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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Maintaining a Flat Li Surface during the Li Stripping Process via Interface Design

Electroplating has been the main focus in mitigating the dendrite growth on the Li-metal electrode; however, the stripping process is equally critical, since the nonsmooth Li surface during stripping will lead to nonuniform local current density, planting the seeds for dendrite growth. In this paper, density functional theory (DFT) and kinetic Monte Carlo (KMC) techniques were combined to investigate the vacancy evolution in Li interfaced with different solid–electrolyte interphase (SEI) materials. It was found that the lithiophilic interface, such as Li/Li 2 O, repels vacancies into the bulk Li, so Li atoms can quickly fill the Li vacancies near the Li/Li 2 O interface and maintain a smooth Li surface. In contrast, the lithiophobic interface, such as Li/LiF, traps Li vacancies toward the interface, and the accumulated Li vacancies form voids and roughen the surface. The predicted critical stripping current density, below which a smooth Li surface will be maintained, is therefore much faster at the lithiophilic interface than that at the lithiophobic interface. It was further revealed that the lithiophilicity at different SEI or coating materials can be ranked as Li/Li 2 O > Li/LiPON > Li/Li 2 CO 3 > Li/LiF based on the calculated interfacial adhesion and accumulation of electron density at the interface. Furthermore, this suggests that interface and coating design at nanoscale can be effective for maintaining a smooth Li surface during the stripping process, solving another challenge to achieving a dendrite-free Li-metal electrode in both liquid and solid electrolytes.

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

Discovery of Energy Storage Molecular Materials Using Quantum Chemistry-Guided Multiobjective Bayesian Optimization

Redox flow batteries (RFBs) are a promising technology for stationary energy storage applications due to their flexible design, scalability, and low cost. In RFBs, energy is carried in flowable redoxactive materials (redoxmers) which are stored externally and pumped to the cell during operation. Further improvements in the energy density of RFBs necessitates redoxmer designs with wider redox potential windows and higher solubility. Additionally, designing redoxmers with a fluorescence-enabled self-reporting functionality allows monitoring of the state of health of RFBs. To accelerate the discovery of redoxmers with desired properties, state-of-the- art machine learning ( ML) methods, such as multiobjective Bayesian optimization (MBO), are useful. Here, we first employed density functional theory calculations to generate a database of reduction potentials, solvation free energies, and absorption wavelengths for 1400 redoxmer molecules based on a 2,1,3-benzothiadiazole (BzNSN) core structure. From the computed properties, we identified 22 Pareto-optimal molecules that represent best trade-off among all of the desired properties. We further utilized these data to develop and benchmark an MBO approach to identify candidates quickly and efficiently with multiple targeted properties. With MBO, optimal candidates from the 1400-molecule data set can be identified at least 15 times more efficiently compared to the brute force or random selection approach. Importantly, we utilized this approach for discovering promising redoxmers from an unseen database of 1 million BzNSN-based molecules, where we discovered 16 new Pareto-optimal molecules with significant improvements in properties over the initial 1400 molecules. Furthermore, we anticipate that this active learning technique is general and can be utilized for the discovery of any class of functional materials that satisfies multiple desired property criteria.

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

How Water Attacks MXene

Two-dimensional (2D) transition metal carbides and nitrides (MXenes) have shown outstanding performances in electrochemical energy storage and many other applications. However, the stability of MXene remains a concern, especially its quick degradation in aqueous solutions under ambient conditions. Here, we report on the water/Ti 3 C 2 O 2 -MXene interfacial chemistry from first-principles molecular dynamics simulations at room temperature. Surprisingly, we find that the water molecules can attack the basal plane of Ti 3 C 2 O 2 and pull the surface Ti atoms out, thereby reconstructing the surface. Further, by tracking close encounters of water molecules and surface Ti atoms on the basal plane of Ti 3 C 2 O 2 , we show that the attack is initiated by the chemisorption of a water molecule on a surface Ti atom, followed by the breaking of Ti–C bonds and deprotonation of the water molecule, leading to the formation of Ti–OH on the Ti 3 C 2 O 2 surface and a hydronium ion in the aqueous phase. Our finding highlights the susceptibility of Ti 3 C 2 O 2 MXene to water attack, supporting recent experimental observations. Furthermore, we demonstrate that preventing close encounters of water molecules and the surface Ti atoms is key to the stability of the basal plane and can be realized by negatively charging the surface (thereby reorienting the O atoms of water away from the surface) or converting the surface O to -OH groups (thereby shifting the water layer further away from the surface). Our insights and approach highlight the importance of the reactivity of water when interfacing with 2D materials such as MXenes.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Reversible Electrochemical Lithium Cycling in a Vanadium(IV)- and Niobium(V)-Based Wadsley–Roth Phase

