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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Coupled-cluster techniques for computational chemistry: The CFOUR program package

An up-to-date overview of the CFOUR program system is given. After providing a brief outline of the evolution of the program since its inception in 1989, a comprehensive presentation is given of its well-known capabilities for high-level coupled-cluster theory and its application to molecular properties. Subsequent to this generally well-known background information, much of the remaining content focuses on lesser-known capabilities of CFOUR, most of which have become available to the public only recently or will become available in the near future. Each of these new features is illustrated by a representative example, with additional discussion targeted to educating users as to classes of applications that are now enabled by these capabilities. Lastly, some speculation about future directions is given, and the mode of distribution and support for CFOUR are outlined in the appendix.

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

Properties of molecular clumps and cores in colliding magnetized flows

ABSTRACT We simulate the formation of molecular clouds in colliding flows of warm neutral medium with the adaptive mesh refinement code flash in eight simulations with varying initial magnetic field strength, between 0.01–5 μG. We include a chemical network to treat heating and cooling and to follow the formation of molecular gas. The initial magnetic field strength influences the fragmentation of the forming cloud because it prohibits motions perpendicular to the field direction and hence impacts the formation of large-scale filamentary structures. Molecular clump and core formation occurs anyhow. We identify 3D clumps and 3D cores, which are defined as connected, CO-rich regions. Additionally, 3D cores are heavily shielded. While we do not claim those 3D objects to be directly comparable to observations, this enables us to analyse their full virial state. With increasing field strength, we find more fragments with a smaller average mass; yet the dynamics of the forming clumps and cores only weakly depends on the initial magnetic field strength. The molecular clumps are mostly unbound, probably transient objects, which are weakly confined by ram pressure or thermal pressure, indicating that they are swept up by the turbulent flow. They experience significant fluctuations in the mass flux through their surface, such that the Eulerian reference frame shows a dominant time-dependent term due to their indistinct nature. We define the cores to encompass highly shielded molecular gas. Most cores are in gravitational-kinetic equipartition and are well described by the common virial parameter $\alpha _\mathrm{vir}$, while some undergo minor dispersion by kinetic surface effects.

Weis, M. (ORCID:0000000256838860)↗
DOE OSTI2021

Ion Pairing, Clustering and Transport in a LiFSI-TMP Electrolyte as Functions of Salt Concentration using Molecular Dynamics Simulations

Battery capacity is highly related to ion-pairing mechanisms in electrolytes, since a cluster formation can lead to dead Li formation, reducing the number of charge carriers and leading to capacity fading. We use molecular dynamics simulations to model an electrolyte comprising trimethyl phosphate (TMP) solvent and a lithium bis(fluorosulfonyl)imide (LiFSI) salt, exploring effects of salt concentration on solvation and ion-transport. We simulate the LiFSI-TMP electrolyte for salt concentrations of 0.7, 1.43 and 3.82 molar. A statistical analysis was performed to study ion-pairing, clustering, diffusivity, conductivity, and coordination of Li-ions, providing insights into relations between molecular structures and transport properties. Molecular structure of ionic components changes as concentration increases, from a predominant solvent separated ion pair (SSIP) and contact ion pair (CIP) to aggregate salt (AGG) and ionic cluster formation. Given the formation of the ionic cluster, the diffusion mechanism followed by Li-ions changes from a hopping/exchange to a vehicular mechanism as concentration increases; this is reflected in a decrease of ionic conductivities. Ionicity was also calculated to reveal how the ionic motion changes from an uncorrelated to a correlated one as the salt concentration increases. Furthermore, we also compared our results with experimental calculations performed for similar electrolyte systems

25 ENERGY STORAGE↗
DOE OSTI2026

EvoDiffMol: evolutionary diffusion framework for 3D molecular design with optimized properties

