Computational Investigation of Nanoscale Electrocatalysts for Clean Energy Conversion

Electrocatalysis provides a practical solution to the increasing global energy demand while maintaining a sustainable environment. Recently nanoscale catalysts (nanoparticles, nanowires, and dealloyed surfaces) have been shown to have experimentally far superior performance than metallic crystals at sustainable energy conversion. However, the surface feature of these improved catalysts is still unknown, as the detection of the active sites directly from experiment has not been possible. In this thesis work, we discuss using the quantum mechanics based muitiscale simulations and machine learning to understand the nature of these superior materials. We first studied jagged Pt nanowire (J-PtNW), which was shown to have performance at oxygen reduction reactions (ORR) 50 times better than Pt/C. We used multiscale simulations (reactive force field, and density functional theory) to explain this remarkably accelerated ORR activity from an atomistic perspective. Next, we looked into the irregular gold surfaces and copper surfaces (nanoparticles and dealloyed surfaces), which showed dramatically improved performance at CO2 reduction reactions (CO2RR) and CO reduction reactions (CORR). We developed the strategy to combine the reactive force field, density functional theory, and machine learning to identify the active sites responsible for their improved performance. This approach provided the possibility to understand the highly irregular and disordered surface, which is impossible with surface science experiments or with quantum mechanics. The identification of the active sites provides insights into new design concepts (alloys, NP, NW, and electrolytes such as ionic liquids) aimed at increasing product selectivity and rates simultaneously with reducing energy requirements.</p

Atomistic Explanation of the Dramatically Improved Oxygen Reduction Reaction of Jagged Platinum Nanowires, 50 times better than Pt

Pt is the best catalyst for the oxygen reduction reactions (ORRs), but it is far too slow. Huang and co-workers showed that dealloying 5 nm Ni7Pt3 nanowires (NW) led to 2 nm pure Pt jagged NW (J-PtNW) with ORRs 50 times faster than Pt/C. They suggested that the undercoordinated surface Pt atoms, mechanical strain, and high electrochemically active surface area (ECSA) are the main contributors. We report here multiscale atomic simulations that further explain this remarkably accelerated ORR activity from an atomistic perspective. We used the ReaxFF reactive force field to convert the 5 nm Ni₇Pt₃ NW to the jagged 2 nm NW. We applied quantum mechanics to find that 14.4% of the surface sites are barrierless for O_(ads) + H₂O_(ads) → 2OH_(ads), the rate-determining step (RDS). The reason is that the concave nature of many surface sites pushes the OH bond of the H₂O_(ads) close to the O_(ads), leading to a dramatically reduced barrier. We used this observation to predict the performance improvement of the J-PtNW relative to Pt (111). Assuming every surface site reacts independently with this predicted rate leads to a 212-fold enhancement at 298.15 K, compared to 50 times experimentally. The atomic structures of the active sites provide insights for designing high-performance electrocatalysts for ORR

Identifying Active Sites for CO₂ Reduction on Dealloyed Gold Surfaces by Combining Machine Learning with Multiscale Simulations

Gold nanoparticles (AuNPs) and dealloyed Au_3Fe core–shell NP surfaces have been shown to have dramatically improved performance in reducing CO_2 to CO (CO2RR), but the surface features responsible for these improvements are not known. The active sites cannot be identified with surface science experiments, and quantum mechanics (QM) is not practical for the 10 000 surface sites of a 10 nm NP (200 000 bulk atoms). Here, we combine machine learning, multiscale simulations, and QM to predict the performance (a-value) of all 5000–10 000 surface sites on AuNPs and dealloyed Au surfaces. We then identify the optimal active sites for CO2RR on dealloyed gold surfaces with dramatically reduced computational effort. This approach provides a powerful tool to visualize the catalytic activity of the whole surface. Comparing the a-value with descriptors from experiment, computation, or theory should provide new ways to guide the design of high-performance electrocatalysts for applications in clean energy conversion

Model-Driven Based Deep Unfolding Equalizer for Underwater Acoustic OFDM Communications

It is challenging to design an equalizer for the complex time-frequency doubly-selective channel. In this paper, we employ the deep unfolding approach to establish an equalizer for the underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) system, namely UDNet. Each layer of UDNet is designed according to the classical minimum mean square error (MMSE) equalizer. Moreover, we consider the QPSK equalization as a four-classification task and adopt minimum Kullback-Leibler (KL) to achieve a smaller symbol error rate (SER) with the one-hot coding instead of the MMSE criterion. In addition, we introduce a sliding structure based on the banded approximation of the channel matrix to reduce the network size and aid UDNet to perform well for different-length signals without changing the network structure. Furthermore, we apply the measured at-sea doubly-selective UWA channel and offshore background noise to evaluate the proposed equalizer. Experimental results show that the proposed UDNet performs better with low computational complexity. Concretely, the SER of UDNet is nearly an order of magnitude lower than that of MMSE

Activation mechanism of the G protein-coupled sweet receptor heterodimer with sweeteners and allosteric agonists

The sweet taste in humans is mediated by the TAS1R2/TAS1R3 G protein-coupled receptor (GPCR), which belongs to the class C family that also includes the metabotropic glutamate and γ-aminobutyric acid receptors. We report here the predicted 3D structure of the full-length TAS1R2/TAS1R3 heterodimer, including the Venus Flytrap Domains (VFDs) [in the closed–open (co) active conformation], the cysteine-rich domains (CRDs), and the transmembrane domains (TMDs) at the TM56/TM56 interface. We observe that binding of agonists to VFD2 of TAS1R2 leads to major conformational changes to form a TM6/TM6 interface between TMDs of TAS1R2 and TAS1R3, which is consistent with the activation process observed biophysically on the metabotropic glutamate receptor 2 homodimer. We find that the initial effect of the agonist is to pull the bottom part of VFD3/TAS1R3 toward the bottom part of VFD2/TAS1R2 by ∼6 Å and that these changes get transmitted from VFD2 of TAS1R2 (where agonists bind) through the VFD3 and the CRD3 to the TMD3 of TAS1R3 (which couples to the G protein). These structural transformations provide a detailed atomistic mechanism for the activation process in GPCR, providing insights and structural details that can now be validated through mutation experiments

Artificial Intelligence and QM/MM with a Polarizable Reactive Force Field for Next-Generation Electrocatalysts

To develop new generations of electrocatalysts, we need the accuracy of full explicit solvent quantum mechanics (QM) for practical-sized nanoparticles and catalysts. To do this, we start with the RexPoN reactive force field that provides higher accuracy than density functional theory (DFT) for water and combine it with QM to accurately include long-range interactions and polarization effects to enable reactive simulations with QM accuracy in the presence of explicit solvent. We apply this RexPoN-embedded QM (ReQM) to reactive simulations of electrocatalysis, demonstrating that ReQM accurately replaces DFT water for computing the Raman frequencies of reaction intermediates during CO₂ reduction to ethylene. Then, we illustrate the power of this approach by combining with machine learning to predict the performance of about 10,000 surface sites and identify the active sites of solvated gold (Au) nanoparticles and dealloyed Au surfaces. This provides an accurate but practical way to design high-performance electrocatalysts