Fast charging remains one of the greatest safety challenges in Li-ion batteries due to Li-dendrite growth occurring on graphite anodes if they are lithiated too quickly. The search for high-rate anodes has highlighted materials in the Wadsley–Roth (WR) shear phase family. The relative abundance of V compared with traditional WR compositions of Nb and W makes V-based phases attractive. However, the high voltage and poor reversibility typically associated with V redox have made V-rich WR phases less studied than Nb- and W-rich phases. Here, we show that a new V-rich Wadsley–Roth phase, V 7 Nb 6 O 29 , achieves excellent rate capability and 80% capacity retention after 228 cycles with a relatively low average voltage of 1.76 V vs Li/Li + compared with other V-rich WR phases. Single-crystal X-ray diffraction reveals a P4/m space group with repeating 2 × 2 × ∞ and 3 × 3 × ∞ blocks of V 4+ and Nb 5+ octahedra. Finally, combined neutron pair distribution function analysis, X-ray absorption spectroscopy, and density functional theory calculations show that V redox is the primary source of capacity and that cycling stability is provided by the stable octahedral coordination adopted by V 4+ in the material.

25 ENERGY STORAGE↗
DOE OSTI2024

Study of Void Formation at the Lithium|Solid Electrolyte Interface

There is growing recognition of the critical role of void formation in lithium metal anodes in solid-state batteries and its impact on electrochemical performance. While experimental studies have demonstrated the challenges ensuing from void formation at the lithium metal interface with the solid electrolyte, there is a need to understand and quantify the role of intrinsic transport properties in lithium metal and the impact of external stimuli, such as temperature, pressure, and current density. Here, we develop this understanding by constructing a phase field-based model that captures the evolution of void domains at the lithium-solid electrolyte interface. Growth of the pores is driven by the fast removal of lithium from the interface during stripping at high current densities. Relative magnitudes of the bulk and surface lithium diffusivities, along with the applied current density, dictate the final pore morphology. Increasing the temperature results in faster diffusion, while external applied pressure causes creep flow of lithium, both of which help to mitigate the evolution of voids by quickly transporting metal from the bulk to the interface. Finally, a phase map as a function of temperature and pressure is developed as a guide to determine the regions that can lead to the stable cycling of lithium metal.

25 ENERGY STORAGE↗
DOE OSTI2026

First-Principles Evaluation of Proton Hopping in Tetrahedral Oxide Motifs

Proton-conducting oxides (PCOs) are important materials used as ionic conductors for energy conversion technologies. Existing research efforts on PCO optimization and discovery generally focus on complex perovskite-based oxides that require doping and alloying to engineer oxygen deficiency and high proton conductivity. However, the variety of chemical compositions and coordination environments in oxides poses challenges for efficient materials design. In this computational study, we construct a database of simplified motifs to elucidate the relationship between fundamental materials chemistry and proton kinetics. Specifically, we focus on the zincblende crystal structure as a proxy for tetrahedral metal–oxide (M–O) coordination environments. We systematically quantified the effects of cation type, oxidation states, and M–O bond lengths on the proton hopping barrier, and found that strong M–O bonds and metal cations with large and variable oxidation states (e.g., Mo 6+ , V 5+ ) lead to smaller proton hopping barriers. By mapping the candidate cations and their preferred bond geometries onto materials databases such as the Inorganic Crystal Structure Database (ICSD) and Materials Project, we identified real materials containing the corresponding metal–oxide units. In general, we observed good agreement between the calculated proton hopping barriers obtained in real crystal structures and those predicted by our motif database. We also discuss the limitations of our model and possible future extensions to improve its predictive capabilities. Overall, our model provides a first step for the rational design and quick screening of energy-efficient PCOs.

