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cclib 2.0: An updated architecture for interoperable computational chemistry

Interoperability in computational chemistry is elusive, impeded by the independent development of software packages and idiosyncratic nature of their output files. The cclib library was introduced in 2006 as an attempt to improve this situation by providing a consistent interface to the results of various quantum chemistry programs. The shared API across programs enabled by cclib has allowed users to focus on results as opposed to output and to combine data from multiple programs or develop generic downstream tools. Initial development, however, did not anticipate the rapid progress of computational capabilities, novel methods, and new programs; nor did it foresee the growing need for customizability. Here, we recount this history and present cclib 2, focused on extensibility and modularity. We also introduce recent design pivots—the formalization of cclib’s intermediate data representation as a tree-based structure, a new combinator-based parser organization, and parsed chemical properties as extensible objects.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH

Computational chemistry

With the advent of supercomputers, modern computational chemistry algorithms and codes, a powerful tool was created to help fill NASA's continuing need for information on the properties of matter in hostile or unusual environments. Computational resources provided under the National Aerodynamics Simulator (NAS) program were a cornerstone for recent advancements in this field. Properties of gases, materials, and their interactions can be determined from solutions of the governing equations. In the case of gases, for example, radiative transition probabilites per particle, bond-dissociation energies, and rates of simple chemical reactions can be determined computationally as reliably as from experiment. The data are proving to be quite valuable in providing inputs to real-gas flow simulation codes used to compute aerothermodynamic loads on NASA's aeroassist orbital transfer vehicles and a host of problems related to the National Aerospace Plane Program. Although more approximate, similar solutions can be obtained for ensembles of atoms simulating small particles of materials with and without the presence of gases. Computational chemistry has application in studying catalysis, properties of polymers, all of interest to various NASA missions, including those previously mentioned. In addition to discussing these applications of computational chemistry within NASA, the governing equations and the need for supercomputers for their solution is outlined.

Arnold, J. O.

The role of computational chemistry in the science and measurements of the atmosphere

The role of computational chemistry in determining the stability, photochemistry, spectroscopic parameters, and parameters for estimating reaction rates of atmospheric constituents is discussed. Examples dealing with the photolysis cross sections of HOCl and (1 Delta g) O2 and with the stability of gaseous NH4Cl and asymmetric ClO3 are presented. It is concluded that computational chemistry can play an important role in the study of atmospheric constituents, particularly reactive and short-lived species which are difficult to investigate experimentally.

Phillips, D. H.

Computational chemistry and aeroassisted orbital transfer vehicles

An analysis of the radiative heating phenomena encountered during a typical aeroassisted orbital transfer vehicle (AOTV) trajectory was made to determine the potential impact of computational chemistry on AOTV design technology. Both equilibrium and nonequilibrium radiation mechanisms were considered. This analysis showed that computational chemistry can be used to predict (1) radiative intensity factors and spectroscopic data; (2) the excitation rates of both atoms and molecules; (3) high-temperature reaction rate constants for metathesis and charge exchange reactions; (4) particle ionization and neutralization rates and cross sections; and (5) spectral line widths.

Cooper, D. M.

ChemGraph as an agentic framework for computational chemistry workflows

Atomistic simulations are essential in chemistry and materials science but remain challenging to run due to the expert knowledge required for the setup, execution, and validation stages of these calculations. We present ChemGraph, an agentic framework powered by artificial intelligence and state-of-the-art simulation tools to streamline and automate computational chemistry and materials science workflows. ChemGraph leverages graph neural network-based foundation models for accurate yet computationally efficient calculations and large language models (LLMs) for natural language understanding, task planning, and scientific reasoning to provide an intuitive and interactive interface. We evaluate ChemGraph across 13 benchmark tasks and demonstrate that smaller LLMs (GPT-4o-mini, Claude-3.5-haiku, Qwen-2.5-14B) perform well on simple workflows, while more complex tasks benefit from using larger models. Importantly, we show that decomposing complex tasks into smaller subtasks through a multi-agent framework enables GPT-4o to reach perfect accuracy and smaller LLMs to match or exceed single-agent GPT-4o's performance in these benchmarks.

