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DOE OSTI2023

Deep learning workflow for the inverse design of molecules with specific optoelectronic properties

The inverse design of novel molecules with a desirable optoelectronic property requires consideration of the vast chemical spaces associated with varying chemical composition and molecular size. First principles-based property predictions have become increasingly helpful for assisting the selection of promising candidate chemical species for subsequent experimental validation. However, a brute-force computational screening of the entire chemical space is decidedly impossible. To alleviate the computational burden and accelerate rational molecular design, we here present an iterative deep learning workflow that combines (i) the density-functional tight-binding method for dynamic generation of property training data, (ii) a graph convolutional neural network surrogate model for rapid and reliable predictions of chemical and physical properties, and (iii) a masked language model. As proof of principle, we employ our workflow in the iterative generation of novel molecules with a target energy gap between the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO).

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Alignment switching in 3D-printed smectic liquid crystal elastomers

Extrusion-based additive manufacturing has emerged as a powerful platform for designing shape-morphing materials through controlled orientation. However, existing approaches primarily rely on a single mode of flow-induced alignment, limiting orientation programmability. Herein, we present a direct-ink-writing approach for smectic liquid crystal elastics that exploits two distinct alignment modes within a single ink. The smectic ink exhibits shear- and temperature-dependent orientation switching, enabling molecular alignment either perpendicular or parallel to the print direction. Combined rheological, X-ray, and molecular dynamics analyses reveal that this alignment inversion arises from the preservation or collapse of smectic layers under flow. This reversible switching encodes both contractile and elongational actuation within individual filaments, greatly expanding the design freedom of printed liquid crystal elastomers. We demonstrate 2D and 3D structures with diverse programmed shape transformations, highlighting the potential of this platform for adaptive soft actuators and architected functional materials.

Lee, Jin Hyeong [Pusan National University, Busan,↗

DOE OSTI2021

Variational design principles for nonequilibrium colloidal assembly

Using large deviation theory and principles of stochastic optimal control, we show that rare molecular dynamics trajectories conditioned on assembling a specific target structure encode a set of interactions and external forces that lead to enhanced stability of that structure. Such a relationship can be formulated into a variational principle, for which we have developed an associated optimization algorithm and have used it to determine optimal forces for targeted self-assembly within nonequilibrium steady-states. We illustrate this perspective on inverse design in a model of colloidal cluster assembly within linear shear flow. We find that colloidal clusters can be assembled with high yield using specific short-range interactions of tunable complexity. Shear decreases the yields of rigid clusters, while small values of shear increase the yields of nonrigid clusters. The enhancement or suppression of the yield due to shear is rationalized with a generalized linear response theory. Furthermore, by studying 21 unique clusters made of six, seven, or eight particles, we uncover basic design principles for targeted assembly out of equilibrium.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Structural Mechanism of an Efficacy Photoswitch Targeting the β 2 ‐adrenergic Receptor

The field of photopharmacology develops light-responsive drugs that can modulate protein activity, enabling precise and dynamic investigations of their roles in health and disease. Adrenergic receptors are prominent targets for this approach because they are prototypical G protein-coupled receptors with high clinical relevance in bronchial and cardiovascular diseases. Here, we employed the azobenzene-based compound photoazolol-1 in combination with time-resolved serial crystallography at X-ray free-electron lasers to resolve the molecular mechanisms by which photoswitchable β-blockers modulate activity of the β 2 -adrenoceptor (β 2 AR). Time-resolved structures of the receptor bound to trans-photoazolol-1 (pre-photoconversion), a strained intermediate in the nanosecond range, and the fully photoisomerized cis-photoazolol-1 reveal how isomerization of the azobenzene moiety induces distinct conformational changes within the orthosteric ligand binding pocket. Within seconds, light-excited photoazolol-1 adopts a new binding pose, altering interactions with extracellular loop 2 and shifting the positions of transmembrane helices 5, 6, and 7. Functional assays of β 2 AR in cellular membranes show that photoazolol-1 acts as an efficacy photoswitch, changing from an inverse agonist to a neutral antagonist upon isomerization without leaving the binding pocket. In combination, these findings suggest a molecular mechanism for activity modulation via efficacy photoswitches and provide a framework for designing ligands that exploit light-driven transitions within the binding pocket to achieve spatiotemporal control of receptor function.

