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NASA NTRS1972

Classical scattering calculations for diatomic molecules: A general procedure and application to the microwave spectrum O2

Many properties of gaseous systems such as electromagnetic absorption and emission, sound dispersion and absorption, may be elucidated if the nature of collisions between the particles in the system is understood. A procedure for the calculation of the classical trajectories of two interacting diatomic molecules is described. The dynamics of the collision will be assumed to be that of two rigid rotors moving in a specified potential. The actual outcome of a representative sample of many trajectories at 298K was computed, and the use of these values at any temperature for calculations of various molecular properties will be described. Calculations performed for the O2 microwave spectrum are given to demonstrate the use of the procedure described.

Mingelgrin, U.↗

NASA NTRS2003

Spirochaeta americana sp. nov., a new haloalkaliphilic, obligately anaerobic spirochaete isolated from soda Mono Lake in California

A novel, obligately anaerobic, mesophilic, haloalkaliphilic spirochaete, strain ASpG1(T), was isolated from sediments of the alkaline, hypersaline Mono Lake in California, USA. Cells of the Gram-negative strain were motile and spirochaete-shaped with sizes of 0.2-0.22 x 8-18 microm. Growth of the strain was observed between 10 and 44 degrees C (optimum 37 degrees C), in 2-12% (w/v) NaCl (optimum 3% NaCl) and between pH 8 and 10.5 (optimum pH 9.5). The novel strain was strictly alkaliphilic, required high concentrations of carbonates in the medium and was capable of utilizing D-glucose, fructose, maltose, sucrose, starch and D-mannitol. End products of glucose fermentation were H2, acetate, ethanol and formate. Strain ASpG(T) was resistant to kanamycin and rifampicin, but sensitive to gentamicin, tetracycline and chloramphenicol. The G + C content of its DNA was 58.5 mol%. DNA-DNA hybridization analysis of strain ASpG1(T) with its most closely related species, Spirochaeta alkalica Z-7491(T), revealed a hybridization value of only 48.7%. On the basis of its physiological and molecular properties, strain ASpG1(T) appears to represent a novel species of the genus Spirochaeta, for which the name Spirochaeta americana is proposed (type strain ASpG1(T) =ATCC BAA-392(T) = DSM 14872(T)).

Sodium Chloride/metabolism↗

DOE OSTI2020

Neural Network Interatomic Potentials

In this project, we investigate the use of neural networks for the prediction of molecular properties, namely the interatomic potential. We use the machine learning package Tensorflow to build a variety of neural networks and compare performance with a popular Fortran package - Atomic Energy Networks (aenet). There are two primary goals for this work: 1) use the wide availability of different optimization techniques in Tensorflow to outperform aenet and 2) use new descriptors that can outperform Behler descriptors.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2020

Isotope effects in molecular structures and electronic properties of liquid water via deep potential molecular dynamics based on the SCAN functional

Here, Feynman path-integral deep potential molecular dynamics (PI-DPMD) calculations have been employed to study both light (H 2 O) and heavy water (D 2 O) within the isothermalisobaric ensemble. In particular, the deep neural network is trained based on ab initio data obtained from the strongly constrained and appropriately normed (SCAN) exchange-correlation functional. Because of the lighter mass of hydrogen than deuteron, the properties of light water are more influenced by nuclear quantum effect than those of heavy water. Clear isotope effects are observed and analyzed in terms of hydrogen-bond structure and electronic properties of water that are closely associated with experimental observables. The molecular structures of both liquid H 2 O and D 2 O agree well with the data extracted from scattering experiments. The delicate isotope effects on radial distribution functions and angular distribution functions are well reproduced as well. Our approach demonstrates that deep neural network combined with SCAN functional based ab initio molecular dynamics provides an accurate theoretical tool for modeling water and its isotope effects.

36 MATERIALS SCIENCE↗

DOE OSTI2021

PYSEQM

PYSEQM is a package for performing semi-empirical quantum mechanical (SEQM) simulations on molecular systems utilizing PyTorch. SEQM simulations determine molecular properties (energy, electron density, dipole moment, ect.) by solving an approximate Schrödinger equation for the motions of electrons in a molecule. The use of PyTorch provides three specific advantages. First, it allows the calculations to be offloaded to GPU accelerators, giving an order of magnitude increase in speed. Second, back propagation is used to get atomic forces (derivative of the total energy with respect to atomic position) at the same computational cost as the energy calculation itself. Finally, the use of PyTorch makes for a natural interface to modern machine learning methods, which can be used to adjust the semi-empirical parameters build into SEQM methods. Additionally, PYSEQM implements various other optimizations for performing quantum mechanics based molecular dynamics, including SP2 for rapid GPU based solution of the self-consistent field algorithm, and the extended Lagrangian method for rapid QM-MD.