organic↗
DOE OSTI2026

Alumina Priming-Mediated Enhanced Binding of Diethylzinc with Carbonyl Groups in Poly(Methyl Methacrylate) during Vapor-Phase Infiltration

Vapor-phase infiltration (VPI) of inorganic materials in polymers is increasingly becoming popular for synthesizing various functional hybrid materials. While AlO x infiltration using trimethylaluminum (TMA) has been extensively studied, the mechanism of diethylzinc (DEZ)-based ZnO x infiltration, especially one that is initiated by AlO x priming, has not received much attention because highly reactive hydroxyl groups generated by AlO x -priming are expected to dominate the initial binding of DEZ, thus enabling the overall ZnO x VPI. Here, we interrogate the ZnO x infiltration mechanism in AlO x -primed poly(methyl methacrylate) (PMMA) in comparison to the control AlO x -only infiltration by utilizing a suite of complementary characterizations, including quartz crystal microbalance mass gain measurement, transmission electron microscopy, infrared reflection–absorption spectroscopy (IRRAS), and synchrotron X-ray absorption spectroscopy (XAS). The multivalent TMA precursor and associated hyperbranched AlO x network can quickly saturate the AlO x infiltration by clogging the polymer-free volume near the top. On the contrary, the ZnO x infiltration using divalent DEZ precursor, once activated via AlO x -priming, can lead to accelerated ZnOx infiltration. With the help of IRRAS, XAS, and density functional theory (DFT) simulations, we uncover that the AlO x -priming enhances the reactivity of neighboring carbonyl groups toward DEZ and opens up simultaneous reaction pathways, leading to accelerated high-fidelity infiltration of ZnO x .

36 MATERIALS SCIENCE↗
DOE OSTI2025

Microwave-Assisted Reactive CO 2 Capture with the SrCO 3 -Graphite System

This study details the initial development of a microwave heat-driven reactive CO 2 capture (RCC) process using the SrO/SrCO 3 cycle and graphitic carbon to absorb ppm-levels of CO 2 from humidified room temperature air and selectively convert it into CO. By combining first-principles density functional theory (DFT) simulations with thermogravimetric analysis (TGA) and X-ray diffraction (XRD) verification, it is demonstrated that SrO spontaneously absorbs moisture to generate Sr(OH) 2 followed by Sr(OH) 2 *1H 2 O, which then can spontaneously react with atmospheric levels of CO 2 to form SrCO 3 . The resulting SrCO 3 can then react with solid carbon at elevated temperatures to selectively produce CO and regenerate SrO. Graphitic carbon is an excellent microwave absorber that can quickly generate temperatures approaching 1000 °C and then cool to room temperature within minutes, potentially allowing for integration with intermittent electricity. It is shown that using microwaves to selectively heat a mixture of graphitic carbon and SrCO 3 produced CO with 85 ± 3% selectivity and stable performance over ten cycles. The rapid heating for release and room temperature CO 2 uptake processes appeared to prevent performance loss from particle sintering typically observed with traditional thermal systems. In conclusion, these results demonstrate a new RCC approach that adds to the growing number of potential technologies available for converting CO 2 to useful chemicals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Unsteady- and Steady-State Relative Permeability Study with X-ray and Acoustic Monitoring for CO 2 Storage in Deep Saline Aquifers