Computational chemistry

Algorithms versus architectures for computational chemistry

The algorithms employed are computationally intensive and, as a result, increased performance (both algorithmic and architectural) is required to improve accuracy and to treat larger molecular systems. Several benchmark quantum chemistry codes are examined on a variety of architectures. While these codes are only a small portion of a typical quantum chemistry library, they illustrate many of the computationally intensive kernels and data manipulation requirements of some applications. Furthermore, understanding the performance of the existing algorithm on present and proposed supercomputers serves as a guide for future programs and algorithm development. The algorithms investigated are: (1) a sparse symmetric matrix vector product; (2) a four index integral transformation; and (3) the calculation of diatomic two electron Slater integrals. The vectorization strategies are examined for these algorithms for both the Cyber 205 and Cray XMP. In addition, multiprocessor implementations of the algorithms are looked at on the Cray XMP and on the MIT static data flow machine proposed by DENNIS.

Partridge, H.

Computational Chemistry and Lubrication

Members of NASA Lewis Research Center's Tribology and Surface Science Branch are applying high-level computational chemistry techniques to the development of new lubrication systems for space applications and for future advanced aircraft engines. The next generation of gas turbine engines will require a liquid lubricant to function at temperatures in excess of 350 C in oxidizing environments. Conventional hydrocarbon-based lubricants are incapable of operating in these extreme environments, but a class of compounds known as the perfluoropolyether (PFAE) liquids (see the preceding illustration) shows promise for such applications. These commercially available products are already being used as lubricants in conditions where low vapor pressure and chemical stability are crucial, such as in satellite bearings and composite disk platters. At higher temperatures, however, these compounds undergo a decomposition process that is assisted (catalyzed) by metal and metal oxide bearing surfaces. This decomposition process severely limits the applicability of PFAE's at higher temperatures. A great deal of laboratory experimentation has revealed that the extent of fluid degradation depends on the chemical properties of the bearing surface materials. Lubrication engineers would like to understand the chemical breakdown mechanism to design a less vulnerable PFAE or to develop a chemical additive to block this degradation.

Zehe, Michael J.

In the Mix : A Workshop Merging Computational Chemistry and Electrochemistry Alongside Data Science

As chemistry expands to more complex and interdisciplinary areas, a new generation of diverse researchers must engage with science and learn effective cross-disciplinary collaboration and communication. To these ends, we designed and implemented In the Mix, a graduate student-led, two-day workshop for undergraduate students promoting collaborative science in the context of energy storage innovations. Here, the interactive workshop was designed for future and emerging researchers to gain hands-on experience with data science, computational chemistry, and electrochemistry techniques that are critical for developing materials for battery technologies. Participants also visited commercial renewable energy facilities to help them connect discovery-based research with industry and broader societal considerations. The workshop content and structure ensured that participants experienced the interrelatedness of the fields and understood the importance of collaborative research to yield scientific advances with real-world applications. An external team evaluated the workshop and participants’ perceptions of their experiences. While our research context was energy storage, the workshop goals and outcomes are applicable to other contexts. Interdisciplinary, experiential workshops are a key avenue to broadening participation in science and research, and the ideas presented here can be readily modified for other scientific contexts and/or incorporated as broader impact activities.

25 ENERGY STORAGE

Computational chemistry research

Task 41 is composed of two parts: (1) analysis and design studies related to the Numerical Aerodynamic Simulation (NAS) Extended Operating Configuration (EOC) and (2) computational chemistry. During the first half of 1987, Dr. Levin served as a member of an advanced system planning team to establish the requirements, goals, and principal technical characteristics of the NAS EOC. A paper entitled 'Scaling of Data Communications for an Advanced Supercomputer Network' is included. The high temperature transport properties (such as viscosity, thermal conductivity, etc.) of the major constituents of air (oxygen and nitrogen) were correctly determined. The results of prior ab initio computer solutions of the Schroedinger equation were combined with the best available experimental data to obtain complete interaction potentials for both neutral and ion-atom collision partners. These potentials were then used in a computer program to evaluate the collision cross-sections from which the transport properties could be determined. A paper entitled 'High Temperature Transport Properties of Air' is included.

Levin, Eugene

A numerical method for parameterization of atmospheric chemistry - Computation of tropospheric OH

An efficient and stable computational scheme for parameterization of atmospheric chemistry is described. The 24-hour-average concentration of OH is represented as a set of high-order polynomials in variables such as temperature, densities of H2O, CO, O3, and NO(t) (defined as NO + NO2 + NO3 + 2N2O5 + HNO2 + HNO4) as well as variables determining solar irradiance: cloud cover, density of the overhead ozone column, surface albedo, latitude, and solar declination. This parameterization of OH chemistry was used in the three-dimensional study of global distribution of CH3CCl3. The proposed computational scheme can be used for parameterization of rates of chemical production and loss or of any other output of a full chemical model.

Spivakovsky, C. M.