G protein-coupled receptors↗

DOE OSTI2022

End-to-End Optimization for Battery Materials and Molecules by Combining Graph Neural Networks and Reinforcement Learning

The National Renewable Energy Laboratory (NREL), together with the Colorado School of Mines (CSM) and Colorado State University (CSU), has developed a machine learning-enhanced approach to the design of new battery materials. Currently, such materials are designed in part via numerous expensive high-fidelity computational simulations that predict the performance of a given composition. Even with computational screening tools, the vast landscape of possible molecular or crystal structures exceeds current and future computational capacity. Improving the efficiency by which new materials can be optimized will therefore disrupt the cost, risk, and time required to bring new energy solutions to the marketplace. Predicting the properties of an organic molecule or periodic crystalline material given its structure has grown increasingly common. These approaches leverage large-scale computational and experimental databases and ML approaches such as graph neural networks. The inverse design problem of finding a material that possesses desired properties is substantially more challenging, since enumerating all valid material structures is not feasible. In this project, we leveraged recent success in reinforcement learning to efficiently navigate this high-dimensional search space. Just as algorithms can find the optimal chess moves from nearly limitless options, we train an approach to evolve a simple starting structure into a complex structure that possess the desired properties. Our solution has been demonstrated by applying it to two related design application tasks for short- and long-term energy storage, respectively: (1) the design of solid-state ion conductors and (2) the design of organic redox-active materials. The project has resulted an open-source software library for material design, documented examples of applying the library to both organic and inorganic material optimization, and peer-reviewed publications detailing the data, computational models, and resulting candidate materials.

25 ENERGY STORAGE↗

DOE OSTI2021

Core-shell and egg-shell zeolite catalysts for enhanced hydrocarbon processing

Developing structure-performance relationships with the underlying goal of optimizing known zeolite catalysts involves the manipulation of their physicochemical properties. Here, we systematically assessed the impact of mesoscopic gradients in acid site concentration, which has generally received little attention in the design of zeolite catalysts for hydrocarbon upgrading. A series of core–shell MEL-type zeolites were synthesized with catalytically active ZSM-11 cores and passivated silicalite-2 shells of varying thickness. Our findings revealed that ZSM-11@silicalite-2 particles with ultrathin shells (<10 nm) have enhanced mass transport, characteristic of relatively smaller particles, compared to the corresponding ZSM-11 core. Additionally, catalytic testing using the methanol-to-hydrocarbon (MTH) reaction showed that core–shell zeolites exhibit longer lifetimes, higher total turnovers, and an unexpected promotion of the aromatic cycle in the hydrocarbon pool mechanism. Time-resolved acid titration of core and core–shell catalysts confirmed that the siliceous shell introduces a hydrophobic exterior that impacts molecular diffusion. In comparison, prepared MFI core-shells (ZSM-5@silicalite-1) showed similar enhancement in catalyst performance. Moreover, we prepared egg-shell configurations of each zeolite, silicalite-2@ZSM-11 and silicalite-1@ZSM-5, comprised of an inert core and catalytically active shell. This inverse design of the egg-shell created pseudo nanosheets with total turnovers that were markedly higher than their homogeneous counterparts. Collectively, this study demonstrated that mesoscopic gradients in acid concentration via the design of core–shell and egg-shell zeolites significantly improve catalyst performance over conventional analogues for hydrocarbon upgrading.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Structural and compositional complexities of hierarchical self-assembly: A hypergraph approach

