Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “surface complexation modeling”

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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Probing the initial stages of iron surface corrosion: Effect of O 2 and H 2 O on surface carbonation

Iron plays a vital role in natural processes such as water, mineral, iron, and nutrient cycles. Iron undergoes reduction-oxidation and catalytic reactions to produce various corrosion films depending on its chemical environment. Near ambient pressure X-ray photoelectron spectroscopy, polarized modulated infrared reflection absorption spectroscopy, and Auger electron spectroscopy were used to study the key reactants, from O 2 (g), H 2 O vapor, Na + and Cl – on the initial stages of iron surface corrosion. Here, with increasing the ratio of O 2 and H 2 O, surface hydrocarbons were shown to oxidize into carbonates, while the Cl – was found to migrate into the interface. The effect of each individual reactant was measured separately and water was shown to have a first order rate dependence on the carbonate growth at low pressures, with little dependence for O 2 . Near ambient pressures, both H 2 O and O 2 were found to increase the carbonate growth, which was estimated using the Langmuir isotherm model, yielding Gibbs energies between –9.8 to –8.5 kJ/mol. A mechanism is suggested to explain the oxidation is catalyzed by NaCl on iron surfaces and the adventitious hydrocarbons served as the source for surface carbonation. These findings have implications for understanding other surface catalytic and redox interface chemistry in complex environments.

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

A Polycrystalline Pd Surface Studied by Two-Dimensional Surface Optical Reflectance during CO Oxidation: Bridging the Materials Gap

Industrial catalysts are complex materials systems operating in harsh environments. The active parts of the catalysts are nanoparticles that expose different facets with different surface orientations at which the catalytic reactions occur. However, these facets are close to impossible to study in detail under industrially relevant operating conditions. Instead, simpler model systems, such as single crystals with a well-defined surface orientation, have been successfully used to study gas–surface interactions such as adsorption and desorption, surface oxidation, and oxidation/reduction reactions. To more closely mimic the many facets exhibited by nanoparticles and thereby close the so-called materials gap, there has also been a recent move toward using polycrystalline surfaces and curved crystals. However, these studies are limited either by the pressure or spatial resolution at realistic pressures or by the number of surfaces studied simultaneously. In this work, we demonstrate the use of reflectance microscopy to study a vast number of catalytically active surfaces simultaneously under realistic and identical reaction conditions. As a proof of concept, we have conducted an operando experiment to study CO oxidation over a Pd polycrystal, where the polycrystalline surface acts as a collection of many single-crystal surfaces. Finally, we visualized the resulting data by plotting the reflectivity as a function of surface orientation. We think the techniques and visualization methods introduced in this work will be key toward bridging the materials gap in catalysis.

36 MATERIALS SCIENCE↗
DOE OSTI2023

CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling

Large-scale simulations with complex electron interactions remain one of the greatest challenges for atomistic modelling. Although classical force fields often fail to describe the coupling between electronic states and ionic rearrangements, the more accurate ab initio molecular dynamics suffers from computational complexity that prevents long-time and large-scale simulations, which are essential to study technologically relevant phenomena. Here we present the Crystal Hamiltonian Graph Neural Network (CHGNet), a graph neural network-based machine-learning interatomic potential (MLIP) that models the universal potential energy surface. CHGNet is pretrained on the energies, forces, stresses and magnetic moments from the Materials Project Trajectory Dataset, which consists of over 10 years of density functional theory calculations of more than 1.5 million inorganic structures. The explicit inclusion of magnetic moments enables CHGNet to learn and accurately represent the orbital occupancy of electrons, enhancing its capability to describe both atomic and electronic degrees of freedom. We demonstrate several applications of CHGNet in solid-state materials, including charge-informed molecular dynamics in Li x MnO 2 , the finite temperature phase diagram for Li x FePO 4 and Li diffusion in garnet conductors. We highlight the significance of charge information for capturing appropriate chemistry and provide insights into ionic systems with additional electronic degrees of freedom that cannot be observed by previous MLIPs.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Subsurface hydrogen, curvature, and strain: lessons from electro-reduction of benzaldehyde on nano-structured Pd catalysts

