Okafor, Andrew N. | Engineering Papers

DOE OSTI2024

Vibrational study of CO, O 2 , and H 2 Adsorbed on the CoCrFeNi (110) High Entropy Alloy Surface

The vibrational properties of CO, O 2 , and H 2 molecularly or dissociatively adsorbed on a CoCrFeNi(110) surface have been probed using highresolution energy loss spectroscopy (HREELS) and modeled using density functional theory (DFT) calculations. Large (~20 mm3) single-crystal, quaternary face-centered cubic CoCrFeNi was synthesized via a modified Czochralski technique. We show strong evidence that CO adsorbs primarily on bridge and on-top sites in compositionally varied local environments, which reflect the random, multielemental surface composition inherent in a high entropy alloy. A variation of adsorption sites is also found with oxygen, which exhibits two broad groups of modes. Comparison to previous photoemission and theoretical studies suggests that the higher energy modes consist primarily of local CrO x species, while the lower energy modes are due to oxygen atoms adsorbed on other metal sites. Unlike CO and O 2 , HREELS upon H 2 adsorption shows only two much narrower modes and is consistent with atomic adsorption on 3-fold hollow sites. The hypothesized adsorption sites for all three species are directly corroborated by our DFT calculations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Surfactant-Specific AI-Driven Molecular Design: Integrating Generative Models, Predictive Modeling, and Reinforcement Learning for Tailored Surfactant Synthesis

Molecular design is a critical aspect of various scientific and industrial fields, where the properties of molecules hold significant importance. In this study, a 3-fold methodology design is presented that leverages the power of generative artificial intelligence (AI), predictive modeling, and reinforcement learning to create tailored molecules with desired properties. This model synergistically combines deep learning techniques with Self-Referencing Embedded Strings (SELFIES) molecular representation to build a generative model that generates valid molecules and a graphical neural network model that accurately forecasts molecular properties. The Variational Autoencoder (VAE) coupled with reinforcement learning helps refine molecule generation based on targeted attributes. Data from an experimental study involving surfactants were used to test the framework. A validation of the structural integrity of the molecules generated was conducted, and Tanimoto similarities were used to quantify the similarity and diversity between the original and generated molecular structures. Also, saliency maps for the generated surfactants were produced to identify the features explaining the property values. Lastly, molecular dynamics simulations were used to validate the stability of the generated molecules. The results showed that the proposed framework can effectively produce valid molecules within the set property threshold value.

36 MATERIALS SCIENCE↗