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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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129 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Spectroscopic Probe Molecule Selection Using Quantum Theory, First-Principles Calculations, and Machine Learning

Probe molecule vibrational spectra have a long history of being used to characterize materials including metals, oxides, metal-organic frameworks, and even human proteins. Furthermore, recent advances in machine learning have enabled computationally generated spectra to aid in detailed characterization of complex surfaces with probe molecules. Despite widespread use of probe molecules, the science of probe molecule selection is underdeveloped. Here, we develop physical concepts, including orbital interaction energy and the energy overlap integral, to explain and predict the ability of probe molecules to discriminate structural descriptors. We resolve the crystal orbital overlap population (COOP) to specific molecular orbitals and quantify their bonding character, which directly influences vibrational frequencies. Using only a single adsorbate calculation from density function theory (DFT), we compute the interaction energy of individual adsorbate molecular orbitals with adsorption site atomic orbitals across many different sites. Combining the molecular orbital resolved COOP and changes in orbital interaction energy enables probe molecule selection for improved discrimination of various sites. We demonstrate these concepts by comparing the predicted effectiveness of carbon monoxide (CO), nitric oxide (NO), and ethylene (C 2 H 4 ) to probe Pt adsorption sites. Finally, using a previously developed machine learning framework, we show that models trained on hundreds of thousand C 2 H 4 spectra, computed from DFT, which regress surface binding-type and generalized coordination number (GCN), outperform those trained using CO and NO spectra. Lastly, a python package, pDOS_overlap, for implementing the electron density based analysis on any combination of adsorbates and materials, is also made available.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2026

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

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

3D molecular generation↗
DOE OSTI2025

Signature of 0 + excited state and shape coexistence in 94 Kr through 93 Kr(d,p) 94 Kr reaction

A measurement of the excitation spectrum of 94 Kr via one-neutron transfer to the ground state of 93 Kr using the 93 Kr(d,p) 94 Kr reaction at 8A MeV, observing the outgoing protons, performed with the IRIS facility at TRIUMF is reported. Two states in 94 Kr, at 1.50±0.14 MeV and 2.20±0.14 MeV, were observed. An adiabatic-wave approximation analysis of the differential cross sections leads us to identify the lower energy state as being populated with neutron transfer to the 3 s1/2 orbital. This leads to the first observation of the lowest 0 + excited state in 94 Kr, hence signaling shape co-existence. Theoretical calculations performed within the in-medium similarity renormalization group framework are presented that also show the existence of a low-energy 0 + state, aligning qualitatively with the observation.

74 ATOMIC AND MOLECULAR PHYSICS↗