To achieve an equitable energy transition toward net-zero 2050 goals, the electrochemical reduction of CO 2 (CO 2 RR) to chemical feedstocks through utilizing both CO 2 and renewable energy is particularly attractive. However, the catalytic activity of CO 2 RR is limited by the scaling relation of the adsorption energies of intermediates. Circumventing the scaling relation is a potential strategy to achieve a breakthrough in catalytic activity. Herein, based on density functional theory (DFT) calculations, we designed a high-entropy alloy (HEA) system of FeCoNiCuMo with high catalytic activity for CO 2 RR. Machine learning models were developed by considering 1280 adsorption sites to predict the adsorption energies of COOH*, CO*, and CHO*. The scaling relation between the adsorption energies of COOH*, CO*, and CHO* is circumvented by the rotation of COOH* and CHO* on the designed HEA surface, resulting in the outstanding catalytic activity of CO 2 RR with the limiting potential of 0.29−0.51 V. This work not only accelerates the development of HEA catalysts but also provides an effective strategy to circumvent the scaling relation.

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We propose a neural evolution structure (NES) generation methodology combining artificial neural networks and evolutionary algorithms to generate high entropy alloy structures. Our inverse design approach is based on pair distribution functions and atomic properties and allows one to train a model on smaller unit cells and then generate a larger cell. With a speed-up factor of ∼1000 with respect to the special quasi-random structures (SQSs), the NESs dramatically reduce computational costs and time, making possible the generation of very large structures (over 40 000 atoms) in few hours. Additionally, unlike the SQSs, the same model can be used to generate multiple structures with the same fractional composition.

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Computational modeling of high entropy alloys (HEA) is challenging given scalability issues of Density functional theory (DFT) and non-availability of Interatomic potentials (IP) for molecular dynamics simulations (MD). This work presents a computationally efficient IP for modeling complex elemental interactions present in HEAs. The proposed random features-based IP can accurately model melting behaviour along with various process related defects. The disordering of atoms during the melting process was simulated. Predicted atomic forces are within 0.08 eV / Å of corresponding DFT forces. MD simulations predictions of mechanical and thermal properties are within 7% of the DFT values. High temperature self diffusion in the alloy system was investigated using the IP. A novel sparse model is also proposed which reduced the computational cost by 94% without compromising on the force prediction accuracy.