The unique ability of palladium (Pd) to absorb hydrogen and form a bulk hydride is vital for chemical transformations that involve hydrogenation reactions. Nano-structured Pd catalysts offer a promise of tuning these reaction rates by exploiting variations of reactant binding energies depending on the surface structure and morphological constraints that result in inhomogeneous strain. However, the interplay between the nano-structure of Pd and the ability of Pd to adsorb (and absorb) hydrogen as well as other reactive species needs to be better understood for a rational understanding of competitive chemical transformations at Pd surfaces. We consider the effects of the surface corrugation, strain, and subsurface Pd hydride on the reduction of benzaldehyde to benzyl alcohol in two qualitatively different samples – Pd nanoparticles and Pd gels formed by quasi-one-dimensional chains of these nanoparticles. Our electrochemical measurements and computational modelling suggest that surface concave sites, inherent to Pd gels, facilitate hydrogen transfer to the Pd subsurface region, thus weakening benzaldehyde binding to the surface. This effect is further modulated by the strain, depending on the local coordination environment on the corrugated surface. Furthermore, these findings demonstrate how structurally complex samples in the form of gels provide degrees of freedom for controlling the behavior of metal catalysts that are not available in isolated nanoparticles, which paves the way for new approaches in the design of catalytic materials and synthesis of metal hydrides.

Padavala, Sri Krishna Murthy [University of Minnes↗
DOE OSTI2026

Predicting Atomistic Transitions with Transformers

Accurate knowledge of the atomistic transition pathways in materials and material surfaces is crucial for many material science problems. However, conventional simulation techniques used to find these transitions are extremely computationally intensive. Even with large-scale, accelerated material simulations, the computational cost constrains the applicable domain in practice. Machine learning models, with the potential to learn the complex emergent behaviors governing atomistic transitions as a fast surrogate model, have great promise to predict transitions with a vastly reduced computational cost. Here, we demonstrate how transformers can be trained to predict atomistic transitions in nano-clusters. We show how we evaluate physical validity of the predictions and how a multitude of additional, different microstates can be generated by slightly varying the data provided to the model.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Trivalent ion overcharging on electrified graphene

We report the structure of the electrical double layer (EDL) formed near graphene in aqueous environments strongly impacts its performance for a plethora of applications, including capacitive deionization. In particular, adsorption and organization of multivalent counterions near the graphene interface can promote nonclassical behaviors of EDL including overcharging followed by co-ion adsorption. In this paper, we characterize the EDL formed near an electrified graphene interface in dilute aqueous YCl 3 solution using in situ high resolution x-ray reflectivity (also known as crystal truncation rod) and resonant anomalous x-ray reflectivity (RAXR). These interface-specific techniques reveal the electron density profiles with molecular-scale resolution. We find that yttrium ions (Y 3+ ) readily adsorb to the negatively charged graphene surface to form an extended ion profile. This ion distribution resembles a classical diffuse layer but with a significantly high ion coverage, i.e., 1 Y 3+ per 11.4 ± 1.6 Å 2 , compared to the value calculated from the capacitance measured by cyclic voltammetry (1 Y 3+ per ~240 Å 2 ). Such overcharging can be explained by co-adsorption of chloride that effectively screens the excess positive charge. The adsorbed Y 3+ profile also shows a molecular-scale gap (≥ 5 Å) from the top graphene surfaces, which is attributed to the presence of intervening water molecules between the adsorbents and adsorbates as well as the lack of inner-sphere surface complexation on chemically inert graphene. We also demonstrate controlled adsorption by varying the applied potential and reveal consistent Y 3+ ion position with respect to the surface and increasing cation coverage with increasing the magnitude of the negative potential. This is the first experimental description of a model graphene-aqueous system with controlled potential and provides important insights into the application of graphene-based systems for enhanced and selective ion separations.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Experiments at TA-55 have used optical pyrometry to measure temperatures of shocked plutonium on the 40mm gun

