider a different bidentate extractant molecule, carbamoylmethylphosphine oxide, and show how the choice of solvent can further reshape the conformational energetic landscape. This study demonstrates the feasibility of using molecular dynamics simulations with advanced sampling techniques to investigate extractant conformational energetics in solution, which, more broadly, will enable extractant design that accounts for entropic effects and explicit solvation.

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

Poly(cyclosilane) Connectivity Tunes Optical Absorbance

We report herein the influence of skeletal connectivity on the conformation-dependent optical properties of cyclosilane homo- and copolymers. 1,3-Linked cyclosilanes were bathochromically shifted by 20 nm in solution relative to 1,4-linked cyclosilanes, an effect reproduced by quantum chemical calculations on oligomeric model systems. Polysilane optical properties are conformation-dependent, and 1,3-linked cyclosilanes were hypothesized to adopt a favorable conformation unavailable to 1,4-linked cyclosilanes constrained to an endocyclic gauche conformation. Copolymerization of the isomeric cyclosilanes 1,3Si 6 and 1,4Si 6 afforded linear statistical copolymers, as characterized by 1 H and 29 Si NMR spectroscopies. The distinct connectivity of each comonomer was found to give rise to tunable absorption spectra, where the position of the absorption band systematically increased with the increased corporation of 1,3Si 6 . Computational studies pointed to conformation-dependent changes in orbital symmetry in shifting the most intense transition from the low-energy highest occupied molecular orbital (HOMO) → lowest unoccupied molecular orbital (LUMO) transition to a higher-energy HOMO → LUMO + n transition. Here, the results of these studies demonstrate for the first time the role of silicon skeletal connectivity in controlling conformation and optoelectronic properties and provide new insight into the structure-based design of solution-processable silicon-based polymeric materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Nanoscale Surface Structure of Nanometer-Thick Ferroelectric BaTiO 3 Films Revealed by Synchrotron X-ray Scanning Tunneling Microscopy: Implications for Catalytic Adsorption Reactions

Ferroelectric nanomaterials are of interest in catalysis, non-volatile memory, and neuromorphic computing among other applications because of their switchable structure that can alter electronic and interface properties of a single material. The investigation of the role of polarization on the surface structure and chemistry of ferroelectric nanomaterials is a longstanding challenge, as it ideally requires a combination of both nanoscale imaging and chemical spectroscopy. In this work we study a model ferroelectric BaTiO 3 thin film by synchrotron X-ray scanning tunneling microscopy (SX-STM), a unique method which integrates nanoscale surface imaging and chemically sensitive spectroscopy. We find that polarization switching from downward to upward in (001)single crystalline BaTiO 3 thin films increases the intensity of X-ray absorption across Ba M, Ti L and O K edges. Chemical mapping of nanometer sized domains further demonstrates the modulation of surface structures upon polarization switching, as well as confirming the trends observed in single point experiments across the surface. Here, we complement these measurements with ab-initio computational absorption spectroscopy to elucidate the effect of polarization switching on the core-hole excitations using the Bethe-Salpeter equation approach. Our experimental and theoretical results thus confirm a stronger binding strength for the upward polarized surface with molecular O 2 as a model reactant, offering mechanistic evidence that supports previous reports. This work advances the understanding of the surface chemistry and electronic structure of ferroelectrics, which can ultimately aid strategies to design interfaces with tailored properties.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2026

An end-to-end workflow for executing a classically bootstrapped variational quantum algorithm on an academic quantum computer

Academic quantum computing platforms often face unique challenges in executing quantum workloads due to fragmented software environments and limited engineering support. Unlike commercial ecosystems, academic devices typically evolve without full-stack integration in mind, making it difficult to run complex applications—such as variational quantum algorithms (VQA)—reliably and efficiently. Issues such as incompatible software layers and lack of automated job management significantly increase the overhead of theory-experiment collaboration. To address these challenges, we develop a modular, end-to-end workflow that decouples application-layer code from low-level hardware control, automates circuit submission and result collection, and supports fine-grained circuit-level job scheduling and recovery. The architecture employs a dual-end application programming interface (API) design, enabling robust operation across unstable or resource-constrained hardware backends. For practical use, the framework is lightweight and user-friendly, allowing rapid prototyping of full-stack workflows using basic Python tools. We validate this workflow on a high-fidelity trapped-ion quantum computer by demonstrating a variational quantum eigensolver (VQE) experiment with a classically bootstrapped ansatz initialization technique. The system successfully executed over 60,000 circuits across multiple molecular test cases with minimal human intervention, highlighting the framework’s effectiveness in enabling reproducible, resilient quantum experimentation in academic settings.