Nebgen, Benjamin↗

DOE OSTI2024

Synthesis and investigation into explosive sensitivity for a series of new picramide explosives

Tailoring the molecular properties that govern energetic material sensitivity is essential to improve safety and help develop new energetic materials. Despite this need, understanding the complex chemistry and physics of explosive initiation and propagation is still a challenge. Recent work by our group has reinforced the view that explosive sensitivity under sub-shock conditions is connected to the strength of the weakest covalent bond in the molecule, that is, its “trigger linkage.” These correlations have been observed with different classes of energetic molecules and indicate that “trigger linkage” bond breaking, and heat of explosion are good indicators for the sensitivity trends. Herein we report the synthesis of aliphatic energetic materials with ethane, propane and neopentane backbones. Experimental and computational studies show that the trigger linkage model, based on results from quantum molecular dynamics simulations, correctly predicts trends observed in the impact sensitivity of the molecules. However, while the model predicts the impact sensitivities of the ethane series, the neopentane series has higher impact sensitivities than predicted, which is presumably influenced by crystal packing effects.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

ACES-GNN: can graph neural network learn to explain activity cliffs?

Graph Neural Networks (GNNs) have revolutionized molecular property prediction by leveraging graph-based representations, yet their opaque decision-making processes hinder broader adoption in drug discovery. This study introduces the Activity-Cliff-Explanation-Supervised GNN (ACES-GNN) framework, designed to simultaneously improve predictive accuracy and interpretability by integrating explanation supervision for activity cliffs (ACs) into GNN training. ACs, defined by structurally similar molecules with significant potency differences, pose challenges for traditional models due to their reliance on shared structural features. By aligning model attributions with chemist-friendly interpretations, the ACES-GNN framework bridges the gap between prediction and explanation. Validated across 30 pharmacological targets, ACES-GNN consistently enhances both predictive accuracy and attribution quality for ACs compared to unsupervised GNNs. Our results demonstrate a positive correlation between improved predictions and accurate explanations, offering a robust and adaptable framework to better understand and interpret ACs. This work underscores the potential of explanation-guided learning to advance interpretable artificial intelligence in molecular modeling and drug discovery.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Ultrafast Imaging of Molecules with Electron Diffraction

Photoexcited molecules convert light into chemical and mechanical energy through changes in electronic and nuclear structure that take place on femtosecond timescales. Gas phase ultrafast electron diffraction (GUED) is an ideal tool to probe the nuclear geometry evolution of the molecules and complements spectroscopic methods that are mostly sensitive to the electronic state. GUED is a passive probing tool that does not alter the molecular properties during the probing process and is sensitive to the spatial distribution of charge in the molecule, including both electrons and nuclei. Improvements in temporal resolution have enabled GUED to capture coherent nuclear motions in molecules in the excited and ground electronic states with femtosecond and subangstrom resolution. Here we present the basic theory of GUED and explain what information is encoded in the diffraction signal, review how GUED has been used to observe coherent structural dynamics in recent experiments, and discuss the advantages and limitations of the method.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Data Generation for Machine Learning Interatomic Potentials and Beyond

The field of data-driven chemistry is undergoing an evolution, driven by innovations in machine learning models for predicting molecular properties and behavior. Recent strides in ML-based interatomic potentials have paved the way for accurate modeling of diverse chemical and structural properties at the atomic level. The key determinant defining MLIP reliability remains the quality of the training data. A paramount challenge lies in constructing training sets that capture specific domains in the vast chemical and structural space. This Review navigates the intricate landscape of essential components and integrity of training data that ensure the extensibility and transferability of the resulting models. We delve into the details of active learning, discussing its various facets and implementations. We outline different types of uncertainty quantification applied to atomistic data acquisition and the correlations between estimated uncertainty and true error. The role of atomistic data samplers in generating diverse and informative structures is highlighted. Furthermore, we discuss data acquisition via modified and surrogate potential energy surfaces as an innovative approach to diversify training data. The Review also provides a list of publicly available data sets that cover essential domains of chemical space.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Impact of Linker Engineering in Core‐Linked Dimeric Acceptors for High‐Performance Organic Solar Cells

