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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Equivariant Graph Attention Network - 3D Conformers & Feature Fusion

EGAN-3F (Equivariant Graph Attention Network - 3D Conformers & Feature Fusion) presents an innovative approach for predicting binding affinity between small molecules and protein targets, a fundamental task in drug discovery. Traditional structure-based methods often depend on protein-ligand complex structures obtained from crystallography or molecular docking. In contrast, ligand-only machine learning models using 1D or 2D representations such as SMILES have been developed to predict binding affinity without structural information about the target; however, their accuracy is often limited due to the lack of 3D ligand information. EGAN-3F addresses this limitation by integrating spatially aware graph learning with traditional descriptor-based features. We systematically investigate how combining 2D and 3D molecular representations enhances binding affinity prediction from SMILES strings. This approach underscores the importance of modeling conformational diversity and incorporating chemically meaningful descriptors to improve predictive accuracy. The key innovation of EGAN-3F lies in its ability to achieve robust ligand-based binding affinity predictions without requiring protein-ligand complex structures, effectively bridging the gap between purely structural and ligand-only modeling paradigms.

Shim, Heesung [Lawrence Livermore National Laborat↗
DOE OSTI2021

Predicting a Key Catalyst-Performance Descriptor for Supported Metal Nanoparticles: Metal Chemical Potential

The chemical potential of metal atoms in supported nanoparticles provides a convenient descriptor of their performance as heterogeneous catalysts that captures the effects of particle size, support and alloying. Based on microcalorimetric measurements, the chemical potential is shown in this work to be predictable as a function of monometallic particle diameter and the adhesion energy of the particle to the support, and, for oxide supports, this adhesion energy correlates predictably with metal oxophilicity. These correlations provide predictions of metal chemical potential that can enable catalyst design. They also suggest an improvement in the Gibbs-Thomson relation for free-standing nanoparticles, whether metals or molecular solids.

metal/oxide interface↗
DOE OSTI2024

Exciting DeePMD: Learning excited-state energies, forces, and non-adiabatic couplings

We extend the DeePMD neural network architecture to predict electronic structure properties necessary to perform non-adiabatic dynamics simulations. While learning the excited state energies and forces follows a straightforward extension of the DeePMD approach for ground-state energies and forces, how to learn the map between the non-adiabatic coupling vectors (NACV) and the local chemical environment descriptors of DeePMD is less trivial. Most implementations of machine-learning-based non-adiabatic dynamics inherently approximate the NACVs, with an underlying assumption that the energy-difference-scaled NACVs are conservative fields. We overcome this approximation, implementing the method recently introduced by Richardson [J. Chem. Phys. 158, 011102 (2023)], which learns the symmetric dyad of the energy-difference-scaled NACV. Furthermore, the efficiency and accuracy of our neural network architecture are demonstrated through the example of the methaniminium cation CH 2 NH 2 + .

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

Guiding Principles for the Rational Design of Hybrid Materials: Use of DFT Methodology for Evaluating Non‐Covalent Interactions in a Uranyl Tetrahalide Model System

Abstract Together with the synthesis and experimental characterization of 14 hybrid materials containing [UO 2 X 4 ] 2− (X=Cl − and Br − ) and organic cations, we report on novel methods for determining correlation trends in their formation enthalpy (ΔH f ) and observed vibrational signatures. Δ H f values were analyzed through isothermal acid calorimetry and a Density Functional Theory+Thermodynamics (DFT+T) approach with results showing good agreement between theory and experiment. Three factors (packing efficiency, cation protonation enthalpy, and hydrogen bonding energy [ ]) were assessed as descriptors for trends in Δ H f . Results demonstrated a strong correlation between and Δ H f , highlighting the importance of hydrogen bonding networks in determining the relative stability of solid‐state hybrid materials. Lastly, we investigate how hydrogen bonding networks affect the vibrational characteristics of uranyl solid‐state materials using experimental Raman and IR spectroscopy and theoretical bond orders and find that hydrogen bonding can red‐shift U≡O stretching modes. Overall, the tightly integrated experimental and theoretical studies presented here bridge the trends in macroscopic thermodynamic energies and spectroscopic features with molecular‐level details of the geometry and electronic structure. This modeling framework forms a basis for exploring 3D hydrogen bonding as a tunable design feature in the pursuit of supramolecular materials by rational design.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Metal Independent Correlations for Site-specific Binding Energies of Relevant Catalytic Intermediates