Clifford↗
DOE OSTI2023

Active Learning Framework

Machine learning (ML) of interatomic potentials show great promise to accelerate scientific simulation, e.g., by emulating expensive computations at a high accuracy but much reduced computational cost. Training datasets are calculated from computationally expensive ab initio quantum mechanics methods, density functional theory (DFT). Trained on this data, an ML model can be very successful in predicting energy and forces for new atomic configurations. A critical factor is the quality and diversity of the training dataset. Thus, a highly automated approach to dataset construction based on active learning framework is designed suitable for material physics. The active learning scheme begins with fully randomized atomic configurations. Then, many Molecular Dynamics (MD) trajectories are simulated using current ML potentials, where each MD trajectory is initialized to a random disordered configuration. The temperature is varied in order to diversify the sampled configuration during these simulations. The variance of predictions for eight neural networks within an ensemble is analyzed to determine whether the model is operating as expected. This helps in determining whether collecting more data would be helpful to the model by checking the ensemble variance is greater than the threshold. In this case, the MD trajectory is terminated and the final atomic configuration is placed on a queue (SQL database) for DFT calculations and added to training dataset. Periodically, ML model is retrained to the updated training model. This Active Learning loop is iterated until the cost of MD simulations becomes prohibitively expensive. The MD simulations will hopefully be sufficiently robust to support nucleation after many active learning iterations. In this sense, active learning scheme must automatically discover the important low energy and nonequilibrium physics.

Nebgen, Benjamin↗
DOE OSTI2023

Dislocation line tension model to improve understanding of the effects of hydrogen on the deformation of structural materials

Decarbonization efforts highlight hydrogen as an attractive alternative to fossil fuels, but its tendency to embrittle structural metals demands careful consideration when designing hydrogen infrastructure. Moreover, the mechanisms by which hydrogen degrades these materials are still being elucidated. The current work develops new computational tools to quantify the different contributions of hydrogen to the energy barrier of cross-slip, a key deformation mechanism. Novel features are implemented to a line tension model, which include the use of non-singular dislocation interactions, character-dependent dislocation energies and simulations of the constriction configurations. A new molecular dynamics technique is developed to calculate the interaction energy between the partials of a dissociated dislocation via fixing the centers of mass of the regions below and above the Shockley partials and performing time-averaged calculations. Hydrogen is found to impact the stacking fault width of dislocations in different ways depending on their characters: it decreases for dislocations with a character θ > 30°, remains unchanged for θ = 30° and increases for θ < 30°. The latter regime is a newly identified mechanism by which hydrogen inhibits cross-slip. Moreover, formation of nano-hydrides is predicted to occur around screw dislocations for high hydrogen concentrations, a phenomenon previously identified only in dislocations with an edge component. If nano-hydrides develop, their influence extending the equilibrium stacking fault width and increasing both the constriction and cross-slip energy barriers dominate over all other hydrogen contributions. The theory and tools developed will pave the way towards a comprehensive understanding of hydrogen-dislocation interactions in structural metals.

08 HYDROGEN↗
DOE OSTI2023

Combining Force Fields and Neural Networks for an Accurate Representation of Chemically Diverse Molecular Interactions

A key goal of molecular modeling is the accurate reproduction of the true quantum mechanical potential energy of arbitrary molecular ensembles with a tractable classical approximation. The challenges are that analytical expressions found in general purpose force fields struggle to faithfully represent the intermolecular quantum potential energy surface at close distances and in strong interaction regimes; that the more accurate neural network approximations do not capture crucial physics concepts, e.g., nonadditive inductive contributions and application of electric fields; and that the ultra-accurate narrowly targeted models have difficulty generalizing to the entire chemical space. We therefore designed a hybrid wide-coverage intermolecular interaction model consisting of an analytically polarizable force field combined with a short-range neural network correction for the total intermolecular interaction energy. Here, we describe the methodology and apply the model to accurately determine the properties of water, the free energy of solvation of neutral and charged molecules, and the binding free energy of ligands to proteins. The correction is subtyped for distinct chemical species to match the underlying force field, to segment and reduce the amount of quantum training data, and to increase accuracy and computational speed. For the systems considered, the hybrid ab initio parametrized Hamiltonian reproduces the two-body dimer quantum mechanics (QM) energies to within 0.03 kcal/mol and the nonadditive many-molecule contributions to within 2%. Simulations of molecular systems using this interaction model run at speeds of several nanoseconds per day.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