Programmable self-assembly enables the construction of complex molecular, supramolecular, and crystalline architectures from well-designed building blocks. In this work, we introduce a hypergraph-based formalism, Blocks & Bonds (B&B), which generalizes classical chemical graph theory by incorporating directed and multicolored interactions, internal symmetries, and hierarchical organization. Within this framework, we develop the Structure Code (SC), a compact and versatile language for describing self-assembled architectures. We define a Kolmogorov-style structural complexity as the total information content of SC, obtained through its tokenization and Shannon information assignment. Complementing this encoding-based measure, we introduce a much simpler quantity, the compositional complexity, which depends only on the number and cumulative usage of block and bond types in the construction set. A central result of this work is a strong empirical correlation between the token-based structural complexity and the compositional complexity across all examined systems. Owing to this agreement, the compositional complexity emerges as the most practical and broadly applicable measure: it is easy to compute, requires no explicit encoding, and yet closely tracks the actual information content of structurally diverse architectures. Applications to molecular systems (ethylene glycol and glucose), DNA-origami lattices, and crystalline assemblies show that B&B hypergraphs provide a unified, scalable, and information-efficient representation of structural organization, naturally capturing symmetry, modularity, and stereochemistry. This framework establishes a quantitative foundation for complexity-aware classification and inverse design of programmable matter.

36 MATERIALS SCIENCE↗

DOE OSTI2022

End-to-end optimization for battery materials and molecules by combining graph neural networks and reinforcement learning

The National Renewable Energy Laboratory (NREL), together with the Colorado School of Mines (CSM) and Colorado State University (CSU), has developed a machine learning-enhanced approach to design new battery materials. Currently, such materials are designed in part via numerous expensive high-fidelity computational simulations that predict the performance of a given composition. Even with computational screening tools, the vast landscape of possible molecular or crystal structures exceeds current and future computational capacity. Improving the efficiency by which new materials can be optimized will therefore disrupt the cost, risk, and time required to bring new energy solutions to the marketplace. Predicting the properties of an organic molecule or periodic crystalline material given its structure has grown increasingly common. These approaches leverage large-scale computational and experimental databases and ML approaches such as graph neural networks. The inverse design problem of finding a material that possesses desired properties is substantially more challenging, since enumerating all valid material structures is not feasible. In this project, we leveraged recent success in reinforcement learning to efficiently navigate this high-dimensional search space. Just as algorithms can find the optimal chess moves from nearly limitless options, we train an approach to evolve a simple starting structure into a complex structure that possess the desired properties. Our solution has been demonstrated by applying it to two related design application tasks for short- and long-term energy storage, respectively: (1) the design of solid-state ion conductors and (2) the design of organic redox-active materials. The project has resulted an open-source software library for material design, documented examples of applying the library to both organic and inorganic material optimization, and peer-reviewed publications detailing the data, computational models, and resulting candidate materials.

25 ENERGY STORAGE↗

DOE OSTI2024

Engineering tertiary chirality in helical biopolymers

Tertiary chirality describes the handedness of supramolecular assemblies and relies not only on the primary and secondary structures of the building blocks but also on topological driving forces that have been sparsely characterized. Helical biopolymers, especially DNA, have been extensively investigated as they possess intrinsic chirality that determines the optical, mechanical, and physical properties of the ensuing material. Here, we employ the DNA tensegrity triangle as a model system to locate the tipping points in chirality inversion at the tertiary level by X-ray diffraction. We engineer tensegrity triangle crystals with incremental rotational steps between immobile junctions from 3 to 28 base pairs (bp). We construct a mathematical model that accurately predicts and explains the molecular configurations in both this work and previous studies. Our design framework is extendable to other supramolecular assemblies of helical biopolymers and can be used in the design of chiral nanomaterials, optically active molecules, and mesoporous frameworks, all of which are of interest to physical, biological, and chemical nanoscience.