In this study, we link the multiphase flow measurements with controls of sedimentary structures (e.g., heterogeneity and anisotropy) on relative permeability to variations in ultrasonic velocities for two deep saline aquifer formations (Entrada and Bluff Sandstones) in the San Juan Basin of the Southwestern USA. The rock specimens were extracted from outcrop sites near Durango, CO, USA. They have distinct differences in grain size, cementation composition, and individual chemical amounts, despite both formations being eolian sandstones. We performed a series of unsteady- and steady-state CO 2 -brine relative permeability experiments under capillary-controlled displacement rates. Unsteady-state experiments were conducted at 71 °C and 9.65 MPa; steady-state experiments were conducted at 85 °C and 22.8 MPa and 89 °C and 24.1 MPa for the Bluff and Entrada Sandstones, respectively. During the unsteady experiments, X-ray computed tomography was used to visualize multiphase flow in porous media and quantify saturations during brine drainage under various flow rates. Scan images and saturation profiles indicate that the CO 2 distribution in the pore volume was strongly impacted by the presence of high-angle cross-laminations, heterogeneous rock structure, and direction of bedding orientation. Those factors contribute to dramatic and quick initial breakthroughs and affect the overall saturation dynamics. Steady-state relative permeability tests were conducted at net flow rates of 1 mL/min for both brine drainage and imbibition. During the experimental steps, the CO 2 fractional flow was increased and decreased for both drainage and imbibition scenarios to mimic the front when CO 2 contacts brine and behind the front when brine enters space previously occupied by CO 2 . It was found that compressional velocity decreased, while shear waves slightly increased as brine saturation decreased. The hysteresis effects for the relative permeability and acoustic velocities were distinct. Furthermore, the CO 2 /brine front stability is quantified by applying a mobility ratio approach to spot saturations at which the boundary line between fluids becomes uneven. The results presented in this work can potentially boost the quality and precision of forecasts for the CO 2 storage projects in which the vertical and horizontal core-scale heterogeneity and anisotropy impact the plume migration within host reservoirs.

carbon dioxide (CO2)↗
DOE OSTI2026

Heat Loss Effects on Emissions in an NH 3 RRQL Combustor

Ammonia (NH 3 ) is a carbon-free energy carrier with an infrastructure for production, storage, and distribution. There is interest in direct NH 3 combustion, but managing pollutant emissions is a key challenge, particularly nitric oxides (NO x ) due to the fuel-bound nitrogen atom, nitrous oxide (N 2 O), which is a potent greenhouse gas, and unburned NH 3 , which is harmful to humans and the environment. Rich staged combustor concepts with extended primary residence times (τ res,primary ), like Rich-Relax- Quick-mix-Lean (RRQL), offer a viable pathway for direct NH 3 combustion with low levels of NO x formation. However, minimizing secondary emissions such as N 2 O and unburned NH 3 and hydrogen (H 2 ) remains a critical challenge. Prior atmospheric-pressure studies have demonstrated that RRQL operation with sufficiently long τ res,primary enables substantial NO x relaxation and promotes NH 3 cracking to H 2 , if heat losses from the relaxation stage are limited. However, the combined influence of elevated pressure and long residence time on RRQL performance has not been explored. The present work examines RRQL operation at pressures up to 5 bar and elevated τ res,primary . Exhaust measurements of NO x , NH 3 , and N 2 O are used to quantify the extent of NO x relaxation and NH 3 cracking under nonadiabatic conditions. To contextualize and quantify the effects of heat losses in the experimental data, chemical reactor networks (CRNs) incorporating prescribed heat loss rates are employed to assess the sensitivity of emissions to thermal losses in the relaxation stage. Collectively, the results demonstrate that management and quantification of heat losses are essential to preserve NO x relaxation and limit NH 3 and N 2 O emissions.

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

Effects of Halides on Organic Compound Degradation during Plasma Treatment of Brines

Plasma has been proposed as an alternative strategy to treat organic contaminants in brines. Chemical degradation in these systems is expected to be partially driven by halogen oxidants, which have been detected in halide-containing solutions exposed to plasma. In this study, we characterized specific mechanisms involving the formation and reactions of halogen oxidants during plasma treatment. Here, we first demonstrated that addition of halides accelerated the degradation of a probe compound known to react quickly with halogen oxidants (i.e., para -hydroxybenzoate) but did not affect the degradation of a less reactive probe compound (i.e., benzoate). This effect was attributed to the degradation of para-hydroxybenzoate by hypohalous acids, which were produced via a mechanism involving halogen radicals as intermediates. We applied this mechanistic insight to investigate the impact of constituents in brines on reactions driven by halogen oxidants during plasma treatment. Bromide, which is expected to occur alongside chloride in brines, was required to enable halogen oxidant formation, consistent with the generation of halogen radicals from the oxidation of halides by hydroxyl radical. Other constituents typically present in brines (i.e., carbonates, organic matter) slowed the degradation of organic compounds, consistent with their ability to scavenge species involved during plasma treatment.