Abstract The dimerization of small molecule acceptors (SMAs) is a promising strategy for enhancing the long‐term stability and power conversion efficiency (PCE) of organic solar cells (OSCs). However, the reported DSMAs are primarily limited to end‐linked molecular configurations, highlighting the need for further exploration of various dimer architectures. Herein, the development of two distinct core‐linked dimerized SMAs (DYF‐V and DYF‐E) are reported with tailored linker structures (vinylene and ethynyl, respectively), achieving high‐performance OSCs (PCE = 18.53%). Interestingly, a subtle change in the linker structures results in markedly different molecular properties and photovoltaic performances of the dimer acceptors. DYF‐E with an ethynyl linker exhibits more twisted backbone conformation and mitigated aggregation property compared to DYF‐V, inducing desirable blend morphologies with a polymer donor including high crystallinity, face‐on oriented packing structures, and well‐intermixed domains. Thus, the DYF‐E‐based OSCs exhibit a high PCE (17.02%), which significantly outperforms the DYF‐V‐based OSCs (PCE = 9.98%). Furthermore, the ternary OSCs based on DYF‐E achieve a higher PCE of 18.53%. Thus, this study highlights the significance of selecting an appropriate linker in core‐linked dimerized SMAs for producing high‐performance OSCs.

Oh, Shinbee↗

DOE OSTI2023

Unidirectional Sidechain Engineering to Construct Dual‐Asymmetric Acceptors for 19.23 % Efficiency Organic Solar Cells with Low Energy Loss and Efficient Charge Transfer

Abstract Achieving both high open‐circuit voltage ( V oc ) and short‐circuit current density ( J sc ) to boost power‐conversion efficiency (PCE) is a major challenge for organic solar cells (OSCs), wherein high energy loss ( E loss ) and inefficient charge transfer usually take place. Here, three new Y‐series acceptors of mono‐asymmetric asy‐YC11 and dual‐asymmetric bi‐asy‐YC9 and bi‐asy‐YC12 are developed. They share the same asymmetric D 1 AD 2 (D 1 =thieno[3,2‐ b ]thiophene and D 2 =selenopheno[3,2‐ b ]thiophene) fused‐core but have different unidirectional sidechain on D 1 side, allowing fine‐tuned molecular properties, such as intermolecular interaction, packing pattern, and crystallinity. Among the binary blends, the PM6 : bi‐asy‐YC12 one has better morphology with appropriate phase separation and higher order packing than the PM6 : asy‐YC9 and PM6 : bi‐asy‐YC11 ones. Therefore, the PM6 : bi‐asy‐YC12‐based OSCs offer a higher PCE of 17.16 % with both high V oc and J sc , due to the reduced E loss and efficient charge transfer properties. Inspired by the high V oc and strong NIR‐absorption, bi‐asy‐YC12 is introduced into efficient binary PM6 : L8‐BO to construct ternary OSCs. Thanks to the broadened absorption, optimized morphology, and furtherly minimized E loss , the PM6 : L8‐BO : bi‐asy‐YC12‐based OSCs achieve a champion PCE of 19.23 %, which is one of the highest efficiencies among these annealing‐free devices. Our developed unidirectional sidechain engineering for constructing bi‐asymmetric Y‐series acceptors provides an approach to boost PCE of OSCs.

Chemistry↗

DOE OSTI2025

A Route to Design Novel Functional Peptides by Applying a Denoising Diffusional Model to mRNA Display Libraries

In vitro directed evolution techniques, such as mRNA display, enable peptide ligand discovery and optimization. However, physical libraries that rely on a genetic code can only search a small fraction of sequence space due to inherent biases in the genetic code and experimental limitations. To address this challenge, denoising diffusion implicit models (DDIMs) are applied to generate novel peptide ligands against B‐cell lymphoma extra‐large (Bcl‐x L ), a key cancer target. Starting with high‐throughput sequencing data from previous selections, a DDIM is trained to produce novel sequences with high affinity binding. Experimental validation confirms that most generated sequences are functionally equivalent to the original library members for Bcl‐x L binding and demonstrated comparable binding kinetics and affinity relative to the wildtype and nearest original neighbors. Importantly, this approach generated rare sequences not easily accessible via mutation and directed evolution. These results indicate that DDIMs can complement and expand directed evolution data, efficiently exploring underrepresented regions of sequence space. This approach provides a broadly applicable framework for accelerating ligand discovery and optimizing molecular properties across diverse targets.