El Agente: An autonomous agent for quantum chemistry

Computational chemistry tools are widely used to study the behavior of chemical phenomena. Yet, the complexity of these tools can make them inaccessible to non-specialists and challenging even for experts. In this work, we introduce El Agente Q, an LLM-based multi-agent system that dynamically generates and executes quantum chemistry workflows from natural language user prompts. The system is built on a novel cognitive architecture featuring a hierarchical memory framework that enables flexible task decomposition, adaptive tool selection, post-analysis, and autonomous file handling and submission. El Agente Q is benchmarked on six university-level course exercises and two case studies, demonstrating robust problem-solving performance (averaging >87% task success) and adaptive error handling through in situ debugging. It also supports longer-term, multi-step task execution for more complex workflows, while maintaining transparency through detailed action trace logs. Together, these capabilities lay the foundation for increasingly autonomous and accessible quantum chemistry.

agentic systems

Computational Chemistry of Adhesive Bonds

This investigation is intended to determine the electrical mechanical, and chemical properties of adhesive bonds at the molecular level. The initial determinations will be followed by investigations of the effects of environmental effects on the chemistry and properties of the bond layer.

Phillips, Donald H.

A Computational Chemistry Database for Semiconductor Processing

The concept of 'virtual reactor' or 'virtual prototyping' has received much attention recently in the semiconductor industry. Commercial codes to simulate thermal CVD and plasma processes have become available to aid in equipment and process design efforts, The virtual prototyping effort would go nowhere if codes do not come with a reliable database of chemical and physical properties of gases involved in semiconductor processing. Commercial code vendors have no capabilities to generate such a database, rather leave the task to the user of finding whatever is needed. While individual investigations of interesting chemical systems continue at Universities, there has not been any large scale effort to create a database. In this presentation, we outline our efforts in this area. Our effort focuses on the following five areas: 1. Thermal CVD reaction mechanism and rate constants. 2. Thermochemical properties. 3. Transport properties.4. Electron-molecule collision cross sections. and 5. Gas-surface interactions.

Jaffe, R.

Chemical research projects office: An overview and bibliography, 1975-1980

The activities of the Chemical Research Projects Office at Ames Research Center, Moffett Field, California are reported. The office conducts basic and applied research in the fields of polymer chemistry, computational chemistry, polymer physics, and physical and organic chemistry. It works to identify the chemical research and technology required for solutions to problems of national urgency, synchronous with the aeronautic and space effort. It conducts interdisciplinary research on chemical problems, mainly in areas of macromolecular science and fire research. The office also acts as liaison with the engineering community and assures that relevant technology is made available to other NASA centers, agencies, and industry. Recent accomplishments are listed in this report. Activities of the three research groups, Polymer Research, Aircraft Operating and Safety, and Engineering Testing, are summarized. A complete bibliography which lists all Chemical Research Projects Office publications, contracts, grants, patents, and presentations from 1975 to 1980 is included.

Kourtides, D. A.

Supercomputing in Aerospace

Topics addressed include: numerical aerodynamic simulation; computational mechanics; supercomputers; aerospace propulsion systems; computational modeling in ballistics; turbulence modeling; computational chemistry; computational fluid dynamics; and computational astrophysics.

Kutler, Paul

Development and Application of Computational/In Vitro Toxicological Methods for Chemical Hazard Risk Reduction of New Materials for Advanced Weapon Systems

The development of quantitative structure-activity relationship (QSAR) is essential for reducing the chemical hazards of new weapon systems. The current collaboration between HEST (toxicology research and testing), MLPJ (computational chemistry) and PRS (computational chemistry, new propellant synthesis) is focusing R&D efforts on basic research goals that will rapidly transition to useful products for propellant development. Computational methods are being investigated that will assist in forecasting cellular toxicological end-points. Models developed from these chemical structure-toxicity relationships are useful for the prediction of the toxicological endpoints of new related compounds. Research is focusing on the evaluation tools to be used for the discovery of such relationships and the development of models of the mechanisms of action. Combinations of computational chemistry techniques, in vitro toxicity methods, and statistical correlations, will be employed to develop and explore potential predictive relationships; results for series of molecular systems that demonstrate the viability of this approach are reported. A number of hydrazine salts have been synthesized for evaluation. Computational chemistry methods are being used to elucidate the mechanism of action of these salts. Toxicity endpoints such as viability (LDH) and changes in enzyme activity (glutahoione peroxidase and catalase) are being experimentally measured as indicators of cellular damage. Extrapolation from computational/in vitro studies to human toxicity, is the ultimate goal. The product of this program will be a predictive tool to assist in the development of new, less toxic propellants.

Frazier, John M.