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

A Proxy Method to Bridge LCA Data Gaps Using Automated Material Classification and Probabilistic Under-Specification

Life cycle assessments (LCAs) are essential for understanding the environmental impacts of material production. However, gaps in life cycle inventory (LCI) data for material and chemical inputs present a key challenge for LCA practitioners, especially in the early design stages. Strategies for filling in these gaps require additional time and expertise, which can hinder the LCA’s completion. This study combined automatic material classification and probabilistic under-specification to create a time-efficient method to fill material LCI data gaps. To illustrate the proposed method, proxy environmental impact distributions were generated using publicly available material LCI data classified into the ChemOnt chemical taxonomy using the open-source chemical classification software ClassyFire. Input materials with data gaps were then classified into the same taxonomy, where proxy environmental impact values could be selected from the available distributions to quickly fill in any data gaps. Although these methods were applied to classify material production processes available in the Federal LCA Commons and Ecoinvent databases, they can be applied to any LCA database. This study shows that classifying materials by their chemical structure produces taxonomies with increased granularity relative to industrial classification, improving the ability of under-specified proxy data to be used for differentiating the environmental impacts of competing designs.

biological databases↗
DOE OSTI2021

La 2 Zr 2 O 7 Nanoparticle-Mediated Synthesis of Porous Al-Doped Li 7 La 3 Zr 2 O 12 Garnet

In this work, we modified the reaction pathway to quickly (minutes) incorporate lithium and stabilize the ionic conducting garnet phase by decoupling the formation of a La–Zr–O network from the addition of lithium. To do this, we synthesized La 2 Zr 2 O 7 (LZO) nanoparticles to which LiNO 3 was added. This method is a departure from typical solid-state synthesis methods that require high-energy milling to promote mixing and intimate particle–particle contact and from sol–gel syntheses as a unique porous microstructure is obtained. We show that the reaction time is limited by the rate of nitrate decomposition and that this method produces a porous high-Li-ion-conducting cubic phase, within an hour, that may be used as a starting structure for a composite electrolyte.

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

High-Throughput Screening for Boride Superconductors

A high-throughput screening using density functional calculations is performed to search for stable boride superconductors from the existing materials database. The workflow employs the fast frozen-phonon method as the descriptor to evaluate the superconducting properties quickly. Twenty-three stable candidates were identified during the screening. The superconductivity was obtained earlier experimentally or computationally for almost all found binary compounds. Previous studies on ternary borides are very limited. Here our extensive search among ternary systems confirmed superconductivity in known systems and found several new compounds. Among these discovered superconducting ternary borides, TaMo 2 B 2 shows the highest superconducting temperature of ∼12 K. Most predicted compounds were synthesized previously; therefore, our predictions can be examined experimentally. Our work also demonstrates that the boride systems can have diverse structural motifs that lead to superconductivity.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Augmentation of MS/MS Libraries with Spectral Interpolation for Improved Identification

Tandem mass spectrometry (MS/MS) is a primary tool for identification of small molecules and metabolites where resultant spectra are most commonly identified by matching them with spectra in MS/MS reference libraries. The high degree of variability in MS/MS spectrum acquisition techniques and parameters creates a significant challenge for building standardized reference libraries. Here we present a method to improve the usefulness of existing MS/MS libraries by augmenting available experimental spectra datasets with statistically interpolated spectra at unreported collision energies. We find that highly accurate spectral approximations can be interpolated from as few as three experimental spectra and that the interpolated spectra will be consistent with true spectra gathered from the same instrument as the experimental spectra. Supplementing existing spectral databases with interpolated spectra yields consistent improvements to identification accuracy on a range of instruments and precursor types. Applying this method yields significant improvements (~10% more spectra correctly identified) on large datasets (2,000 - 10,000 spectra), indicating this is a quick yet adept tool for improving spectral matching in situations where available reference libraries are not yet sufficient. We also find improvements of matching spectra across instrument types (between an Agilent Q-TOF and an Orbitrap Elite), at high collision energies (50 - 90 eV), and with smaller datasets available through MassBank.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Rapid Adaptation of Chemical Named Entity Recognition Using Few-Shot Learning and LLM Distillation