Qi, Pearl [Mork Family Department of Chemical Engi↗

DOE OSTI2021

Terephthalic Acid Copolyesters Containing Tetramethylcyclobutanediol for High‐Performance Plastics

Abstract There is a need for high‐performance applications for terephthalic acid (TPA) polyesters with high heat resistance, impact toughness, and optical clarity. Bisphenol A (BPA) based polycarbonates and polyarylates have such properties, but BPA is an endocrine disruptor. Therefore, new TPA polyesters that are less hazardous to health and the environment are becoming popular. Tetramethylcyclobutanediol (TMCD) is a difunctional monomer that can be polymerized with TPA and other diols to yield copolyesters with superior properties to conventional TPA polyesters. It has a cyclobutyl ring that makes it more rigid than cyclohexanedimethanol (CHDM) and EG. Thus, TMCD containing TPA copolyesters can have high heat resistance and impact strength. TPA can be made from abundantly available upcycled polyethylene terephthalate (PET). Therefore, this review discusses the synthesis of monomers and copolyesters, the impact of diol composition on material properties, molecular weight, effects of photodegradation, health safety, and substitution of cyclobutane diols for future polyesters.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Specific molecular design of polycarboxylate polymers exhibiting optimal compatibility with clay contaminants in concrete

Highlights: • Allyl ether-based PCEs with short side chain length and low side chain density presented superior clay resistance. • PCEs with medium side chain density exhibited best clay robustness when compared at the absolute dosed quantities. • The proportion of PCE being intercalated into the layered structure of clay was quantified. It is widely recognized that the dispersing ability of polycarboxylate superplasticizers (PCEs) could be hindered due to the presence of clay contaminants in concrete. In this study, a series of allyl ether-based polycarboxylate superplasticizers possessing short polyethylene glycol side chains was successfully synthesized and probed for their clay tolerance. The resulting PCE polymers were characterized via Size Exclusion Chromatography (SEC) to obtain their molecular properties. Thereafter, their dispersing ability was probed in the absence and presence of sodium bentonite. Allyl ether-based polycarboxylate (APEG) polymers possessing short side chains were found to exhibit enhanced clay resistance as compared to that of conventional MPEG PCEs holding long pendant chains. The mode of interaction between APEG PCEs and bentonite was investigated via sorption and XRD measurements. The data revealed that APEG PCEs possessing a lower side chain density intercalate less into the interlayer space of bentonite than those exhibiting higher side chain density.

36 MATERIALS SCIENCE↗

DOE OSTI2026

From PAH to fullerenes: Closing a nano bowl with a nano lid

Quantum chemical DLPNO-CCSD(T)/cc-pVDZ//B3LYP/6–31G(d) calculations have been performed to unravel the formation mechanism of buckminsterfullerene (C 60 ) via the reaction of the C 40 nano bowl (C 40 H 10 ) and corannulene (C 20 H 10 ). The generated potential energy surfaces and molecular properties were further utilized to evaluate equilibrium constants and rate constants of elementary chemical reactions involved in the C 60 synthesis. The overall C 40 H 10 + C 20 H 10 → C 60 + 10H 2 reaction is shown to be highly exoergic and hence thermodynamically favorable at temperatures above 700 K and a kinetically feasible pathway under high-temperature conditions was revealed. This route involves hydrogen atom abstraction steps to activate closed-shell reactants/intermediates alternating with cyclodehydrogenation reactions eventually zipping the edges between the reacting C 40 and C 20 units by consecutive closures of six- and five-membered rings between them. The reaction is driven/catalyzed by hydrogen atoms which carry out the hydrogen abstractions from C 60 H x and are later recovered on the cyclodehydrogenation stages. The initial association reaction, C 40 H 9 + C 20 H 10 → C 60 H 18 + H, linking the two units by a covalent C-C bond appears to represent the kinetic bottleneck of the whole multistep process. This reaction is fast at low temperatures but slows down and has its equilibrium shifted toward the reactants at high temperatures. Here, the feasibility of the proposed mechanism corroborates the hypothesis that C 60 can be produced through a series of elementary reactions typical for the PAH growth.

