Empowering neural network-based quantum Monte Carlo with local pseudopotentials.

Local pseudopotentials enhance the efficiency and accuracy of neural network-based quantum Monte Carlo for large, complex systems, including the Fe4S4 cluster.

• Why it matters: Current NNQMC methods are limited to small systems due to high computational demands, restricting their application to more complex, realistic molecules and materials.
• What they did: The authors developed a framework incorporating local pseudopotentials into NNQMC, reducing electron count and avoiding costly integrations, thus improving scalability and accuracy.
• The result: This approach enables reliable, high-precision simulations of large systems, broadening the potential for ab initio studies in complex quantum materials and biological molecules.