Researchers at LANL, along with collaborators from MSTS, have worked for more than a decade to develop and implement an optical pyrometry method to measure the temperature of metals shocked to the high pressures and temperatures. Because traditional shock wave measurements only provide information on the mechanical state of the material, temperature measurements are required to validate and improve equation-of-state (EOS) models for materials. The conditions sampled are complex; commenserate with those found in planetary impacts, and accressed by conventional and nuclear weapons. Optical pyrometry is currently the best available method for inferring the temperature of shocked metals, by careful measurement of the calibrated radiance emitted by a surface at finite temperature. These measurements are difficult often complicated by the short time scales associated with shock wave experiments (<1 µs) coupled with the many sources of non-thermal light (impact flash, fracture light, etc.) that pollutes the radiant light measured from the sample.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

A molecular conveyor belt-associated protein controls the rotational direction of the bacterial type 9 secretion system

ABSTRACT Many bacteria utilize the type 9 secretion system (T9SS) for gliding motility, surface colonization, and pathogenesis. This dual-function motor supports both gliding motility and protein secretion, where rotation of the T9SS plays a central role. Fueled by the energy of the stored proton motive force and transmitted through the torque of membrane-anchored stator units, the rotary T9SS propels an adhesin-coated conveyor belt along the bacterial outer membrane like a molecular snowmobile, thereby enabling gliding motion. However, the mechanisms controlling the rotational direction and gliding motility of T9SS remain elusive. Shedding light on this mechanism, we find that in the gliding bacteriumFlavobacterium johnsoniae, deletion of the C-terminus of the conveyor belt-associated protein GldJ controls and, in fact, reverses the rotational direction of T9SS from counterclockwise (CCW) to clockwise (CW). This suggests that the interface between the conveyor belt-associated protein GldJ and the T9SS ring protein GldK plays an important role in controlling the directionality of T9SS, potentially by modulating its interaction with the stator complex GldLM, which drives motor rotation. Combined with MD simulation of the T9SS stator units GldLM, we suggest a “tri-component gearset” model where GldJ controls the rotational direction of its driver, the T9SS, thus providing adaptive sensory feedback to influence the motility of the gliding bacterium. IMPORTANCE The type 9 secretion system (T9SS) is fundamental to bacterial gliding motility, pathogenesis, and surface colonization. Our findings reveal that the C-terminal region of the conveyor belt-associated protein GldJ functions as a molecular switch which is capable of reversing the rotational direction of T9SS. Through the coordinated actions of the T9SS stator units (akin to a driving motor), the GldK ring (the gear that converts rotational energy into linear movement), and GldJ, this machinery forms a smart conveyor belt system reminiscent of flexible or cognitive mechanical conveyors. Such advanced conveyors can alter their direction to adapt to shifting demands. Here, we show that the bacterial T9SS similarly adjusts its rotational bias based on feedback from the conveyor belt-associated protein GldJ. This dual-role feedback mechanism underscores an evolved, controllable biological snowmobile, offering new avenues for studying how bacteria fine-tune motility in dynamic environments.

Microbiology↗
DOE OSTI2026

Non-ideal stoichiometry and thermochemistry of aqueous iridium oxide nanoparticles in proton-coupled electron transfer and oxygen-atom transfer

Reported here are reactions of aqueous colloidal IrO x nanoparticles (NPs) with proton-coupled electron transfer (PCET) and oxygen-atom transfer (OAT) organic reagents, determining the reaction stoichiometries and thermochemistry. IrO x NPs have attracted much attention for their high electrocatalytic activity, but understanding of their fundamental reaction chemistry is limited. This IrO x NP model system is simple, with UV-vis titrations demonstrating reversible interconversion between predominantly Ir IV and predominantly Ir III NPs. This simplicity allows studies that reveal their complex non-idealities. The NP redox chemistry has a “super-Nernstian” stoichiometry of ∼1.3H + per 1e − transferred during both PCET and OAT reactions, as measured with electrochemistry and chemical methods. Spectroelectrochemistry revealed a broad distribution of surface IrO x –H bond dissociation free energies (BDFEs), becoming weaker as more H is added. Such variation in binding strengths—a non-ideal binding isotherm—is common for surface adsorbates. For IrO x , the variation of BDFE(IrO–H)s is fit well to a Frumkin isotherm with a width of 6.5 kcal mol −1 . For OAT from the reactive oxygen atoms of IrO x NPs, bracketing experiments gave 93 ± 24 kcal mol −1 for the average BDFE(O x Ir–O), with a predicted spread much larger than that for the BDFE(IrO–H). Taken together, the results show the importance of non-ideal stoichiometry and thermochemistry for IrO x NPs, and they open a path to more complete models to understand catalytic redox reactions at such surfaces.

