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We developed new algorithms for quantum chemistry simulations using AI hardware, significantly speeding up calculations for complex molecules like the FeMo cofactor. This breakthrough enables achieving exact solutions faster than ever before.

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Area of Science:

  • Quantum chemistry
  • Computational materials science
  • Artificial intelligence in scientific computing

Background:

  • Accurate quantum chemical calculations are essential for understanding molecular behavior and designing new materials.
  • Traditional computational methods face limitations in scaling for complex systems, hindering progress in fields like catalysis and materials discovery.
  • Hybrid CPU-multiGPU approaches combined with advanced algorithms are needed to overcome these computational barriers.

Purpose of the Study:

  • To introduce novel algorithmic solutions for hybrid CPU-multiGPU tensor network state algorithms.
  • To leverage non-Abelian symmetries and AI-motivated hardware/software technologies for enhanced computational performance.
  • To demonstrate the capability of these new methods for large-scale simulations of chemically relevant systems.

Main Methods:

  • Development of hybrid CPU-multiGPU tensor network algorithms incorporating non-Abelian symmetries.
  • Large-scale SU(2) spin-adapted density matrix renormalization group (DMRG) calculations.
  • Utilization of NVIDIA Tensor Cores on NVIDIA A100 devices for high-performance computing.

Main Results:

  • Numerical simulations achieved the full configuration interaction (full-CI) limit for complete active space (CAS) calculations up to CAS(18, 18) in a fraction of the time required by traditional methods.
  • Benchmarks up to CAS(113, 76) demonstrated performance around 115 TFLOPS on a single node with eight NVIDIA A100 devices, reaching 71% of hardware capacity.
  • Computational time scaling with bond dimension was reduced from cubic to linear for a wide range of D values.

Conclusions:

  • The developed algorithms and hardware utilization break current computational limits in ab initio quantum chemistry and material science.
  • The hybrid approach offers an estimated effective performance of 300-500 TFLOPS compared to strict U(1) implementations, highlighting the synergy between algorithmic and technological advancements.
  • This work paves the way for tackling previously intractable problems in computational chemistry and materials science.