MARTINI-Compatible Coarse-Grained Model for the Mesoscale Simulation of Peptoids

Peptoids (poly-N-substituted glycines) are a class of synthetic polymers that are regioisomers of peptides (poly-C-substituted glycines), in which the point of side-chain connectivity is shifted from the backbone C to the N atom. Peptoids have found diverse applications as peptidomimetic drugs, protein mimetic polymers, surfactants, and catalysts. Computational modeling is valuable in the understanding and design of peptoid-based nanomaterials. In this work, we report the bottom-up parameterization of coarse-grained peptoid force fields based on the MARTINI peptide force field against all-atom peptoid simulation data. Our parameterization pipeline iteratively refits coarse-grained bonded interactions using iterative Boltzmann inversion and nonbonded interactions by matching the potential of mean force for chain extension. We assure good sampling of the amide bond cis/trans isomerizations in the all-atom simulation data using parallel bias metadynamics. We develop coarse-grained models for two representative peptoids—polysarcosine (poly(N-methyl glycine)) and poly(N-((4-bromophenyl)ethyl)glycine)—and show their structural and thermodynamic properties to be in excellent accord with all-atom calculations but up to 25-fold more efficient and compatible with MARTINI force fields. Here, this work establishes a new rigorously parameterized coarse-grained peptoid force field for the understanding and design of peptoid nanomaterials at length and time scales inaccessible to all-atom calculations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Engineering Catalysts for Selective Ester Hydrogenation

The development of efficient catalysts and processes for synthesizing functionalized (olefinic and/or chiral) primary alcohols and fluoral hemiacetals is currently needed. These are valuable building blocks for pharmaceuticals, agrochemicals, perfumes, and so forth. From an economic standpoint, bench-stable Takasago Int. Corp.’s Ru-PNP, more commonly known as Ru-MACHO, and Gusev’s Ru-SNS complexes are arguably the most appealing molecular catalysts to access primary alcohols from esters and H 2 . This work introduces economically competitive Ru-SNP(O) z complexes ( z = 0, 1), which combine key structural elements of both of these catalysts. In particular, the incorporation of SNP heteroatoms into the ligand skeleton was found to be crucial for the design of a more product-selective catalyst in the synthesis of fluoral hemiacetals under kinetically controlled conditions. Based on experimental observations and computational analysis, this paper further extends the current state-of-the-art understanding of the accelerative role of KO- t -C 4 H 9 in ester hydrogenation. Finally, it attempts to explain why a maximum turnover is seen to occur starting at ~25 mol % base, in contrast to only ~10 mol % with ketones as substrates.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Roadmap on multiscale materials modeling

Modeling and simulation is transforming modern materials science, becoming an important tool for the discovery of new materials and material phenomena, for gaining insight into the processes that govern materials behavior, and, increasingly, for quantitative predictions that can be used as part of a design tool in full partnership with experimental synthesis and characterization. Modeling and simulation is the essential bridge from good science to good engineering, spanning from fundamental understanding of materials behavior to deliberate design of new materials technologies leveraging new properties and processes. This Roadmap presents a broad overview of the extensive impact computational modeling has had in materials science in the past few decades, and offers focused perspectives on where the path forward lies as this rapidly expanding field evolves to meet the challenges of the next few decades. The Roadmap offers perspectives on advances within disciplines as diverse as phase field methods to model mesoscale behavior and molecular dynamics methods to deduce the fundamental atomic-scale dynamical processes governing materials response, to the challenges involved in the interdisciplinary research that tackles complex materials problems where the governing phenomena span different scales of materials behavior requiring multiscale approaches. The shift from understanding fundamental materials behavior to development of quantitative approaches to explain and predict experimental observations requires advances in the methods and practice in simulations for reproducibility and reliability, and interacting with a computational ecosystem that integrates new theory development, innovative applications, and an increasingly integrated software and computational infrastructure that takes advantage of the increasingly powerful computational methods and computing hardware.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Complex Structure of Molten FLiBe (2 Li F – Be F 2 ) Examined by Experimental Neutron Scattering, X-Ray Scattering, and Deep-Neural-Network Based Molecular Dynamics