Science & Technology - Other Topics↗

DOE OSTI2025

Inverse Trans Influence and Uranium-Arene σ-Bonding Drive Molecular Geometry: Ligand Modification from Hard to Soft Flips the Oxide from Axial to Equatorial

A rare example of an equatorially bound terminal uranium(V) oxo complex in a chelating sulfur-based ligand environment, namely [(mes( Me,Ad ArS) 3 )U V (O eq )(THF)] (2), is presented. Octahedrally coordinated 2 is obtained by reaction of the mesitylene-anchored tris-thiophenolate-coordinated uranium(III) complex [U III ((SAr Ad,Me ) 3 mes)] (1) with the oxygen-atom transfer reagent N 2 O. The observed, equatorially bound oxo ligand in 2 is in stark contrast to its known tris-aryloxide analog, [(mes( Me,Ad ArO) 3 )U V (O ax )(THF)] (A), where the oxo ligand occupies the typically observed axial coordination site. Complexes 1 and 2 are characterized by single-crystal X-ray diffraction analyses and spectroscopic and magnetochemical methods, including 1 H NMR, UV/vis/NIR electronic absorption, as well as EPR spectroscopy and SQUID magnetometry, thus confirming the C S symmetry and the pentavalent oxidation state of 2. Encompassing quantum chemical calculations (DFT and CASPT2) on 2 and its tris-phenolate analog A, support and rationalize the structural and electronic differences. The molecular orbital pictures show that a stabilizing σ-bonding interaction arising from the U–O eq inverse trans influence (ITI) is present in 2 but missing in A. In 2, the sulfur 3p orbitals are closer in energy to the uranium 5f manifold than the arene π-system, leading to an ITI, while U–arene σ- or δ-bonding is not observed. Although the arene orbitals remain separated from the uranium 5f orbitals in A, the absence of an ITI allows the arene a 2u orbital to engage in a σ-type interaction with the metal. Thus, incorporating a tris-thiophenolate to an arene anchor introduces a new design concept in molecular f-element chemistry. This approach stabilizes an equatorially bound U(V) oxo center, contrasting with its tris-phenolate counterpart, where oxo coordination is axial. The observed geometric divergence, driven by competing ITI and U–arene interactions, not only tunes electronic structure but also leads to differentiated reactivity: only the phenolate analogs activate H 2 O, while the thiolates do not.

Hydrocarbons↗

DOE OSTI2026

Interfacial Inversion of Stealth Surfactants

Amphiphilic macromolecular surfactants segregate to liquid–liquid interfaces, thereby reducing the interfacial tension and free energy. Here, we investigated “stealth surfactants” in the form of core–shell bottlebrush polymers comprised of pH-responsive diblock copolymer side chains forming a hydrophilic core and a hydrophobic shell, enabling solubility in oil. At liquid–liquid interfaces, these polymers undergo a structural “inversion”, with hydrophilic blocks segregating into the aqueous phase and hydrophobic blocks residing in the oil phase. The reconfiguration kinetics and surfactant properties are influenced by multiple factors, including the molecular weights of the backbone and side chain components, the hydrophilic-to-hydrophobic balance of the side chains, and the pH of the aqueous phase. An observed nonmonotonic dependence of interfacial tension with time is attributed to a progressive structural inversion, where the projected area of the macromolecule onto the interface decreases. To validate this inversion hypothesis, interfacial properties were characterized by sum-frequency generation vibrational spectroscopy, which revealed configurational changes of the core–shell bottlebrush polymers at the fluid interface and revealed a pH-dependent interfacial coverage. Coarse-grained molecular dynamics simulations supported these experimental findings, showing that the pH-responsive core and hydrophobic shell assume a time-averaged configuration with orientations parallel and perpendicular to the plane of the interface, respectively. These findings open routes to design multistimuli-responsive polymeric surfactants and compatibilizers, expanding their potential applications in advanced interfacial systems.

