Large-scale molecular systems require massive computational power. Rev. C.01 stabilizes shared-memory parallel processing (OpenMP/shared memory) and distributed parallel processing (Linda). It minimizes overhead communication between CPU cores, preventing performance throttling when scaling calculations across 64, 128, or more cores on modern AMD EPYC or Intel Xeon processors. Solvent Model Stability
Dynamic memory allocation errors ( %Mem ) are a frequent headache for computational chemists. Rev. C.01 patches minor memory leaks associated with high-angular-momentum basis functions (such as
Revision C.01 brings several subtle yet impactful enhancements to Gaussian 16’s expansive toolset. 1. Enhanced Code Stability and Bug Fixes
Revision C.01 expanded GPU support to include NVIDIA V100 (Volta architecture) cards, building upon previous support for K40, K80, and P100 cards introduced in Rev. B.01. For Linux systems, the x86_64 AVX-enabled binary version includes GPU support for NVIDIA K40, K80, P100, and V100 boards with 12 GB of memory or higher, requiring NVIDIA drivers compatible with CUDA 10.0. gaussian 16 revision c.01
Revision C.01 provides sophisticated tools for predicting spectra (IR, Raman, VCD, ROA). It doesn’t just give you "stick" diagrams; it accounts for anharmonicity—the "real world" stretches and bends of molecules—leading to predictions that match laboratory experimental data with much higher fidelity. 5. Stability and Parallelism
Are you setting up an or looking to resolve a specific error ?
The standout feature of Revision C.01 is its optimization for . It includes improved algorithms for DFT (Density Functional Theory) and HF (Hartree-Fock) calculations, specifically targeting the reduction of I/O bottlenecks. This means it handles molecules with hundreds of atoms much more fluidly than previous versions. 2. New Functional Support As of 2025
On high-performance computing clusters, the revision is typically accessed via module systems. For example, the Center for High Performance Computing at the University of Utah offers three versions: legacy (pre-SSE4.2), SSE4 (for older nodes), AVX (for standard nodes), and AVX2 (for newer nodes). Users are advised to select the optimal version for their hardware to maximize performance. A typical environment setup command is module load gaussian16/AVX.C01 .
%Mem and %NProcShared : Allocates 4 gigabytes of RAM and 4 CPU cores to the job.
: It can be interfaced with external optimizers (such as Python-based Gaussian Process optimizers) for evaluating semi-empirical prior mean functions like AM1. Spectroscopic Analysis new double-hybrid methods were added
Fixed edge-case convergence failures when calculating high-lying excited states using time-dependent density functional theory. Core Capabilities Maintained in Rev. C.01
The revision expanded the library of density functional theory methods with five new functionals: M08HX, MN15, MN15L, PW6B95, and PW6B95D3. These additions provided researchers with more options for accurately modeling various chemical systems. Furthermore, new double-hybrid methods were added, including DSDPBEP86, PBE0DH, and PBEQIDH, which combine DFT with perturbative correlation for improved accuracy.
As of 2025, Gaussian Inc. has not announced a formal "Gaussian 17" or "Gaussian 20". Instead, they have iterated on the Gaussian 16 codebase with revisions C.02, C.03, and D.01 (a minor update for Apple Silicon). However, remains the last version with extensive HPC testing and widespread community validation.