Iridium Oxide Nanoparticles (NPs)↗
DOE OSTI2021

Nanoscale Turing patterns in a bismuth monolayer

Turing’s reaction–diffusion theory of morphogenesis has been very successful for understanding macroscopic patterns within complex objects ranging from biological systems to sand dunes. However, Turing patterns on microscopic length scales are extremely rare. Here we show that a strained atomic bismuth monolayer assembled on the surface of NbSe 2 —and subject to interatomic interactions and kinetics—displays Turing patterns. Our reaction–diffusion model produces stripe patterns with a period of five atoms (approximately 2 nm) and domain walls with Y-shaped junctions that bear a striking resemblance to what has been experimentally observed. Our work establishes that Turing patterns can occur at the atomic scale in a hard condensed-matter setting.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Combined density functional theory/kinetic Monte Carlo investigation of surface morphology during cycling of Li-Cu electrodes

In this work, we employed a combined density functional theory and kinetic Monte Carlo approach in order to investigate the nanoscale behavior of lithium stripping and plating on lithium-copper nanoalloys. Density functional theory calculations were used to develop an atom-by-atom understanding of the nucleation behavior of lithium on copper surfaces with different surface morphologies. We found that nanometric pits resulted in significantly more favorable lithium deposition when compared to pristine or lightly-defective Cu surfaces, which is in agreement with previous experimental studies. We then used density functional theory and constrained ab initio molecular dynamics to develop interaction and reaction parameters for a larger-scale kinetic Monte Carlo model in order to provide insight into the stripping and plating behavior of nanostructured Li-Cu nanoalloy anodes. Substantial losses in capacity and strong indications of morphological change were observed for a wide range of Cu:Li ratios over 100 cycles. We observed a complex relationship between the nanoparticle composition and the properties of the formed solid-electrolyte interphase.

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

New Particle Formation and Growth Dynamics for α-Pinene Ozonolysis in a Smog Chamber and Implications for Ambient Environments

Secondary organic aerosol (SOA) plays an important role in the Earth’s energy balance and air quality. Understanding the formation of SOA accompanying the oxidation of volatile organic compounds (VOCs) remains incomplete because of the complex reactions and partitioning mechanisms. In this study, we investigated new particle formation (NPF) of SOA derived from α-pinene ozonolysis in the Harvard Environmental Chamber using the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) with the condensing species as two products, one of lower volatility (LVOC, 0.01 μg m –3 ) and one of higher volatility (SVOC, 1.0 μg m –3 ). A module for the classical nucleation theory was included. The surface tension (σ), bulk diffusivity (D b ), and mass accommodation coefficient (α) were varied until convergence between the simulated and observed particle number size distributions. α of LVOC was constrained as 0.03–0.1 with the reported σ of α-pinene-ozonolysis-derived SOA and Tolman surface correction (~23.0–27.5 mN m –1 ) and Db from 10 –15.5 to >10 –11 cm 2 s –1 . The observed particle number size distribution of α-pinene ozonolysis was well-simulated to derive the physical parameters, including σ, Db, and α, suggesting the possible application to group the reported complex oxidation products and further application for other atmospheric-related oxidations for quantifying NPF.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Temperature Field Reconstruction of Surfaces Heated Through Radiative Heat Transfer Using Convolutional Neural Networks

