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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Chemical Design of Spin Frustration to Realize Topological Spin Glasses

Patterning spins to generate collective behavior is at the core of condensed matter physics. Physicists develop techniques, including the fabrication of magnetic nanostructures and precision layering of materials specifically to engender frustrated lattices. As chemists, we can access such exotic materials through targeted chemical synthesis and create new lattice types by chemical design. Here, in this study, we introduce a new approach to induce magnetic frustration on a modified honeycomb lattice through a competition of alternating antiferromagnetic (AFM) and ferromagnetic (FM) nearest-neighbor interactions. By subtly modulating these two types of interactions through facile synthetic modifications, we created two systems: (1) a topological spin glass and (2) a frustrated spin-canted magnet with low-temperature exchange bias. To design this unconventional magnetic lattice, we used a metal–organic framework (MOF) platform, Ni 3 (pymca) 3 X 3 (NipymcaX where pymca = pyrimidine-2-carboxylato and X = Cl, Br). We isolated two MOFs, NipymcaCl and NipymcaBr, featuring canted Ni 2+ -based moments. Despite this similarity, differences in the single-ion anisotropies of the Ni 2+ spins result in distinct magnetic properties for each material. NipymcaCl is a topological spin glass, while NipymcaBr is a rare frustrated magnet with low-temperature exchange bias. Density functional theory calculations and Monte Carlo simulations on the NipymcaX lattice support the presence of magnetic frustration as a result of alternating AFM and FM interactions. Our calculations enabled us to determine the ground-state spin configuration and the distribution of spin–spin correlations relative to paradigmatic kagomé and triangular lattices. This modified honeycomb lattice is similar to the electronic Kekulé-O phase in graphene and provides a highly tunable platform to realize unconventional spin physics.

Exchange bias↗
DOE OSTI2023

Chemical Design of Pb-Free Relaxors for Giant Capacitive Energy Storage

Dielectric capacitors have captured substantial attention for advanced electrical and electronic systems. Developing dielectrics with high energy density and high storage efficiency is challenging owing to the high compositional diversity and the lack of general guidelines. Herein, we propose a map that captures the structural distortion ($\delta$) and tolerance factor (t) of perovskites to design Pb-free relaxors with extremely high capacitive energy storage. Our map shows how to select ferroelectric with large $\delta$ and paraelectric components to form relaxors with a t value close to 1 and thus obtaining eliminated hysteresis and large polarization under a high electric breakdown. Taking the Bi 0.5 Na 0.5 TiO 3 -based solid solution as an example, we demonstrate that composition-driven predominant order-disorder characteristic of local atomic polar displacements endows the relaxor with a slushlike structure and strong local polar fluctuations at several nanoscale. This leads to a giant recoverable energy density of 13.6 J cm -3 , along with an ultrahigh efficiency of 94%, which is far beyond the current performance boundary reported in Pb-free bulk ceramics. In conclusion, our work provides a solution through rational chemical design for obtaining Pb-free relaxors with outstanding energy-storage properties.

25 ENERGY STORAGE↗
DOE OSTI2022

Chemical design of self-propelled Janus droplets

Solubilizing, self-propelling droplets have emerged as a rich chemical platform for the exploration of active matter, but isotropic droplets rely on spontaneous symmetry breaking to sustain motion. The introduction of permanent asymmetry, e.g., in the form of a biphasic Janus droplet, has not been explored as a comprehensive design strategy for active droplets, despite the widespread use of Janus structures in motile solid particles. Here, we uncover the chemomechanical framework underlying the self-propulsion of biphasic Janus oil droplets solubilizing in aqueous surfactant. We elucidate how droplet propulsion is influenced by the degree of oil mixing, droplet shape, and oil solubilization rates for a range of oil combinations. In addition, spatiotemporal control over droplet swimming speed and orientation is demonstrated through the application of thermal gradients applied via joule heating and laser illumination. Finally, we also explore the interactions between collections of Janus droplets, including the spontaneous formation of spinning multi-droplet clusters.