Establishing energy correlations among different metals can accelerate the discovery of efficient and cost-effective catalysts for complex reactions. Using a recently introduced coordination-based model, we can predict site specific metal binding energies (∆EM) that can be used as a descriptor for chemical reactions. In this study, we have examined a range of metals including Ag, Au, Co, Cu, Ir, Ni, Os, Pd, Pt, Rh, and Ru and found linear correlations between predicted ∆EM and adsorption energies of CH and O (∆ECH and ∆EO) at various coordination environment for all the considered metals. Interestingly, all the metals correlate with one another under specific surface site coordination, indicating that different metals are interrelated in a particular coordination environment. Furthermore, we have tested and verified for PtPd and PtIr-based alloys that they follow a similar behavior. Moreover, we have expanded the metal space by taking some early transition metals along with few s-block metals and shown a cyclic behavior of the adsorbate binding energy (∆EA) versus ∆EM. Therefore, ∆ECH and ∆EO can be efficiently interpolated between metals, alloys and intermetallics based on information related to one metal only. This simplifies the process of screening new metal catalyst formulations and their reaction energies.

Density function theory↗
DOE OSTI2025

CHEMREASONER: Heuristic Search over a Large Language Model’s Knowledge Space using Quantum-Chemical Feedback

The discovery of new catalysts is essential for the design of new and more efficient chemical processes in order to transition to a sustainable future. We introduce an AI-guided computational screening framework unifying linguistic reasoning with quantum-chemistry based feedback from 3D atomistic representations. Our approach formulates catalyst discovery as an uncertain environment where an agent actively searches for highly effective catalysts via the iterative combination of large language model (LLM)-derived hypotheses and atomistic graph neural network (GNN)-derived feedback. Identified catalysts in intermediate search steps undergo structural evaluation based on spatial orientation, reaction pathways, and stability. Scoring functions based on adsorption energies and barriers steer the exploration in the LLM's knowledge space toward energetically favorable, high-efficiency catalysts. We introduce planning methods that automatically guide the exploration without human input, providing competitive performance against expert-enumerated chemical descriptor-based implementations. By integrating language-guided reasoning with computational chemistry feedback, our work pioneers AI-accelerated, trustworthy catalyst discovery.

artificial intelligence↗
DOE OSTI2021

Automated theoretical chemical kinetics: Predicting the kinetics for the initial stages of pyrolysis

Large scale implementation of high level computational theoretical chemical kinetics offers the prospect for dramatically improving the fidelity of combustion chemical modeling. To facilitate such efforts, we have developed a suite of codes, collectively referred to as AutoMech, that allow for the automatic prediction of the kinetics for large sets of reactions via ab initio transition-state-theory based master-equation calculations. The primary input is simply the mechanism, a dictionary relating chemically identifiable species descriptors (e.g., SMILES or InChIs) to species labels in the mechanism, and a specification of the electronic structure and transition state theory models to be implemented. Here we illustrate the current utility of AutoMech through a study of the initial stages of pyrolysis for 3 sets of fuels: sequences of alkanes, alcohols, and aldehydes. For simplicity, the analysis focuses on abstractions from the fuel by H, CH 3 , and OH, and the decomposition of the resulting radicals. Altogether, there are a total of 166 input channels in these sets (more than 363 forward reactions when expanded to the full set of elementary reactions). The code successfully produces high quality rate estimates (with apparent uncertainties less than a factor of two in limited comparisons with experiment) for > 95% of these. For the radical decomposition reactions, the analysis includes predictions for the pressure dependence of the kinetics. This wide-ranging exploration illustrates (i) the effect of different levels of prediction on the expected accuracy, (ii) the branching between abstractions at different sites for different abstractors, (iii) the dependence of the rates on the chemical structure, and (iv) the variation in radical stabilities across chemical families. These results, as well as the demonstrated feasibility of the methodology, should find further utility in the development of accurate rate expressions for arbitrary fuels. (c) 2020 The Combustion Institute. Published by Elsevier Inc. All rights reserved.