Equilibrium constant↗

DOE OSTI2024

Ligand Many-Body Expansion as a General Approach for Accelerating Transition Metal Complex Discovery

Methods that accelerate the evaluation of molecular properties are essential for chemical discovery. While some degree of ligand additivity has been established for transition metal complexes, it is underutilized in asymmetric complexes, such as the square pyramidal coordination geometries highly relevant to catalysis. To develop predictive methods beyond simple additivity, we apply a many-body expansion to octahedral and square pyramidal complexes and introduce a correction based on adjacent ligands (i.e., the cis interaction model). We first test the cis interaction model on adiabatic spin-splitting energies of octahedral Fe(II) complexes, predicting DFT-calculated values of unseen binary complexes to within an average of 1.4 kcal/mol. Uncertainty analysis reveals the optimal basis, comprising the homoleptic and mer symmetric complexes. We next show that the cis model (i.e., the cis interaction model solved for the optimal basis) infers both DFT- and CCSD(T)-calculated model catalytic reaction energies to within 1 kcal/mol on average. The cis model predicts low-symmetry complexes with reaction energies outside the range of binary complex reaction energies. We observe that trans interactions are unnecessary for most monodentate systems but can be important for some combinations of ligands, such as complexes containing a mixture of bidentate and monodentate ligands. Lastly, we demonstrate that the cis model may be combined with Δ-learning to predict CCSD(T) reaction energies from exhaustively calculated DFT reaction energies and the same fraction of CCSD(T) reaction energies needed for the cis model, achieving around 30% of the error from using the CCSD(T) reaction energies in the cis model alone.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Zero-Field Splitting Calculations by Multiconfiguration Pair-Density Functional Theory

Zero-field splitting (ZFS) is a fundamental molecular property that is especially relevant for single-molecule magnets (SMMs), electron paramagnetic resonance spectra, and quantum computing. Developing a method that can accurately predict ZFS parameters can be very powerful for designing new SMMs. One of the challenges is to include external correlation in an inherently multiconfigurational open-shell species for the accurate prediction of magnetic properties. Previously available methods depend on expensive multireference perturbation theory calculations to include external correlation. In this paper, we present spin-orbit-inclusive multiconfiguration and multistate pair-density functional theory (MC-PDFT) calculations of ZFSs; these calculations have a cost comparable to complete-active-space self-consistent field (CASSCF) theory, but they include correlation external to the active space. We found that combining a multistate formulation of MC-PDFT, namely, compressed-state multistate pair-density functional theory, with orbitals optimized by weighted-state-averaged CASSCF, yields reasonably accurate ZFS results

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

How Does HF-DFT Achieve Chemical Accuracy for Water Clusters?

Bolstered by recent calculations of exact functional-driven errors (FEs) and density-driven errors (DEs) of semilocal density functionals in the water dimer binding energy , we investigate approximate FEs and DEs in neutral water clusters containing up to 20 monomers, charged water clusters, and alkali- and halide-water clusters. Our proxy for the exact density is r 2 SCAN 50, a 50% global hybrid of exact exchange with r 2 SCAN, which may be less correct than r 2 SCAN for the compact water monomer but importantly more correct for long-range electron transfers in the noncompact water clusters. We show that SCAN makes substantially larger FEs for neutral water clusters than r 2 SCAN, while both make essentially the same DEs. Unlike the case for barrier heights, these FEs are small in a relative sense and become large in an absolute sense only due to an increase in cluster size. SCAN@HF, short for SCAN evaluated on the Hartree–Fock (HF) density, produces a cancellation of errors that makes it chemically accurate for predicting the absolute binding energies of water clusters. Likewise, adding a long-range dispersion correction to r 2 SCAN@HF, as in the composite method HF-r 2 SCAN-DC4, makes its FE more negative than in r 2 SCAN@HF, permitting a near-perfect cancellation of FE and DE. r 2 SCAN by itself (and even more so, r 2 SCAN evaluated on the r 2 SCAN 50 density), is almost perfect for the energy differences between water hexamers, and thus probably also for liquid water away from the boiling point. Thus, the accuracy of composite methods like SCAN@HF and HF-r 2 SCAN-DC4 is not due to the HF density being closer to the exact density, but to a compensation of errors from its greater degree of localization. We also give an argument for the approximate reliability of this unconventional error cancellation for diverse molecular properties. Lastly, we confirm this unconventional error cancellation for the SCAN description of the water trimer via Kohn–Sham inversion of the CCSD(T) density.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