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

Chemical Design of Organic Interface Modifiers for Highly Efficient and Stable Perovskite Solar Cells

Perovskite solar cells (PSCs) have demonstrated rapid progress in their power conversion efficiencies (PCEs) - from 3.8% in 2009 to 25.7% in 2022 - and they have received considerable attention as a promising future photovoltaic (PV) technology. However, the operational stability of PSCs is still inadequate to satisfy the standards for commercial applications. Interface engineering has become one of the most important strategies to push PSCs' efficiency and stability for practical use. Among the various interface engineering approaches, organic interface modifiers (OIMs) have been frequently used by the PSC field to address the issues limiting PSC stability at high efficiency levels. In this perspective, the chemical structures of state-of-the-art OIMs are discussed, and their characteristics are reviewed, as well as the impact on device performance associated with key device interfaces (e.g., metal oxide/perovskite and organic transport layer/perovskite interfaces) from a chemical and materials engineering point of view is discussed. Finally, design considerations and the authors' perspective are discussed, on the basis of representative literature examples, for building new, customized organic OIMs to further improve PSC efficiency and stability toward commercialization.

14 SOLAR ENERGY↗
DOE OSTI2026

Creating and Interfacing Designer Chemical Qubits (Final Technical Report)

The Final Technical Report describes a multi‑institution effort to develop programmable molecular qubits as precision quantum sensors for probing quantum materials. The team created chemically tunable qubits with optical addressability and practical coherence, integrated them into thin films and frameworks while preserving functionality, and established new magnetic and electric sensing methods suited to two‑dimensional magnets, ferroelectrics, and multiferroics. They also built computational models and spectroscopic tools that connect molecular design to material behavior, enabling access to quantum phenomena that previously could not be measured. The project produced more than 40 publications and trained a large cohort of graduate students and postdocs, strengthening the workforce and infrastructure needed for DOE's quantum information science mission.

2D Quantum Materials↗
DOE OSTI2026

Simplifying the Chemical Design of Nonfused-Ring Electron Acceptors─Lessons Learned from Thienothiophene and Benzodithiophene Cores

Nonfused-ring electron acceptors (NFREA)s have been proposed as alternatives to fused-ring acceptors in organic photovoltaics (OPV)s. The simpler molecular design of NFREAs results in a reduced synthetic complexity, potentially lowering manufacturing costs, and easier access to desired optoelectronic properties by structural modifications. However, they have not seen the same rise in popularity as fused nonfullerene acceptors (NFA)s due to their lower performances in devices. In this work, we explore structure–property relationships of three NFREA molecules based on thieno[3,2-b]thiophene (TT) and benzo[1,2-b:4,5-b′]dithiophene (BDT) cores and malononitrile functionalized-isatin end group acceptors. We describe their synthetic routes, computational analysis, and photophysical and electronic properties. Here, we investigated the effect of changing the core from TT to BDT on electronic properties of the molecules─a result of raising/lowering of the frontier molecular orbital (FMO) levels. We also explored the effect of branched vs nonbranched alkyl groups on the morphological and blend-capabilities in bulk heterojunction (BHJ) OPV devices. These effects were then reflected in the overall device performances, primarily due to favorable FMO energy alignment between the NFREA and polymer. To investigate the behavior of the excited states of the NFREAs within the active layer of the devices, we performed time-resolved microwave conductivity and transient absorption experiments, which confirmed that the TT molecules exhibited very low conductance while the BDT molecule showed moderate photoconductance. Our combined theoretical, synthetic, device, and charge carrier dynamics studies of these materials reveal the central role that structure–property relationships play in future molecular designs of NFREAs that can perform as well as their fused counterparts.

14 SOLAR ENERGY↗
DOE OSTI2023

Chemical design of electronic and magnetic energy scales of tetravalent praseodymium materials

Lanthanides in the trivalent oxidation state are typically described using an ionic picture that leads to localized magnetic moments. The hierarchical energy scales associated with trivalent lanthanides produce desirable properties for e.g., molecular magnetism, quantum materials, and quantum transduction. Here, we show that this traditional ionic paradigm breaks down for praseodymium in the tetravalent oxidation state. Synthetic, spectroscopic, and theoretical tools deployed on several solid-state Pr 4+ -oxides uncover the unusual participation of 4f orbitals in bonding and the anomalous hybridization of the 4f 1 configuration with ligand valence electrons, analogous to transition metals. The competition between crystal-field and spin-orbit-coupling interactions fundamentally transforms the spin-orbital magnetism of Pr 4+ , which departs from the J eff = 1/2 limit and resembles that of high-valent actinides. Our results show that Pr 4+ ions are in a class on their own, where the hierarchy of single-ion energy scales can be tailored to explore new correlated phenomena in quantum materials.