Elliott, Sarah N.↗
DOE OSTI2020

Chemometric modeling of power conversion efficiency of organic dyes in dye sensitized solar cells for the future renewable energy

The present study reports chemometric modeling of power conversion efficiency (PCE) of dye sensitized solar cells (DSSCs) using the biggest available data set till date which comprises around 1200 dyes covering 7 chemical classes. To extract the best structural features required for higher PCE, we have developed multiple partial least squares (PLS) quantitative structure-property relationship (QSPR) models for the Triphenylamine, Phenothiazine, Indoline, Porphyrin, Coumarin, Carbazole and Diphenylamine chemical classes using descriptors derived from the best subset selection method followed by selection of best five models in each dataset based on the Mean Absolute Error (MAE) values. The models were validated both internally and externally followed by the consensus predictions employing “Intelligent Consensus Predictor” tool to examine whether the quality of predictions can be improved with the “intelligent” selection of multiple PLS models. The quality of predictions for the respective external sets showed that the consensus models (CM) are better than the individual models (IM) in most of the cases. From the insights of the developed models, we concluded that attributes like a packed structure toward higher conductivity of electrons, auxiliary donor fragment of aromatic tertiary amines, number of thiophenes inducing the bathochromic shift and augmenting the absorption, presence of additional electron donors, enhancement of electron-donating abilities, number of non-aromatic conjugated C(sp 2 ) which helps as conjugation extension units to broaden the absorption and highly conjugated $π$-systems exert positive contributions to the PCE. On the contrary, features negatively contributing to PCE are the followings: fragments which lower the tendency of localized $π–π*$ transition, fragments related to larger volume and surface area of dyes along with hydrophobicity resulting in poor adhesion, fragment RC = N causing dye hydrolysis, steric hindrance for π electronic mobility, fragments enhancing polarity, etc. Finally, the identified features from the best QSPR model of the coumarin dataset was employed in designing of ten more efficient coumarin dyes (predicted %PCE ranging from 8.93 to 10.62) than the existing ones.

14 SOLAR ENERGY↗
DOE OSTI2024

Artificial Intelligence for Conjugated Polymers

Conjugated polymers have garnered significant attention due to their diverse applications in electronics, photonics, and energy storage. However, realizing their full potential poses a formidable challenge, as their design has historically relied on iterative adjustments and continuous inspiration from researchers. Traditional methods often struggle to efficiently navigate their vast chemical landscape. In this work, the application of artificial intelligence (AI), specifically machine learning (ML), needs to be discussed in the realm of conjugated polymers. Our paper emphasizes the importance of understanding the structure–property relationships of these polymers and how ML can facilitate property prediction and inverse-design. We delve into various chemical fingerprints, structural descriptors, and ML algorithms, showcasing their utility across a spectrum of applications, including simulations, glass transition temperature determination, photovoltaics, reorganization energy for charge transport, photocatalysts, and sensors. Finally, we give some outlooks in this filed and propose unexplored areas within the field that hold the potential to benefit from ML techniques.

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

Hydrogen in Nanocatalysis

Hydrogen is ubiquitous in catalysis. It is involved in many important reactions such as water splitting, N 2 reduction, CO 2 reduction, and alkane activation. In this Perspective, we focus on the hydrogen atom and follow its electron as it interacts with a catalyst or behaves as part of a catalyst from a computational point of view. In this work, we present recent examples in both nanocluster and solid catalysts to elucidate the parameters governing the strength of the hydrogen–surface interactions based on site geometry and electronic structure. We further show the interesting behavior of hydride in nanometal and oxides for catalysis. Overall, the key take-home messages are: (1) the in-the-middle electronegativity and small size of hydrogen give it great versatility in interacting with active sites on nanoparticles and solid surfaces; (2) the strength of hydrogen binding to an active site on a surface is an important descriptor of the chemical and catalytic properties of the surface; (3) the energetics of the hydrogen binding is closely related to the electronic structure of the catalyst; (4) hydrides in nanoclusters and oxides and on surfaces offer unique reactivity for reduction reactions.