Stealth surfactants↗

DOE OSTI2024

Machine learning-based discovery of molecular descriptors that control polymer gas permeation

While machine learning has found increasing use in predicting the properties of polymeric materials with only a knowledge of chain architecture, determining the molecular factors underpinning properties (“interpretable AI”) has remained less well explored. We show that encoding chain chemistry in commonly employed formats, e.g., binary-valued fingerprints, leads to uniqueness issues during the hashing process to save storage space. This is because the hashing algorithm can map several chemical moieties into the same bit. These issues carry over into the ML algorithms, especially for “inverse” design and interpretable AI, and cannot be avoided by changing the length of the fingerprint. Using MACCS key featurizations of monomer repeats resolves some of these issues, and we show that a few substructures consistently appear in top features for maximizing permeability across several gases and ML models. These are carbon–carbon double bonds (as in polyacetylenes) especially when they are associated with methyl groups (found in branching architectures). Here these results, derived from the limited data set of ~ 500 polymers with experimental gas permeation data, are in agreement with physical insight and thus provide a robust foundation which could further enable study of these material classes through detailed experiments and simulations.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Targeting Oxide Concentration in Tantalum Oxide-Fluoride Anions

The design of both molecular and non-molecular solid materials with specific properties fundamentally relies on the controlled synthesis of crystals with desired functional groups, bonding motifs, polarity, chirality, and more. To this end, fluoride and oxide-fluoride anions have been utilized as basic building units (BBUs) in the synthesis of noncentrosymmetric racemic materials for their ability to create polar axes that facilitate the breaking of an inversion center as demonstrated in a series of compounds with [MF6]2- anions (M = Ti, Zr, Hf). Targeting an analog with a [TaOF5]2- anion, the phase space of (CuO, Ta2O5)/bpy/HF(aq)/H2O (bpy = 2,2′-bipyridine) was investigated and three new compounds with Cu-bpy cations and Ta-fluoride or Ta-oxyfluoride anions were synthesized: [Cu(bpy)2][TaF6], [Cu(bpy)2][Ta2OF10], and [Cu(bpy)F(H2O)2]2[TaF7]∙H2O with the anions [TaF6]-, [Ta2OF10]2-, and [TaF7]2-, respectively. The formation of these anions was found to be a product of both the concentration of hydrofluoric acid in solution and the ratio of metal-oxide starting materials to ligand. This work contributes to the understanding of mixed anion formation in the solid state.

Kamp, Kendall R.↗

DOE OSTI2025

Inverse mapping of properties to composition through generative modeling for designing molten salts

Generative modeling (GM) has been increasingly used for the inverse design and optimization of materials, yet its application to molten salt mixtures remains unexplored despite how a successful approach to the inverse design of molten salts would contribute to efficiently exploiting their customizability and unlocking their advantages in applications, such as energy production and energy storage. This work presents a workflow for the inverse design of molten salts with targeted density values, addressing the challenge of representing these complex mixtures in GM. A dataset of critically evaluated molten salt densities is used to train a variational autoencoder coupled with a predictive deep neural network, which then can be used to generate new molten salt compositions with desired density values. The effectiveness of the approach is demonstrated by designing mixtures with distinct densities and validating the predicted values using ab initio molecular dynamics simulations.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2024

Understanding the Origin of Negative Temperature Dependence and Activity of N-Coordinated Cobalt Sites During Ethylene Dimerization

The on-demand production of short-chain linear alpha olefins (LAOs; C4-C8) via C2H4 dimerization and oligomerization is industrially attractive, prompting extensive research on designing active, selective, and stable catalysts for industrial use. Cobalt supported on ammoniated carbon (Co(NH3)x/C) catalysts have shown remarkable activity and selectivity in this process. However, critical aspects such as the active phase, active site structure, the role of the catalyst support, cobalt loading effects, and the inverse correlation of the reaction rate with temperature remain inadequately understood. This study systematically explores these factors using a combination of steady-state differential catalytic tests, in situ molecular characterization including diffuse reflectance UV-Vis (DR-UV-Vis), Infrared, and Raman spectroscopies, and ex situ X-ray diffraction (XRD) and high annular aberration-corrected dark field transmission electron microscopy (HAADF-STEM). Various supports (SiO2, Al2O3, NH4-ZSM-5, g-C3N4, and C) and cobalt loadings (1.0-3.0 Co nm-2) were studied to determine the optimal catalyst composition and identify the active phase and sites. Carbon-supported catalysts uniquely produce C4-8 LAOs during C2H4 dimerization, with site-time-yield remaining constant (~10-3 s-1) for 1.0-4.0 Co nm-2 at prolonged reaction times (24-48?h time-on-stream). At higher loadings of 6.0 Co nm-2, the formation of crystalline CoO and Co3O4 phases reduces catalytic activity and LAO selectivity. Our findings show that active catalysts lack crystalline cobalt oxides and instead feature dispersed Co2+ sites, tetra-coordinated to a mix of N/NH3 and O/H2O ligands, which catalyze C2H4 dimerization via the Cossee-Arlman mechanism, exhibiting 1st order dependence on C2H4 concentration. The observed inverse rate-temperature correlation is attributed to compensation effects (i.e., presence of Cremer-Constable relationship) linked to changes in adsorption enthalpic and entropic factors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