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

Targeted Chemical Looping Materials Discovery by an Inverse Design

Chemical looping with oxygen uncoupling (CLOU) materials is actively sought for combustion of carbonaceous materials to achieve complete conversion and capture of carbon dioxide. These materials may play a vital role in reducing atmospheric carbon via negative carbon output. However, there is no one‐size‐fits‐all approach as different operating conditions and feedstocks may require different CLOU materials. As a result, the exploration and discovery of high‐performance CLOU materials can be a slow process. To address this challenge, a high‐throughput inverse machine learning workflow that identifies optimum materials from perovskite oxides for a given set of targets is developed—temperature and Gibbs free energy of oxygen formation. The model is trained on high‐throughput density functional theory calculations of CLOU materials and inverts the materials design process using a genetic algorithm to produce realistic substituted SrFeO 3‐δ compositions as output. Using the inverse model, it is able to identify several interesting new families of CLOU materials: Sr 1‐ x A x Fe 1‐ y B y O 3‐δ (e.g., A = Ca or K; B = Mg, Bi, Mn, Ni, Co, Cu, or Zn). These materials have shown promising properties, and some of them even outperform the benchmark material in terms of oxygen release kinetics under relevant CLOU operating conditions.

36 MATERIALS SCIENCE↗
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 OSTI2025

Hyperplane decision trees as piecewise linear surrogate models for chemical process design

Recent trends in chemical engineering research point towards an increasing reliance on data-driven modeling approaches. Neural networks, for instance, have proven to be accurate when data is plentiful and high-dimensional, but in many cases, they require computationally-intensive training procedures. Here, in this work, we describe hyperplane decision trees (HT) as a highly expressive and low-compute machine learning model architecture. These models are locally linear and have linear decision boundaries, resulting in a piecewise linear model of the data. This property allows them to be converted into mixed-integer linear constraints which can be globally optimized. Our open-source PyTorch implementation of this method is a fast, flexible, and accessible way to build accurate piecewise linear models of data.

Decision trees↗
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 OSTI2025

Expediting field-effect transistor chemical sensor design with neuromorphic spiking graph neural networks

Improving the sensitive and selective detection of analytes in a variety of applications requires accelerating the rational design of field-effect transistor (FET) chemical sensors. Achieving high-performance detection relies on identifying optimal probe materials that can effectively interact with target analytes, a process traditionally driven by chemical intuition and time-consuming trial-and-error methods. To address the difficulties in probe screening for FET sensor development, this work presents a methodology that combines neuromorphic machine learning (ML) architectures, specifically a hybrid spiking graph neural network (SGNN), with an enriched dataset of physicochemical properties through semi-automated data extraction using large language models. Achieving a classification accuracy of 0.89 in predicting sensor sensitivity categories, the SGNN model outperformed traditional ML techniques by leveraging its ability to capture both global physicochemical properties and sparse topological features through a hybrid modeling framework. Next-generation sensor design was informed by the actionable insights into the connections between material properties and sensing performance offered by the SGNN framework. Through virtual screening for the detection of per- and polyfluoroalkyl substances (PFAS) as a use case, the effectiveness of the SGNN model was further validated. Density functional theory simulations confirmed graphene as a promising active material for PFAS detection as suggested by the SGNN framework. By bridging gaps in predictive modeling and data availability, this integrated approach provides a strong foundation for accelerating advancements in FET sensor design and innovation.

Ferreira, Rodrigo Pires [Univ. of Chicago, IL (Uni↗
DOE OSTI2025

Mixed-Integer Linear Programming Formulation with Embedded Machine Learning Surrogates for the Design of Chemical Process Families

In previous work, we introduced process family design. The main idea is to design a platform of common elements, and, allowing us to capture additional cost savings, simultaneously design a family of processes, and reducing both engineering and deployment timelines. We formulate this as an optimization problem, specifically a nonlinear generalized disjunctive program (GDP). We have proposed two approaches for reformulating and solving this problem: one based on full-discretization of the design space and one that uses Machine Learning (ML) surrogates to replace the nonlinear process models. Using ML surrogates to predict required system costs and performance indicators allows us to reformulate the nonlinearities in the GDP generate an efficient MILP formulation. In this work, we apply the ML surrogate approach to two case studies. One case study involves designing a family of carbon capture systems to cover a set of different flue gas flow rates and inlet CO 2 concentrations, where we consider the absorber and stripper as common unit module types. The second case study focuses on a water-desalination process, where we design a family of these processes for a variety of salt concentrations and flow rates. In both of these case studies, we demonstrate a scalable optimization approach that enables the design of multiple processes simultaneously, reducing the time-to-market and overall costs by maximizing the cost savings due to both economies of scale and economies of numbers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