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

Intermetallic alloy structure–activity descriptors derived from inelastic X-ray scattering

Synchrotron spectroscopy and Density Functional Theory (DFT) are combined to develop a new descriptor for the stability of adsorbed chemical intermediates on metal alloy surfaces. This descriptor probes the separation of occupied and unoccupied d electron density in platinum and is related to shifts in Resonant Inelastic X-ray Scattering (RIXS) signals. Simulated and experimental spectroscopy are directly compared to show that the promoter metal identity controls the orbital shifts in platinum electronic structure. The associated RIXS features are correlated with the differences in the band centers of the occupied and unoccupied d bands, providing chemical intuition for the alloy ligand effect and providing a connection to traditional descriptions of chemisorption. The ready accessibility of this descriptor to both DFT calculations and experimental spectroscopy, and its connection to chemisorption, allow for deeper connections between theory and characterization in the discovery of new catalysts.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Developing predictive models for µ opioid receptor binding using machine learning and deep learning techniques

Opioids exert their analgesic effect by binding to the µ opioid receptor (MOR), which initiates a downstream signaling pathway, eventually inhibiting pain transmission in the spinal cord. However, current opioids are addictive, often leading to overdose contributing to the opioid crisis in the United States. Therefore, understanding the structure-activity relationship between MOR and its ligands is essential for predicting MOR binding of chemicals, which could assist in the development of non-addictive or less-addictive opioid analgesics. This study aimed to develop machine learning and deep learning models for predicting MOR binding activity of chemicals. Chemicals with MOR binding activity data were first curated from public databases and the literature. Molecular descriptors of the curated chemicals were calculated using software Mold2. The chemicals were then split into training and external validation datasets. Random forest, k-nearest neighbors, support vector machine, multi-layer perceptron, and long short-term memory models were developed and evaluated using 5-fold cross-validations and external validations, resulting in Matthews correlation coefficients of 0.528–0.654 and 0.408, respectively. Furthermore, prediction confidence and applicability domain analyses highlighted their importance to the models’ applicability. Our results suggest that the developed models could be useful for identifying MOR binders, potentially aiding in the development of non-addictive or less-addictive drugs targeting MOR.

Research & Experimental Medicine↗
DOE OSTI2024

Generalized representative structures for atomistic systems

A new method is presented to generate atomic structures that reproduce the essential characteristics of arbitrary material systems, phases, or ensembles. Previous methods allow one to reproduce the essential characteristics (e.g. the chemical disorder) of a large random alloy within a small crystal structure. The ability to generate small representations of random alloys, along with the restriction to crystal systems, results from using the fixed-lattice cluster correlations to describe structural characteristics. A more general description of the structural characteristics of atomic systems is obtained using complete sets of atomic environment descriptors. These are used within for generating representative atomic structures without restriction to fixed lattices. A general data-driven approach is provided here utilizing the atomic cluster expansion (ACE) basis. The N-body ACE descriptors are a complete set of atomic environment descriptors that span both chemical and spatial degrees of freedom and are used within for describing atomic structures. The generalized representative structure (GRS) method presented within generates small atomic structures that reproduce ACE descriptor distributions corresponding to arbitrary structural and chemical complexity. It is shown that systematically improvable representations of crystalline systems on fixed parent lattices, amorphous materials, liquids, and ensembles of atomic structures may be produced efficiently through optimization algorithms. With the GRS method, we highlight reduced representations of atomistic machine-learning training datasets that contain similar amounts of information and small 40–72 atom representations of liquid phases. The ability to use GRS methodology as a driver for informed novel structure generation is also demonstrated. The advantages over other data-driven methods and state-of-the-art methods restricted to high-symmetry systems are highlighted.

atomic cluster expansion↗
DOE OSTI2026

High entropy oxides prediction and discovery by the Mixed Enthalpy-Entropy Descriptor

The vast, high-dimensional composition space of high-entropy oxides (HEOs) offers exceptional opportunities for functional materials discovery, yet it also poses a fundamental challenge: the rational and efficient prediction of stable, synthesizable compositions and the corresponding structure–property relationships. Despite growing interest, the field still lacks broadly applicable, physically grounded descriptors capable of navigating various large chemical spaces. Here, we introduce a Mixed Enthalpy–Entropy Descriptor (MEED) that enables rapid, first-principles–based prediction of HEOs synthesizability across diverse chemistries. Using MEED, we perform high-throughput screening of two distinct HEO families: rocksalt oxides and perovskite oxides. The predicted top candidates in each family were experimentally validated. MEED reveals unifying thermodynamic and structural principles governing stability across both chemical compositions and polymorphs, providing mechanistic insight into the formation of high-entropy phases. This work significantly broadens the accessible chemical design space for HEOs and establishes a data-efficient framework for accelerating the discovery of next-generation functional materials.

