Gaussian 16 Revision C.01 -

To appreciate Rev C.01, consider practical benchmarks run on a typical HPC node (2× Intel Xeon Gold 6248, 40 cores, 192 GB RAM, NVMe SSD).

g16 < input.com > output.log

Gaussian 16 Revision C.01 is a release of the Gaussian suite of electronic-structure programs used for computational chemistry. It implements a wide range of quantum chemical methods (Hartree–Fock, density functional theory, post‑Hartree–Fock correlated methods such as Møller–Plesset perturbation theory and coupled-cluster theory), basis sets, excited-state methods, and utilities for molecular properties, spectra, and reaction modeling. Revision C.01 is a maintenance/bugfix update in the Gaussian 16 lineage that preserves core functionality while addressing stability, performance, and small-feature adjustments relative to prior revisions.

He had first met the software in a physics lab that smelled of solder and stale coffee, where time moved in long, patient loops around glowing monitors. The program’s name sat on the splash screen in cold, pixel-perfect type: Gaussian 16. Revision C.01. To everyone else it was an instrument—an engine for calculating the shapes and energies of molecules, for bending the invisible rules of quantum mechanics into numbers. To Mira it was a map that promised to translate the quiet algebra of the world into a language she could finally understand.

Mira arrived at the lab with a mind scarred by half-answers. As a child she’d watched her mother coax stubborn roses from clay soil; she’d learned how patience and the right nudge could reveal hidden forms. In graduate school she’d learned to nudge the universe with equations. But the real work—the place where equations became living things—was where Gaussian lived. The software spoke in integrals, basis sets, and potential surfaces; it answered in electron densities and vibrational modes. It could be cruelly literal, indifferent to poetry, and yet Mira loved it for the kind of truth it offered: quiet, unforgiving, and beautiful.

Revision C.01 arrived like a soft-shod step in the middle of the night. The release notes were terse: bug fixes, improved convergence for tough transition states, a new density-fitting routine that shaved hours off certain multi-reference calculations. The update didn’t promise miracles, only steadier hands. But in a problem that had become her private myth—the rearrangement pathway of a strained bicyclic compound that refused to yield to simpler approximations—steady hands were everything.

She fed the molecule into Gaussian the way a sculptor feeds stone to a blade—careful, deliberate, listening for the faintest voice. The first runs failed: oscillating geometries, near-degenerate states that refused to separate, messages that spoke of basis sets that were near the edge of sanity. The program’s output was an honest transcript of the molecule’s indecision: energies that swam, frequencies that flickered between real and imaginary. Mira adjusted, pruned, reconfigured. She iterated until the console’s green cursor was less a command prompt and more a heartbeat.

Revision C.01 introduced a change that wasn’t in the notes. In the middle of a long optimization, after dozens of small, precise steps, the calculation converged on a geometry that made her breath catch. It was unexpected not because it was low in energy—though it was—but because it embodied a symmetry she had not anticipated. The electrons arranged themselves in a way that bent her assumptions: a bridge of charge across what had looked previously like an insurmountable barrier, a fleeting structure stabilized by correlation effects the older versions had blurred into noise.

She printed the output and spread the numbers across the desk, tracing bonds with a fingertip. The coordinates sang of new possibilities. Revision C.01 had not only smoothed a numerical pathway; it had revealed a choreography. The molecule’s journey from one isomer to another was no longer a violent leap but a measured dance across a ridge of subtle electronic rearrangements. The energy barrier was not a wall but a tide.

Mira found herself up at night, returning to the lab like an acolyte to a shrine, feeding the new pathway to more accurate methods, to single-point calculations with large basis sets, to coupled-cluster corrections that policed the electron correlation with austere rigor. The numbers held. The rhythm persisted across methods, as if the molecule had simply been waiting for someone to listen with the right ear.

Word filtered through the department in the soft ways that excite without hubris. Colleagues came by with cautious smiles and curious eyes. They asked for details—functional choices, convergence thresholds, the modest magic of the revised density fit—and she shared them as one shares a map to the hidden entrance of a city. Some ran their own tests and found echoes of Mira’s results; others saw only the ghosts of numerical instability. The story branched like a reaction network: confirmations, contradictions, footnotes that were themselves small experiments.