The use of molten salts as coolants, fuels, and tritium breeding blankets in the next generation of fission and fusion nuclear reactors benefits from furthering the characterization of the molecular structure of molten halide salts, paving the way to predictive capability of the chemical and thermophysical properties of molten salts. Due to its neutronic, chemical, and thermochemical properties, 2 Li F - Be F 2 is a candidate molten salt for several fusion- and fission-reactor designs. We performed neutron and x-ray total-scattering measurements to determine the atomic structure of liquid 2 Li F - Be F 2 . We also performed and neural-network molecular-dynamics simulations to predict the structure obtained by neutron- and x-ray-diffraction experiments. The use of machine learning provides improvements to the efficiency in predicting the structure at a longer length scales than is achievable with simulations at significantly lower computational expense while retaining near accuracy. We found that the NNMD simulations accurately predicted the Be F 4 2 − oligomer formations seen in the experimental first-structure-factor peak. Our combination of high-resolution measurements with large-scale molecular dynamics provided an avenue to explore and experimentally verify the intermediate-range ordering beyond the first-nearest neighbor that has posed too many experimental and computational challenges in previous works. With a deeper understanding of the salt structure and ion ordering, the evolution of salt chemistry over the lifetime of a reactor can be better predicted, which is crucial to the licensing and operation of advanced fission and fusion reactors that employ molten salts. To this end, this work will serve as a reference for future studies of salt structure and macroscopic properties with and without the addition of solutes. Published by the American Physical Society 2024

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

TeraChem: A graphical processing unit-accelerated electronic structure package for large-scale ab initio molecular dynamics

TeraChem was born in 2008 with the goal of providing fast on-the-fly electronic structure calculations to facilitate ab initio molecular dynamics studies of large biochemical systems such as photoswitchable proteins and multichromophoric antenna complexes. Originally developed for videogaming applications, graphics processing units (GPUs) offered a low-cost parallel computer architecture that became more accessible for general-purpose GPU computing with the release of CUDA in 2007. The evaluation of the electron repulsion integrals (ERIs) is a major bottleneck in electronic structure codes and provides an attractive target for acceleration on GPUs. Thus, highly efficient routines for evaluation of and contractions between the ERIs and density matrices were implemented in TeraChem. Here, electronic structure methods were developed and implemented to leverage these integral contraction routines, resulting in the first quantum chemistry package designed from the ground up for GPUs. This GPU acceleration makes TeraChem capable of performing large-scale ground and excited state calculations in the gas and condensed phase. Today, TeraChem's speed forms the basis for a suite of quantum chemistry applications, including optimization and dynamics of proteins, automated and interactive chemical discovery tools, and large-scale nonadiabatic dynamics simulations.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2026

Benchmarking the performance of uncertainty quantification methods for neural network-based interatomic potentials

Machine-learned interatomic potentials (ML-IAPs) continue to gain popularity as accurate, computationally efficient replacements for traditional, physics-based interatomic potentials and expensive ab initio methods. Uncertainty quantification (UQ) of ML-IAPs is a growing area of research as UQ is critical in many applications of IAPs, such as developing curated datasets, active learning-based data augmentation, self-improving models, and estimating the uncertainty of molecular dynamics simulations. In this paper, we construct and benchmark a series of different neural network potentials (NNPs) with varying network architectures to determine the performance of these models with respect to both the mean and uncertainty calibration error. Each NNP method is specifically designed to predict either epistemic or aleatoric uncertainty with particular focus on the differences in behavior between the epistemic and aleatoric uncertainty estimates. We benchmark these methods using multiple datasets common in the ML-IAP literature. The results show that the aleatoric uncertainty from single-shot model architectures is a competitive alternative to ensemble-based epistemic uncertainty predictions in regions of sufficient data-density. However, in regions where the representative data is sparse, aleatoric uncertainty models tend to overpredict and epistemic methods tend to underpredict the actual model error. We conclude that the type of UQ is crucial when discussing performance of probabilistic model results as different methods have different performance characteristics depending on the regime in which they are evaluated. Therefore, the type of UQ method should be carefully evaluated against both the data characteristics and requirements for the intended application.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Thermal transport in defective and disordered materials

With significant recent advancements in thermal sciences—such as the development of new theoretical and experimental techniques, and the discovery of new transport mechanisms—it is helpful to revisit the fundamentals of vibrational heat conduction to formulate an updated and informed physical understanding. The increasing maturity of simulation and modeling methods sparks the desire to leverage these techniques to rapidly improve and develop technology through digital engineering and multi-scale, electro-thermal models. With that vision in mind, this review attempts to build a holistic understanding of thermal transport by focusing on the often unaddressed relationships between subfields, which can be critical for multi-scale modeling approaches. For example, we outline the relationship between mode-specific (computational) and spectral (analytical) models. We relate thermal boundary resistance models based on perturbation approaches and classic transmissivity based models. We discuss the relationship between lattice dynamics and molecular dynamics approaches along with two-channel transport frameworks that have emerged recently and that connect crystal-like and amorphous-like heat conduction. Throughout, we discuss best practices for modeling experimental data and outline how these models can guide material-level and system-level design.