Yu, Liping [University of Central Florida]↗
DOE OSTI2024

Application of machine learning to discover new intermetallic catalysts for the hydrogen evolution and the oxygen reduction reactions

The adsorption energies for hydrogen, oxygen, and hydroxyl were calculated by means of density functional theory on the lowest energy surface of 24 pure metals and 332 binary intermetallic compounds with stoichiometries AB, A 2 B, and A 3 B taking into account the effect of biaxial elastic strains. This information was used to train two random forest regression models, one for the hydrogen adsorption and another for the oxygen and hydroxyl adsorption, based on 9 descriptors that characterized the geometrical and chemical features of the adsorption site as well as the applied strain. All the descriptors for each compound in the models could be obtained from physico-chemical databases. The random forest models were used to predict the adsorption energy for hydrogen, oxygen, and hydroxyl of ≈2700 binary intermetallic compounds with stoichiometries AB, A 2 B, and A 3 B made of metallic elements, excluding those that were environmentally hazardous, radioactive, or toxic. This information was used to search for potential good catalysts for the HER and ORR from the criteria that their adsorption energy for H and O/OH, respectively, should be close to that of Pt. Further, this investigation shows that the suitably trained machine learning models can predict adsorption energies with an accuracy not far away from density functional theory calculations with minimum computational cost from descriptors that are readily available in physico-chemical databases for any compound. Moreover, the strategy presented in this paper can be easily extended to other compounds and catalytic reactions, and is expected to foster the use of ML methods in catalysis.

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

Strain‐Induced Orbital Contributions to Oxygen Electrocatalysis in Transition‐Metal Perovskites

Abstract Epitaxial strain has been shown to produce dramatic changes to the orbital structure in transition metal perovskite oxides and, in turn, the rate of oxygen electrocatalysis therein. Here, epitaxial strain is used to investigate the relationship between surface electronic structure and oxygen electrocatalysis in prototypical fuel cell cathode systems. Combining high‐temperature electrical‐conductivity‐relaxation studies and synchrotron‐based X‐ray absorption spectroscopy studies of La 0.5 Sr 0.5 CoO 3 and La 0.8 Sr 0.2 Co 0.2 Fe 0.8 O 3 thin films under varying degrees of epitaxial strain reveals a strong correlation between orbital structure and catalysis rates. In both systems, films under biaxial tensile strain simultaneously exhibit the fastest reaction kinetics and lowest electron occupation in the d z 2 orbitals. These results are discussed in the context of broader chemical trends and electronic descriptors are proposed for oxygen electrocatalysis in transition metal perovskite oxides.

Fernandez, Abel↗
DOE OSTI2024

Mixed Enthalpy–Entropy Descriptor for the Rational Design of Synthesizable High-Entropy Materials Over Vast Chemical Spaces

The practically unlimited high-dimensional composition space of high-entropy materials (HEMs) has emerged as an exciting platform for functional material design and discovery. However, the identification of stable and synthesizable HEMs and robust design rules remains a daunting challenge. Here, we propose a mixed enthalpy–entropy descriptor (MEED) that enables highly efficient, robust, high-throughput prediction of synthesizable HEMs across vast chemical spaces from first-principles. The MEED is based on two parameters: the relative formation enthalpy with respect to the most stable competing compound and the spread of the point-defect formation energy spectrum. The former measures the relative synthesizability of an HEM to its most stable competing phase, going beyond the conventional thermodynamic understanding. Further, the latter gauges the relative entropy forming ability of an HEM, entailing no sampling over numerous alloy configurations. By applying the MEED to two structurally distinct representative material systems (i.e., 3D rocksalt carbides and 2D layered sulfides), we not only successfully identify all experimentally reported HEMs within these systems but also reveal a cutoff criterion for assessing their relative synthesizability within each system. By the MEED, tens of new high-entropy carbides and 2D high-entropy sulfides are also predicted, which have the potential for a wide variety of applications such as coating in aerospace devices, energy conversion and storage, and flexible electronics.

36 MATERIALS SCIENCE↗