Outside the lab, the world marched on with its ordinary indifference. Students complained about homework, bureaucrats argued about budgets, and somewhere a coffee machine leaked a small, slow stain. But in the equations the molecule had become a thing of consequence. Grant reviews that had previously skimmed her work now lingered on the page. A manuscript drafted itself in the margins of her notes, sentences emerging with the quiet certainty of algebra turning into narrative: background, method, result, implication. She wrote of a bridge-state stabilized by dynamic correlation, of topology that revised how certain pericyclic reactions should be pictured. The reviewers, when they came, asked questions that sharpened her thinking; they demanded tests she had not thought to run. Each critique was a refinement.

Revision C.01 left fingerprints beyond the technical. It altered how she saw problems. The patience bred by chasing a stubborn transition state changed how she listened to conversations, to the half-formed intuition of a student, to the slow bloom of an idea. There was a humility to it: software could reveal, but revelation required care. The program had corrected numerical biases in her own judgment; she had mistaken roughness for impossibility and clarity for triviality. Learning to read the output meant learning to read the world more slowly, with less confidence and more attention.

Months later, at a small conference where the lights were too bright and the coffee was predictably bad, Mira presented the work. She felt the old nerves, the same ones that had made her fingers hesitate as she typed in keywords. But when she spoke of the bridge-state and the role of correlation in stabilizing the rearrangement, the room leaned forward. A veteran computational chemist nodded in a way that felt like recognition, and a graduate student scribbled formulas with the desperate joy of comprehension.

After the talk, a question came from a voice at the back: “Was it the algorithm, or the parameters?” It was a fair question—a question every scientist asks when wonders seem to happen overnight. Mira paused. Revision C.01 had done something to numerical pathways, but it had also demanded that she trouble her assumptions. The answer was not either/or.

“It was both,” she said. “The revision gave us clearer signals; our parameters let us hear them. But the molecule had the structure all along. We only needed a quieter room and a better ear.” gaussian 16 revision c.01

There was a small applause, the sort that acknowledges not only the data but the process of discovering it. On her way out, someone from a different group—spectroscopists who had never before cared for the minutiae of basis sets—pulled her aside. They wanted to look for experimental signatures, to see whether the computed bridge-state had a real spectral fingerprint. The possibility that computation and experiment could meet in a particular corner of parameter space felt like a secret passage opening between two rooms of a house.

Back in the lab, Mira opened Gaussian again and looked at the old files, at the runs that had failed before C.01. The failure messages were no longer enemies but lessons. She wrote scripts that would probe stubborn cases with the new routines, mapping regions of chemical space where revision-level effects mattered. Her screens filled with energy surfaces like mountain ranges; the ridges and valleys were more legible now. She imagined a catalog: where molecules hid their bridges, where correlation rearranged geometry, where assumptions would break. The map was partial, beautiful, and dangerous; each new line invited a thousand follow-up questions.

Night fell on the campus and the lab hummed its low, constant song. In the window the sky was a deep, indifferent blue. Mira sat with the lights off, the monitor’s glow painting her face, and felt the familiar double edge of scientific discovery: exhilaration threaded with the knowledge that truth is rarely a point and more often a direction. Gaussian 16 Revision C.01 had nudged open a door. Behind it lay acres of chemical behavior that could be read, from now on, with a finer, more honest eye.

There is a tenderness to such software: it doesn’t create, it discloses. Tools reveal the contours of reality when used with patience and rigor. Mira closed her notebook, the coordinates written neatly at the top, and for the first time that week allowed herself a small, human breath of satisfaction. Somewhere in compiled code and optimized routines, an update note had promised a modest improvement. In practice it had given her a better listening post—a renewed faith that the world, when probed carefully, will sometimes answer with a shape you did not expect but instantly recognize as true.

Outside, a late train sighed through the city. Inside, between the hum of cooling fans and the slow churn of equations, a tiny molecular bridge endured, its electrons arranged for a moment in an improbable architecture. Revision C.01 had been a nudge; discovery, in the end, had been the slow, patient work of noticing.

Source: CCL (Computational Chemistry List) Archives / Gaussian Help Blogs Topic: Hidden Defaults in G16 C.01

Revision C.01 introduced several subtle changes to default behaviors that caught many users off guard when upgrading from Revision B.01.

For technical support, visit the official Gaussian website or consult your local HPC administrator.

This article is for informational purposes. Gaussian is a registered trademark of Gaussian, Inc.