Designing molecules with specific target properties remains a fundamental challenge in computational chemistry. While existing approaches show promise, most rely on simplified representations like SMILES strings or 2D graphs that lack essential three-dimensional geometric information. We present EvoDiffMol, a computational framework that integrates evolutionary algorithms with three-dimensional diffusion models for property-driven molecular generation. The method operates through adaptive evolutionary optimization, where population-based selection guides the generation process toward desired property landscapes. EvoDiffMol supports both unconstrained molecular design and scaffold-constrained generation that preserves fixed substructures while optimizing complementary regions. Comprehensive evaluation demonstrates exceptional performance, achieving the highest drug-likeness score (0.94) among all compared state-of-the-art methods while maintaining excellent validity, uniqueness, and novelty. Beyond single property optimization, the framework demonstrates flexible multi-property optimization capabilities, simultaneously controlling multiple molecular descriptors including synthetic accessibility, lipophilicity, topological polar surface area, and clinically relevant ADMET properties such as cardiotoxicity (hERG) and intestinal permeability (Caco-2). This adaptability spans from simple descriptors to practical pharmaceutical endpoints without requiring complete model retraining. The framework achieves precise control over target property values, generating molecules with properties closely matching specified targets for both single and multiple descriptors. Scaffold-constrained experiments preserve fixed molecular cores while maintaining effective property optimization. The three-dimensional representation offers advantages in maintaining structural validity during iterative optimization, with potential for geometry-aware applications in materials science and drug discovery.

3D molecular generation↗
DOE OSTI2022

Natural organic matter composition and nanomaterial surface coating determine the nature of platinum nanomaterial-natural organic matter corona

Natural organic matter corona (NOM corona) is an interfacial area between nanomaterials (NMs) and the sur- rounding environment, which gives rise to NMs' unique surface identity. While the importance of the formation of natural organic matter (NOM) corona on engineered nanomaterials (NMs) to NM behavior, fate, and toxicity has been well-established, the understanding of how NOM molecular properties affect NOM corona composition remains elusive due to the complexity and heterogeneity of NOM. This is further complicated by the variation of NOMs from different origins. Here we use eight NOM isolates of different molecular composition and ultrahigh resolution Fourier-transform ion cyclotron resonance-mass spectrometry (ESI-FT-ICR-MS) to determine the mol- ecular composition of platinum NM-NOM corona as a function of NOM composition and NM surface coating. We observed that the composition of PtNM-NOM corona varied with the composition of the original NOM. The per- centage of NOM formulas that formed PVP-PtNM-NOM corona was higher than those formed citrate-PtNM-NOM corona, due to increased sorption of NOM formulas, in particular condensed hydrocarbons, to the PVP coating. The relative abundance of heteroatom formulas (CHON, CHOS, and CHOP) was higher in the PVP-PtNM-NOM corona than in citrate-PtNM-corona which was in turn higher than those in the original NOM isolate, indicating preferential partitioning of heteroatom-rich molecules to NM surfaces. The relative abundance of CHO, CHON, CHOS, CHOP and condensed hydrocarbons in PtNM-NOM corona increased with their increase in NOM isolates. Furthermore, PtNM-NOM corona is rich with compounds with high molecular weight. This study demonstrates that the composition and properties of PtNM-NOM corona depend on NOM molecular properties and PtNM sur- face coating. The results here provide evidence of molecular interactions between NOM and NMs, which are crit- ical to understanding NM colloidal properties (e.g., surface charge and stability), interaction forces (e.g., van der Waals and hydrphobic), environmental behaviors (e.g., aggregation, disso.

Baalousha, Mohammed↗
DOE OSTI2021

Dipole Moment Calculations Using Multiconfiguration Pair-Density Functional Theory and Hybrid Multiconfiguration Pair-Density Functional Theory

The dipole moment is the molecular property that most directly indicates molecular polarity. The accuracy of computed dipole moments depends strongly on the quality of the calculated electron density, and the breakdown of single-reference methods for strongly correlated systems can lead to poor predictions of the dipole moments in those cases. Here, we derive the analytical expression for obtaining the electric dipole moment by multiconfiguration pair density functional theory (MCPDFT), and we assess the accuracy of MC-PDFT for predicting dipole moments at equilibrium and nonequilibrium geometries. We show that MC-PDFT dipole moment curves have reasonable behavior even for stretched geometries, and they significantly improve upon the CASSCF results by capturing more electron correlation. Herein, the analysis of a dataset consisting of 18 first-row transition metal diatomics and 6 main-group polyatomic molecules with multireference character suggests that MC-PDFT and its hybrid extension (HMC-PDFT) perform comparably to CASPT2 and MRCISD+Q methods and have a mean unsigned deviation of 0.2–0.3 D with respect to the best available dipole moment reference values. We explored the dependence of the predicted dipole moments upon the choice of the on-top density functional and active space, and we recommend the tPBE and hybrid tPBE0 on-top choices for the functionals combined with the moderate correlated participating orbital scheme for selecting the active space. With these choices, the mean unsigned deviations (in debyes) of the calculated equilibrium dipole moments from the best estimates are 0.77 for CASSCF, 0.29 for MC-PDFT, 0.24 for HMCPDFT, 0.28 for CASPT2, and 0.25 for MRCISD+Q. These results are encouraging because the computational cost of MC-PDFT or HMC-PDFT is largely reduced compared to the CASPT2 and MRCISD+Q methods.