42 ENGINEERING↗
DOE OSTI2025

CO 2 in Ionene–Ionic Liquid Composite Membranes

Abstract Ionene – ionic liquid (IL) composites are promising materials for CO 2 separation, yet a molecular‐level understanding of their structure and its impact on CO 2 speciation, solubility, rotation, and diffusivity remains unclear. Herein, using multimodal nuclear magnetic resonance (NMR), time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS), atomic force microscopy (AFM), and molecular dynamics (MD) simulations, we reveal that the composites contain IL‐rich domains extending across hundreds of nanometres within the ionene matrix, and these bicontinuous domains span the entire membrane depth. CO 2 also absorbs into the ionene matrix, with the distribution between two CO 2 species varying with temperature and time. The rotational correlation times of these two species are on the timescale of 0.1 and 1 ns, respectively. As IL content increases, the ionic domains expand, resulting in higher CO 2 solubility due to enhanced molecular dynamics and increased free volume in both ionene backbones and IL‐rich regions. Although CO 2 diffusion in the membranes is an order of magnitude slower than in bulk IL, the activation energy for CO 2 diffusion remains comparable. Ionene‐IL composites represent a promising platform for designing CO 2 separation membranes, offering enhanced CO 2 diffusion and selectivity through IL‐rich domains, and increased CO 2 solubility and mechanical integrity from the ionene matrix.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Probing the Local Site Disorder and Distortion in Pyrochlore High-Entropy Oxides

High-entropy oxides (HEOs) have attracted great interest in diverse fields because of their inherent opportunities to tailor and combine materials functionalities. The control of local order/disorder in the class is by extension a grand challenge toward realizing their vast potential. In this paper, we report the first examples of pyrochlore HEOs with five M-site cations, for Nd 2 M 2 O 7 , in which the local structure has been investigated by neutron diffraction and pair distribution function (PDF) analysis. The average structure of the pyrochlores is found to be orthorhombic Imma, in agreement with radius-ratio rules governing the structural archetype. The computed PDFs from density functional theory relaxed special quasirandom structure models are compared with real space PDFs in this work to evaluate M-site order/disorder. Reverse Monte Carlo combined with ab initio molecular dynamics and Metropolis Monte Carlo simulations demonstrates that Nd 2 (Ta 0.2 Sc 0.2 Sn 0.2 Hf 0.2 Zr 0.2 ) 2 O 7 is synthesized with its M-site local to nanoscale order highly randomized/disordered, while Nd 2 (Ti 0.2 Nb 0.2 Sn 0.2 Hf 0.2 Zr 0.2 ) 2 O 7+x exhibits a strong distortion of the TiO 6 octahedron and small degree of Ti chemical short-range order (SRO) on the subnanometer scale. Calculations suggest that this may be intrinsic, energetically favored SRO rather than due to sample processing. These results offer an important demonstration that the engineered variation of participating ions in HEOs, even among those with very similar radii, provides richly diverse opportunities to control local order/disorder motifs—and therefore materials properties for future designs. This work also hints at the exquisite level of detail that may be needed in computational and experimental data analysis to guide structure–property tuning in the emerging HEO materials class.

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

Building Toward the Future in Chemical and Materials Simulation with Accessible and Intelligently Designed Web Applications

Over the last few decades, significant progress has been made in the development and use of electronic structure and other molecular simulation methods. As these methods become more mature and are able to simulate larger and more complex chemical simulations, the need for improvement in scientific visualization, molecular builders, simplified input to simulation methods, and the development of new approaches and languages to describe simulations, along with workflows to carry them out, becomes more apparent. In this chapter, we describe our recent efforts in developing a prototype open-source computational tool called Arrows that combines NWChem, SQL and NoSQL databases, email, web APIs, and web applications in a way that make molecular and materials modeling accessible to all scientists and engineers. At the same time, because of its simplified input, it provides a framework for expert users to carry out large numbers of calculations and run complex workflows.

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