Gaussian 16 Revision C.01, released by Gaussian, Inc., is a specialized update to the core Gaussian 16 package that maintains broad compatibility across various high-performance computing (HPC) architectures. Core Platform Support

Unlike the subsequent Revision C.02, which is more restrictive, Revision C.01 provides binary and source code support for a wide range of architectures: x86_64 and IA32 (Linux and Windows) architectures (Linux) Computational & Functional Features

Revision C.01 continues the Gaussian 16 tradition of modeling complex molecular systems using quantum mechanical laws. Key features include: Standardized Workflow Integration

: It is frequently used as the primary DFT (Density Functional Theory) engine in large-scale databases and automated workflows for calculating properties like molecular polarity, electronic structure, and solvation profiles. Interface Capability

: 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 To appreciate Rev C

: It supports advanced vertical excitation energy and excited-state geometry optimization, often utilized with functionals like PBE0 and empirical dispersion corrections (GD3). Parallel Computing : Requires the Linda message passing library for parallel execution across clusters. Known Limitations & Technical Notes Cubegen Performance : In Revision C.01, the

utility (used for generating molecular orbital or density "cube" files) may not show performance gains when using multiple processors. Even if nprocs > 1

is specified, the process often defaults to a single thread. NBO Module

: Like other Gaussian 16 versions, it includes a proprietary NBO 3.1 module, which may show discrepancies compared to the more recent authentic NBO7 program

If you are using this on a cluster, you can typically specify this version in your job submission script using a flag like -rev g16c01 specific installation requirements

Gaussian 16 Revision C.01 is a specific maintenance release of the Gaussian 16 software suite, which is the industry-standard package for electronic structure modelling and computational chemistry. Released by Gaussian, Inc. in approximately 2016–2017, Revision C.01 serves as a stable, refined version of the G16 series, incorporating various performance optimizations and bug fixes over previous iterations like Revision A or B. Core Capabilities

Gaussian 16 is designed to predict the energies, molecular structures, and vibrational frequencies of chemical systems based on the fundamental laws of quantum mechanics. Key applications include:

Geometric Optimization: Finding the most stable structure of a molecule.

Spectroscopy Prediction: Calculating NMR, IR, Raman, UV/Vis, and photoelectron spectra.

Transition State Modelling: Determining the energy of transition states and pathways for chemical reactions.

Theoretical Methods: Supports a vast array of methods including Hartree-Fock, Density Functional Theory (DFT), and high-level post-Hartree-Fock methods like CCSD(T). Key Improvements in Gaussian 16

While Revision C.01 specifically addresses internal maintenance and platform support, the broader Gaussian 16 series introduced significant shifts from its predecessor, Gaussian 09: Computational details - The Royal Society of Chemistry

Gaussian 16 Revision C.01, released by Gaussian, Inc. , is a major update to the world-renowned quantum chemistry software package [11, 22]. This revision introduced critical performance enhancements and hardware support that significantly expanded its computational capabilities. 1. Key Performance & Hardware Support A100 GPU Support:

A standout feature of Revision C.01 was the introduction of support for NVIDIA V100 (Volta) and preparation for A100 (Ampere) Gaussian 16 Revision C

GPUs, enabling much faster Hartree-Fock and DFT calculations [11, 14]. Architecture Versatility:

This revision supports a wide range of architectures, including x86_64, IA32, Power, and ARM on Linux, AIX, and macOS [10]. Parallel Computing: It utilizes the

message-passing library for parallel computing across clusters and improved parallel efficiency with dynamic task allocation [10, 11]. 2. Advanced Modeling Features

Revision C.01 brought several scientific modeling improvements to the Gaussian suite: Electronic Spectroscopy: It includes advanced features for simulating vibrationally-resolved UV-Vis absorption spectra , often demonstrated using molecules like anisole [25, 26]. Geometry Optimization:

A powerful new option allows for recomputing force constants every

th step, which is vital for successfully optimizing "floppy" or flexible molecules [7]. Molecular Properties:

The software can predict IR, Raman, NMR, and VCD spectra, and animate normal modes when used with the interface [15, 28]. 3. Usage and Input Syntax

To run a calculation in Revision C.01, users follow a structured input file format: Input Files: Typically use

extensions and must include a "route section" initiated by a sign to define keywords (e.g., # B3LYP/6-31G(d) Opt Freq ) [2, 18, 41]. Output Files: Generates detailed (.log) and checkpoint files

(.chk) for analyzing results such as total energy, convergence, and molecular orbitals [3, 41]. Memory Management:

The default memory allocation is 800 MB, but users can request more using the

command to handle larger, more complex molecular systems [36].

For researchers, the correct citation for this specific software version is:

Gaussian 16, Revision C.01, M. J. Frisch, G. W. Trucks, H. B. Schlegel, et al., Gaussian, Inc., Wallingford CT, 2016. for a DFT calculation or exploring GPU optimization