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

Augmenting Molecular Graphs with Geometries via Machine Learning Interatomic Potentials

Accurate molecular property predictions require 3D geometries, which are typically obtained using expensive methods such as density functional theory (DFT). Here, we attempt to obtain molecular geometries by relying solely on machine learning interatomic potential (MLIP) models. To this end, we first curate a large-scale molecular relaxation dataset comprising 3.5 million molecules and 300 million snapshots. Then MLIP pre-trained models are trained with supervised learning to predict energy and forces given 3D molecular structures. Once trained, we show that the pre-trained models can be used in different ways to obtain geometries either explicitly or implicitly. First, it can be used to obtain approximate low-energy 3D geometries via geometry optimization. While these geometries do not consistently reach DFT-level chemical accuracy or convergence, they can still improve downstream performance compared to non-relaxed structures. To mitigate potential biases and enhance downstream predictions, we introduce geometry fine-tuning based on the relaxed 3D geometries. Second, the pre-trained models can be directly fine-tuned for property prediction when ground truth 3D geometries are available. Our results demonstrate that MLIP pre-trained models trained on relaxation data can learn transferable molecular representations to improve downstream molecular property prediction and can provide practically valuable but approximate molecular geometries that benefit property predictions. Our code is publicly available at: https://github.com/divelab/AIRS/.

Fu, Cong [Texas A & M Univ., College Station, TX (↗
DOE OSTI2025

32 examples of LLM applications in materials science and chemistry: towards automation, assistants, agents, and accelerated scientific discovery

Abstract Large language models (LLMs) are reshaping many aspects of materials science and chemistry research, enabling advances in molecular property prediction, materials design, scientific automation, knowledge extraction, and more. Recent developments demonstrate that the latest class of models are able to integrate structured and unstructured data, assist in hypothesis generation, and streamline research workflows. To explore the frontier of LLM capabilities across the research lifecycle, we review applications of LLMs through 32 total projects developed during the second annual LLM hackathon for applications in materials science and chemistry, a global hybrid event. These projects spanned seven key research areas: (1) molecular and material property prediction, (2) molecular and material design, (3) automation and novel interfaces, (4) scientific communication and education, (5) research data management and automation, (6) hypothesis generation and evaluation, and (7) knowledge extraction and reasoning from the scientific literature. Collectively, these applications illustrate how LLMs serve as versatile predictive models, platforms for rapid prototyping of domain-specific tools, and much more. In particular, improvements in both open source and proprietary LLM performance through the addition of reasoning, additional training data, and new techniques have expanded effectiveness, particularly in low-data environments and interdisciplinary research. As LLMs continue to improve, their integration into scientific workflows presents both new opportunities and new challenges, requiring ongoing exploration, continued refinement, and further research to address reliability, interpretability, and reproducibility.

Computer Science↗
DOE OSTI2023

Targeting tissues via dynamic human systems modeling in generative design

Drug discovery is a complex, costly process with high failure rates. A successful drug should bind to a target, be deliverable to an intended site of activity, and promote a desired pharmacological effect without causing toxicity. Typically, these factors are evaluated in series over the course of a pipeline where the number of candidates is sequentially whittled down from a very large initial pool. One promise of AI-driven discovery is the opportunity to evaluate multiple facets of drug performance in parallel. However, despite ML-driven advancements, current models for pharmacological property prediction are exclusively trained to predict molecular properties, ignoring important, dynamic biodistribution and bioactivity effects. Here, we present our progress towards incorporating quantitative systems physiology models into an AI-enabled drug design and molecular generation pipeline. Within a genetic algorithm, we include human-relevant physiologically based pharmacokinetic (PBPK) models. These PBPK models leverage properties that are predicted by a fine-tuned molecular language model. Together, these models will aid in capturing the mapping between molecules and therapeutic outcomes that is necessary to accelerate the drug discovery process.