Microreactors could play a crucial role in decarbonizing our energy portfolio. However, their development and implementation come with specific challenges, particularly regarding cost. Due to their compact size and the harsh operational environment, collecting real-time data on reactor operation can be challenging. Many probe designs are unable to withstand extreme conditions (e.g., temperature, radiation) in the reactor. In this context, using convolutional neural networks (CNNs) can pave the way for developing a nonintrusive approach that relies solely on ex-core sensors. A well-trained physics-informed CNN can reconstruct the distribution of a given physical quantity over a domain using only a few sensors, allowing us to reconstruct the desired field distribution even in a limited space or complex geometries where a large array of sensors is impractical. In this work, we present the initial steps toward developing a real-time tool for monitoring the thermal behavior of nuclear reactor pressure vessels. Based on an experimental setup, a computational model using the Multiphysics Object-Oriented Simulation Environment (moose) framework was built, where the Ray Tracing and Heat Conduction modules were used to evaluate the temperature distribution over a convex metal surface heated through radiative heat transfer. This metal surface represents a section of a heated nuclear reactor vessel wall. The model also accounts for solid mechanics physics through the moose Solid Mechanics module. In situ experimental data, acquired from a Texas A&M facility, were used to validate the computational model. Part of the data generated by the moose model was used to train the convolutional neural network to reconstruct the vessel wall's outer surface temperature. The CNN generalization was then compared against the experimental and computational data.

Aldeia Machado, Luiz Carlos↗
DOE OSTI2020

Screening of Therapeutic Agents for COVID-19 Using Machine Learning and Ensemble Docking Studies

The current pandemic demands a search for therapeutic agents against the novel coronavirus SARS-CoV-2. In this work, we present an efficient computational strategy that combines machine learning (ML)-based models and high-fidelity ensemble docking studies to enable rapid screening of possible therapeutic ligands. Targeting the binding affinity of molecules for either the isolated SARS-CoV-2 S-protein at its host receptor region or the S-protein:human ACE2 interface complex, we screen ligands from drug and biomolecule data sets that can potentially limit and/or disrupt the host-virus interactions. Top scoring one hundred eighty-seven ligands (with 75 approved by the Food and Drug Administration) are further validated by all atom docking studies. Important molecular descriptors ( 2 χ n , topological surface area, and ring count) and promising chemical fragments (oxolane, hydroxy, and imidazole) are identified to guide future experiments. Overall, this work expands our knowledge of small-molecule treatment against COVID-19 and provides a general screening pathway (combining quick ML models with expensive high-fidelity simulations) for targeting several chemical/biochemical problems.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Impact of Forest Canopy Structure on Buoyant Plume Dynamics During Wildland Fires

Heterogeneous forest canopies can generate complex turbulent structures, but in the presence of a fire plume, these interactions are not fully understood. This study investigates the influence of forest canopy heterogeneity on buoyant plume dynamics resulting from surface thermal anomalies representing wildland fires, utilizing Large Eddy Simulation (LES). The Parallelized Large-Eddy Simulation Model (PALM) was employed to simulate six canopy configurations: no canopy, homogeneous canopy, external plume-edge canopy, internal plume-edge canopy, 100 m gap canopy, and 200 m gap canopy. Each configuration was analyzed with and without a static surface heat flux patch of 5000 W ∙ m -2 , resulting in a resting buoyant plume. Simulations were conducted under three crosswind speeds: 0, 5, and 10 m ∙ s -1 . Results show that canopy structure significantly modifies plume behavior, mean flow, and turbulent kinetic energy (TKE) budgets. Plume updraft speed and tilt varied with canopy configuration and crosswind speed. Horizontal pressure gradients associated with plume-atmosphere interaction were modified based on the canopy configuration, resulting in varying crosswind speed reductions at the plume region. Strong momentum absorption was observed above the canopy for the crosswind cases, with the greatest enhancement in the gap canopies. Momentum injection from below the canopy due to the heat source was also observed, resulting in plume structure modulation based on canopy configuration. TKE was found to be the largest in the gap canopy configurations. TKE budget analysis revealed that buoyant production dominated over shear production. At the center of the heat patch, the gap canopy configurations showed enhanced buoyancy within the gap. These results improve our knowledge of fire-canopy-atmosphere interactions that can inform fire models on the impacts of canopy heterogeneity on plume dynamics and ember ejections.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Electro-reduction of organics on metal cathodes: A multiscale-modeling study of benzaldehyde on Au (111)

