报告人：吕定顺 研究员（字节跳动 ByteDance Research）
报告内容：
Applying quantum chemistry algorithms to large-scale systems requires substantial computational resources scaled with the system size and the desired accuracy. To address this, ByteQC, a fully-functional and efficient package for largescale quantum chemistry simulations, has been open-sourced at https://github.com/bytedance/byteqc, leveraging recent advances in computational power and many-body algorithms. Regarding computational power, several standard algorithms are efficiently implemented on modern GPUs, ranging from mean-field calculations (Hartree-Fock and density functional theory) to post-Hartree-Fock methods such as Møller-Plesset perturbation theory, random phase approximation, coupled cluster methods, and quantum Monte Carlo methods. For the algorithmic approach, we also employ a quantum embedding method, which significantly expands the tractable system size while preserving high accuracy at the gold-standard level. All these features have been systematically benchmarked. We further use ByteQC to study adsorption energy for a series of large scale system with the advanced feature ‘embyte’ in ByteQC at CCSD(T) level accuracy for MgO(001) + CO, graphene + small organic molecules, and the metal-organic framework CPO-27-Mg + CO/CO2. The results show that the adsorption energies calculated align within chemical accuracy with experimental values, achieving sub-chemical accuracy in the cases of MgO(001) + CO and CPO-27-Mg + CO/CO2. Subsequently, we attempted to calculate graphene + water monomer by expanding the substrate under both PBC and OBC to eliminate finite size errors. We scaled up to graphene model consisting of 600 carbon atoms, and achieve some surprisely finding.
报告人简介：
Dr. Dingshun Lv is a staff scientist and AI for Science manager at ByteDance Research. Before join ByteDance, I am a senior scientist in Huawei 2012 lab, focus on near-term quantum algorithm, quantum software. I obtain my Ph.D degree from Tsinghua University under Kihwan Kim’s supervisor. I have 10 more publications in Nature Physics, Nature Communication, PRX, PRL, npj Computational Material, Chemical Science, JPCL/JCTC and so on. My current interests including large-scale quantum chemical simulations, strongly correlated system simulations, quantum embedding theory and its applications and AI for Science.
主持人：陈默涵 研究员（北京大学应用物理与技术研究中心）
时 间：2025年3月13日（周四）12:00
地 点：北京大学工学院1号楼210会议室
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