Fox, Zach↗
DOE OSTI2020

A step in the direction of resolving the paradox of Perdew–Zunger self-interaction correction. II. Gauge consistency of the energy density at three levels of approximation

The Perdew-Zunger(PZ) self-interaction correction (SIC) was designed to correct the one-electron limit of any approximate density functional for the exchange-correlation (xc) energy, while yielding no correction to the exact functional. Unfortunately, it spoils the slowly-varying-in-space limits of the uncorrected approximate functionals, where those functionals are right by construction. The right limits can be restored by locally scaling down the energy density of the PZ SIC in many-electron regions, but then a spurious correction to the exact functional would be found unless the self-Hartree and exact self-xc terms of the PZ SIC energy density were expressed in the same gauge. Only the local density approximation satisfies the same-gauge condition for the energy density, which explains why the recent local-scaling SIC (LSIC) is found here to work excellently for atoms and molecules only with this basic approximation, and not with the more advanced generalized gradient approximations (GGAs) and meta-GGAs, which lose the Hartree gauge via simplifying integrations by parts. The transformation of energy density that achieves the Hartree gauge for the exact xc functional can also be applied to approximate functionals. Doing so leads to a simple scaled-down self-interaction (sdSIC) correction that is typically much more accurate than PZ SIC in tests for many molecular properties (including equilibrium bond lengths). The present work shows unambiguously that the largest errors of PZ SIC applied to standard functionals at three levels of approximation can be removed by restoring their correct slowlyvarying- density limits. Here, it also confirms the relevance of these limits to atoms and molecules.

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

Equation-of-motion coupled-cluster method with double electron-attaching operators: Theory, implementation, and benchmarks

We report a production-level implementation of the equation-of-motion (EOM) coupled-cluster (CC) method with double electron-attaching (DEA) EOM operators of 2 p and 3 p 1 h types, EOM-DEA-CCSD. This ansatz, suitable for treating electronic structure patterns that can be described as two-electrons-in-many orbitals, represents a useful addition to the EOM-CC family of methods. We analyze the performance of EOM-DEA-CCSD for energy differences and molecular properties. By considering reduced quantities, such as state and transition one-particle density matrices, we compare EOM-DEA-CCSD wave functions with wave functions computed by other EOM-CCSD methods. The benchmarks illustrate that EOM-DEA-CCSD is capable of treating diradicals, bond-breaking, and some types of conical intersections.

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

“Freedom of design” in chemical compound space: towards rational in silico design of molecules with targeted quantum-mechanical properties

The rational design of molecules with targeted quantum-mechanical (QM) properties requires an advanced understanding of the structure–property/property–property relationships (SPR/PPR) that exist across chemical compound space (CCS). In this work, we analyze these fundamental relationships in the sector of CCS spanned by small (primarily organic) molecules using the recently developed QM7-X dataset, a systematic, extensive, and tightly converged collection of 42 QM properties corresponding to ≈4.2M equilibrium and non-equilibrium molecular structures containing up to seven heavy/non-hydrogen atoms (including C, N, O, S, and Cl). By characterizing and enumerating progressively more complex manifolds of molecular property space—the corresponding high-dimensional space defined by the properties of each molecule in this sector of CCS—our analysis reveals that one has a substantial degree of flexibility or “freedom of design” when searching for a single molecule with a desired pair of properties or a set of distinct molecules sharing an array of properties. To explore how this intrinsic flexibility manifests in the molecular design process, we used multi-objective optimization to search for molecules with simultaneously large polarizabilities and HOMO–LUMO gaps; analysis of the resulting Pareto fronts identified non-trivial paths through CCS consisting of sequential structural and/or compositional changes that yield molecules with optimal combinations of these properties.

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

TURBOMOLE: Modular program suite for ab initio quantum-chemical and condensed-matter simulations

TURBOMOLE is a collaborative, multi-national software development project aiming to provide highly efficient and stable computational tools for quantum chemical simulations of molecules, clusters, periodic systems, and solutions. The TURBOMOLE software suite is optimized for widely available, inexpensive, and resource-efficient hardware such as multi-core workstations and small computer clusters. TURBOMOLE specializes in electronic structure methods with outstanding accuracy–cost ratio, such as density functional theory including local hybrids and the random phase approximation (RPA), GW-Bethe–Salpeter methods, second-order Møller–Plesset theory, and explicitly correlated coupled-cluster methods. TURBOMOLE is based on Gaussian basis sets and has been pivotal for the development of many fast and low-scaling algorithms in the past three decades, such as integral-direct methods, fast multipole methods, the resolution-of-the-identity approximation, imaginary frequency integration, Laplace transform, and pair natural orbital methods. This review focuses on recent additions to TURBOMOLE’s functionality, including excited-state methods, RPA and Green’s function methods, relativistic approaches, high-order molecular properties, solvation effects, and periodic systems. A variety of illustrative applications along with accuracy and timing data are discussed. Moreover, available interfaces to users as well as other software are summarized. TURBOMOLE’s current licensing, distribution, and support model are discussed, and an overview of TURBOMOLE’s development workflow is provided. Challenges such as communication and outreach, software infrastructure, and funding are highlighted.