LuGo: An enhanced quantum phase estimation implementation

Quantum Phase Estimation (QPE) is a cardinal algorithm in quantum computing that plays a crucial role in various applications, including cryptography, molecular simulation, and solving systems of linear equations. However, the standard implementation of QPE faces challenges related to time complexity and circuit depth, which limit its practicality for large-scale computations. We introduce LuGo, a novel framework designed to enhance the performance of QPE by reducing circuit duplication, as well as using parallelization techniques to achieve faster generation of the QPE circuit and gate reduction. We validate the effectiveness of our framework by generating quantum linear solver circuits, which require both QPE and inverse QPE, to solve linear systems of equations. LuGo achieves significant improvements in both computational efficiency and hardware requirements without compromising on accuracy. Compared to a standard QPE implementation, LuGo reduces time consumption to generate a circuit that solves a 2 6 × 2 6 system matrix by a factor of 50.68 and over 31× reduction of quantum gates and circuit depth, with no fidelity loss on an ideal quantum simulator. Furthermore, we demonstrated the versatility and scalability of LuGo enabled HHL algorithm by simulating a canonical Hele-Shaw fluid problem using a quantum simulator. With these advantages, LuGo paves the way for more efficient implementations of QPE, enabling broader applications across several quantum computing domains.

Quantum algorithm↗

DOE OSTI2023

Electrolyte Engineering Stabilizes Photoanodes Decorated with Molecular Catalysts

Molecular catalysts are promising oxygen evolution promoters in conjunction with photoanodes for solar water splitting. Maintaining the stability of both photoabsorber and cocatalyst is still a prime challenge, with many efforts tackling this issue through sophisticated material designs. Such approaches often mask the importance of the electrode-electrolyte interface and overlook easily tunable system parameters, such as the electrolyte environment, to improve efficiency. We provide a systematic study on the activity-stability relationship of a prominent Fe 2 O 3 photoanode modified with Ir molecular catalysts using in situ mass spectroscopy. After gaining detailed insights into the dissolution behavior of the Ir cocatalyst, a comprehensive pH study is conducted to probe the impact of the electrolyte on the performance. An inverse trend in Fe and Ir stability is found, with the best activity-stability synergy obtained at pH 9.7. The results bring awareness to the overall photostability and electrolyte engineering when advancing catalysts for solar water splitting.

14 SOLAR ENERGY↗

DOE OSTI2024

On the engulfment of antifreeze proteins by ice

Antifreeze proteins (AFPs) are remarkable biomolecules that suppress ice formation at trace concentrations. To inhibit ice growth, AFPs must not only bind to ice crystals, but also resist engulfment by ice. The highest supercooling, ΔT*, for which AFPs are able to resist engulfment is widely believed to scale as the inverse of the separation, L, between bound AFPs, whereas its dependence on the molecular characteristics of the AFP remains poorly understood. By using specialized molecular simulations and interfacial thermodynamics, here, we show that in contrast with conventional wisdom, ΔT* scales as L –2 and not as L –1 . We further show that ΔT* is proportional to AFP size and that diverse naturally occurring AFPs are optimal at resisting engulfment by ice. By facilitating the development of AFP structure–function relationships, we hope that our findings will pave the way for the rational design of AFPs.

Science & Technology - Other Topics↗