Named entity recognition (NER) has been widely used in chemical text mining for the automatic identification and extraction of chemical entities. However, existing chemical NER systems primarily focus on scenarios with abundant training data, requiring significant human effort on annotations. This poses challenges for applications in the chemical field, such as catalysis, where many advancements have traditionally relied on trial-and-error investigations and incremental adjustment of variables. This hinders catalysis science and technology progress in addressing emerging energy and environmental crises. In this work, we propose a few-shot NER model that can quickly adapt to extract new types of chemical entities by using only a limited number of annotated examples. Our model employs a metric-learning approach to transfer entity similarity knowledge from high-resource chemical domains (with abundant annotations) to enable effective entity recognition in low-resource specialized domains (limited annotation). We validate the effectiveness of our model on a few-shot chemical NER benchmark built based on six existing chemical NER data sets. Experiments show that the proposed few-shot NER model can achieve reasonable performance with only 5 examples per entity type and shows consistent improvement as the number of examples increases. Furthermore, we demonstrate how the proposed model can be trained with large language model (LLM) annotated data, opening a new pathway for rapid adaptation of NER systems. Furthermore, our approach leverages the knowledge broadness of large language models for chemistry while distilling this knowledge into a lightweight model suitable for efficient and in-house use.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

A Comprehensive Machine Learning Model for Metal–Ligand Binding Prediction: Applications in Chemistry and Biology

A machine-learning (ML) model that predicts metal–ligand binding constants was developed using the open-source Chemprop software. The model was trained on over 30,000 experimental log K 1 values, which include both protonation and metal–ligand stability constants, comprising over 3500 ligands and 10 2 metal ions from 73 total elements, thus generalizing beyond existing limited approaches, which focus only on specific metals or ligand families. The best-performing model included a combination of SMILES-based molecular representations along with descriptors for the metal ion and experimental conditions. It had an external test R 2 value of 0.942, and MAE value of 0.834. A “SMILES-only” simpler version also produced accurate predictions and preserved the binding trends, serving as a quick and easily accessible alternative for users without computational expertise. The SMILES-only model performed comparably to density functional theory (DFT) calculations but utilized a fraction of the computational resources. The model was successfully applied across diverse domains, including bioinorganic chemistry, heavy metal remediation, and sensor development and demonstrated its effectiveness as a rapid and reliable screening tool for both academic and industrial uses.

Ligands↗
DOE OSTI2020

TeraChem Cloud: A High-Performance Computing Service for Scalable Distributed GPU-Accelerated Electronic Structure Calculations

The encapsulation and commoditization of electronic structure arise naturally as interoperability, and the use of nontraditional compute resources (e.g., new hardware accelerators, cloud computing) remains important for the computational chemistry community. Here, we present TERACHEM CLOUD, a high-performance computing service (HPCS) that offers on-demand electronic structure calculations on both traditional HPC clusters and cloud-based hardware. The framework is designed using off-the-shelf web technologies and containerization to be extremely scalable and portable. Within the HPCS model, users can quickly develop new methods and algorithms in an interactive environment on their laptop while allowing TERACHEM CLOUD to distribute ab initio calculations across all available resources. This approach greatly increases the accessibility of hardware accelerators such as graphics processing units (GPUs) and flexibility for the development of new methods as additional electronic structure packages are integrated into the framework as alternative backends. Cost-performance analysis indicates that traditional nodes are the most cost-effective long-term solution, but commercial cloud providers offer cutting-edge hardware with competitive rates for short-term large-scale calculations. We demonstrate the power of the TERACHEM CLOUD framework by carrying out several showcase calculations, including the generation of 300,000 density functional theory energy and gradient evaluations on medium-sized organic molecules and reproducing 300 fs of nonadiabatic dynamics on the B800-B850 antenna complex in LH2, with the latter demonstration using over 50 Tesla V100 GPUs in a commercial cloud environment in 8 h for approximately $1250.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