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

Multiple-objective Reinforcement Learning for Inverse Design and Identification

The aim of inverse chemical design is to develop new molecules with given optimized molecular properties or objectives. Recently, generative deep learning (DL) networks are considered as the state-of-the-art in inverse chemical design and have achieved early success in generating molecular structures with desired properties in the pharmaceutical and material chemistry fields. However, satisfying a large number (> 10 objectives) of molecular objectives is a limitation of current generative models. To improve the model’s ability to handle a large number of molecule design objectives, we developed a Reinforcement Learning (RL) based generative framework to optimize chemical molecule generation. Our use of Curriculum Learning (CL) to fine-tune the pre-trained generative network allowed the model to satisfy up to 21 objectives and increase the generative network’s robustness. The experiments show that the proposed multiple-objective RL-based generative model can correctly identify unknown molecules with an 83% to 100% success rate, compared to the baseline approach of 0%. Additionally, this proposed generative model is not limited to just chemistry research challenges; we anticipate that problems that utilize RL with multiple objectives will benefit from this framework.

reinforcement learning, generative model, inverse ↗
DOE OSTI2021

Three-Dimensional Modeling of the Explicit Chemistry of Organic Aerosols: Formation, Removal, and Comparison with Observations

The major goals of the project were to further our understanding of the formation of secondary organic aerosols (SOA) from major anthropogenic and biogenic precursors, and to evaluate the GECKO-A chemistry model in the context of three DOE-led field campaigns (GoAmazon, BBOP, and HI-SCALE) with the long-term objective of improving the treatment of organic gases and particles in three-dimensional regional and global models. The three field campaigns were selected to evaluate and improve specific aspects of GECKO-A: (i) GoAmazon to help quantify interactions of biogenic and anthropogenic emissions; (ii) BBOP to predict the UV-visible absorption properties of biomass burning aerosols (brown carbon) from explicit molecular properties; and (iii) HI-SCALE to evaluate new model components that include aqueous (cloud) processing of organic gases and aerosols.

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

Oxadiazole-based Heterocycles as Building Block for Material Property Control: Design, Synthesis, and Characterization of 3,4-Bis(3-(4-nitro-1,2,5-oxadiazol-3-yl)- 1,2,4-oxadiazol-5-yl)-1,2,5-oxadiazole (LLM-210)

This paper presents an approach to novel oxadiazole-based melt-castable energetic material, LLM-210. Used in pour-in process and 3D printing, melt-castable energetic materials (MCEM) must possess required physical and safety properties of industry-standard energetic materials, also a narrow range of melting point between 80-100 °C. Melting point is difficult to accurately predict, therefore, searching for MCEM is challenging. 3,4-Bis(5-(4-nitro-1,2,5-oxadiazol-3-yl)-1,2,4-oxadiazol3-yl)-1,2,5-oxadiazole (LLM-205), composed of five-oxadiazole rings allied via carbon-carbon bonds, was selected as new MCEM candidate. The target molecule was synthesized and characterized, showing a density of 1.807g/cm3 ; thermal decomposing temperature at 299 °C; and insensitive to external stimuli, but the melting point of 104 °C was out of the rang. Based on the molecular properties and ab initio calculations, 3,4-bis(3-(4-nitro-1,2,5-oxadiazol-3-yl)-1,2,4-oxadiazol-5-yl)- 1,2,5-oxadiazole (LLM-210), an isomer of LLM-205, was designed and synthesized from a different way. LLM-210 was characterized as MCEM, possessing melting point of 87 °C; density of 1.812 g/cm3 ; and exothermally decomposing temperature at 314 °C; being insensitive to external stimuli. In this study, the relationship of molecular structure to properties of LLM-205 and LLM-210 based on the results of Density Functional Theory (DFT) and X-ray crystallographic analysis is also briefly discussed.

Chemistry - Chemical explosives↗