Here, we present a multiscale modeling study on the electro-reduction of benzaldehyde (BZY) on Au(111) under realistic electrochemical conditions. To model the electrochemical cell, we adopt a capacitor model in which complex solvents are confined between a Au cathode and a carbon anode. Classical molecular dynamics simulations reveal that electrode charge density and the presence of alcohol show strong effects on the density, adsorption geometry and dynamics of benzaldehyde on the Au electrode. Under charging conditions, the surface concentration of benzaldehyde on the Au electrode decreases, while the content of other species increases. Finally, we proposed a scheme that correlates the electric current running through the Au/solvent interface with the applied bias. This study provides a molecular level understanding of how solvent composition, in this case water/alcohol content, controls the activity of electrocatalytic hydrogenation

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Coprecipitation of Fe/Cr Hydroxides at Organic–Water Interfaces: Functional Group Richness and (De)protonation Control Amounts and Compositions of Coprecipitates

Iron/chromium hydroxide coprecipitation controls the fate and transport of toxic chromium (Cr) in many natural and engineered systems. Organic coatings on soil and engineered surfaces are ubiquitous; however, mechanistic controls of these organic coatings over Fe/Cr hydroxide coprecipitation are poorly understood. Here, Fe/Cr hydroxide coprecipitation was conducted on model organic coatings of humic acid (HA), sodium alginate (SA), and bovine serum albumin (BSA). The organics bonded with SiO 2 through ligand exchange with carboxyl (-COOH), and the adsorbed amounts and pK(a) values of -COOH controlled surface charges of coatings. The adsorbed organic films also had different complexation capacities with Fe/Cr ions and Fe/Cr hydroxide particles, resulting in significant differences in both the amount (on HA > SA(-COOH) >> BSA(-NH 2 )) and composition (Cr/Fe molar ratio: on BSA(-NH2) >> HA > SA(-COOH)) of heterogeneous precipitates. Negatively charged -COOH attracted more Fe ions and oligomers of hydrolyzed Fe/Cr species and subsequently promoted heterogeneous precipitation of Fe/Cr hydroxide nanoparticles. Organic coatings containing -NH 2 were positively charged at acidic pH because of the high pK(a) value of the functional group, limiting cation adsorption and formation of coprecipitates. Meanwhile, the higher local pH near the -NH 2 coatings promoted the formation of Cr(OH) 3 . Finally, this study advances fundamental understanding of heterogeneous Fe/Cr hydroxide coprecipitation on organics, which is essential for successful Cr remediation and removal in both natural and engineered settings, as well as the synthesis of Cr-doped iron (oxy)hydroxides for material applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Roadmap on methods and software for electronic structure based simulations in chemistry and materials

This Roadmap article provides a succinct, comprehensive overview of the state of electronic structure methods and software for molecular and materials simulations. Seventeen distinct sections collect insights by 51 leading scientists in the field. Each contribution addresses the status of a particular area, as well as current challenges and anticipated future advances, with a particular eye towards software related aspects and providing key references for further reading. Foundational sections cover density functional theory and its implementation in real-world simulation frameworks, Green's function based many-body perturbation theory, wave-function based and stochastic electronic structure approaches, relativistic effects and semiempirical electronic structure theory approaches. Subsequent sections cover nuclear quantum effects, real-time propagation of the electronic structure, challenges for computational spectroscopy simulations, and exploration of complex potential energy surfaces. The final sections summarize practical aspects, including computational workflows for complex simulation tasks, the impact of current and future high-performance computing architectures, software engineering practices, education and training to maintain and broaden the community, as well as the status of and needs for electronic structure based modeling from the vantage point of industry environments. Overall, the field of electronic structure software and method development continues to unlock immense opportunities for future scientific discovery, based on the growing ability of computations to reveal complex phenomena, processes and properties that are determined by the make-up of matter at the atomic scale, with high precision.

36 MATERIALS SCIENCE↗