AlphaPerrin Computational is a cloud-based platform that gives chemists, researchers, professors, and students access to Gaussian 16 — the industry-standard computational chemistry software — directly through a web browser, with no major software installation required.

You don’t need to configure hardware or involve an IT department. You log in, upload your molecular structure, and run your calculations. Results are returned in the same formats you’d expect from a local Gaussian installation.

AlphaPerrin was founded by Dr. Noah Allen, a former chemistry professor with a research background in estrogen-related cancer chemistry. The platform was built to make professional-grade computational chemistry accessible to anyone who needs it — whether you’re running a research program, teaching a course, or working in industry.

Gaussian is a computational chemistry software package first released in 1970 by Nobel laureate John Pople and colleagues. It has been continuously developed and refined ever since, and today Gaussian 16 is one of the most widely cited and broadly used quantum chemistry programs in the world.

Chemists use Gaussian to calculate molecular properties that cannot easily be measured experimentally — things like reaction energy barriers, molecular geometries, spectroscopic properties, electron density distributions, and excited state behavior. It implements a wide range of quantum mechanical methods, from Hartree-Fock to DFT to post-HF correlated methods, and supports an enormous variety of basis sets.

Gaussian is the tool you’ll encounter throughout computational chemistry — in academic research, in graduate programs, and across industry in pharma, materials science, and chemical engineering. Whether you’re using it for your own research or introducing others to computational methods, you’re working with the same software cited in thousands of published studies.

AlphaPerrin runs the actual, licensed Gaussian 16 software on cloud infrastructure. Your calculations are executed by the same program used by computational research groups worldwide — not a simplified approximation or educational substitute.

This matters for a few reasons. Results from AlphaPerrin are directly comparable to published literature that used Gaussian. Output files are in standard Gaussian format and can be viewed with the same visualization tools (GaussView, Avogadro, Molden, etc.) used throughout research and industry. You’re working with software cited in thousands of published studies — not a substitute.

AlphaPerrin uses a tiered pricing model built to make Gaussian accessible at every level:

University Classes — $45 per student, per month, per semester For professors bringing Gaussian into the classroom. Student access is billed at $45 per student, per month, per semester — a cost comparable to a lab manual or course packet that can be passed along as a course material fee. There are no CPU hour limits; students pay once and run as many calculations as their coursework requires. Faculty accounts are included at no charge with a class subscription.

Single Professor Subscription — $90/month Full platform access for an individual faculty member or researcher who wants continuous, on-demand access to Gaussian 16 outside of a course or class subscription. Run calculations for your own research, explore methods, or evaluate the platform in depth — no student enrollment required.

Single Student Subscription — $30/month Individual access for students who need Gaussian outside of a formal course enrollment. No CPU hour limits — run as many calculations as your work requires.

Single Professional Subscription — $150/month Full platform access for industry chemists, independent researchers, or anyone working outside an academic context who needs reliable, on-demand access to Gaussian 16 without the overhead of a traditional license.

A traditional Gaussian installation requires purchasing a license (typically thousands of dollars per year for an institutional license), hardware capable of running it, IT support to configure and maintain the environment, and ongoing management as the software is updated.

With AlphaPerrin, all of that is handled on our end. You access Gaussian through a browser. There is nothing to install, nothing to maintain, and no IT department to involve. You can run calculations from your laptop, from home, or from any device with a browser and an internet connection — so can anyone else on your team or in your course.

The output is identical — Gaussian 16 results in standard Gaussian file formats. The difference is only in how the software is accessed and who manages the infrastructure.

“in silico” is a phrase borrowed from the Latin naming conventions used in biology and chemistry — like “in vitro” (in glass, meaning test tube experiments) and “in vivo” (in life, meaning experiments in living organisms). “In silico” means, simply, in a computer.

Computational chemistry is chemistry done in silico. Rather than mixing reagents in a flask or running an NMR, you construct a mathematical model of your molecule and calculate its properties computationally. The results can predict experimental outcomes, explain observed data, or explore hypothetical systems that would be difficult or impossible to study in the lab.

Gaussian is one of the primary tools used for this kind of work.

Classical physics treats particles and waves as fundamentally different things. A baseball follows a predictable trajectory. A water wave spreads out, interferes with itself, and has no single definite location.

Quantum mechanics reveals that at the scale of electrons and atoms, this distinction breaks down. Quantum systems — electrons, photons, atoms — exhibit characteristics of both particles and waves simultaneously. This is wave-particle duality.

The practical consequence is significant: you cannot describe an electron’s behavior the way you’d describe a billiard ball. An electron doesn’t follow a single definite path. Instead, it exists as a distribution of probabilities — described mathematically by a wavefunction. Measurements of quantum systems also face fundamental limits on precision (the Heisenberg uncertainty principle): the more precisely you know an electron’s position, the less precisely you can know its momentum, and vice versa.

All of quantum chemistry — and everything Gaussian calculates — is built on this foundation. The methods, the basis sets, the SCF procedure: all of it is a framework for making quantitative predictions about systems that behave quantum mechanically.

Why it’s called a “wave”

The function evolves according to the Schrödinger equation, which mathematically resembles wave equations used for water or sound waves.

That is why particles in quantum mechanics behave like waves—showing interference, diffraction, and superposition.

What information it contains

A wavefunction can include:

• position
• momentum
• spin
• energy states
• time evolution

In principle, all measurable properties of a quantum system can be calculated from it.

A useful mental picture:

Imagine a cloud of possibility around a particle.

• Thick parts of the cloud → particle is likely to appear there
• Thin parts → unlikely
• Zero regions → impossible

For electrons in atoms, these probability clouds are exactly what we call atomic orbitals.

In truth physicists still argue about what the wavefunction really is.

Two broad camps exist:

1. Information view: the wavefunction describes our knowledge of the system.
2. Reality view: the wavefunction is a real physical object.

Quantum theory works either way mathematically, so the debate is ongoing.

A delightful thought experiment emerges here: in some interpretations (like Everett’s many-worlds), the entire universe may be described by a single gigantic wavefunction evolving in time.

If you enjoy seeing how this plays out chemically, the wavefunction is exactly what computational chemistry programs like Gaussian calculate—the molecular orbitals you visualize are simply different shapes of ψ for electrons in molecules. The shapes of bonds, nodes, and interference patterns are literally the geometry of the wavefunction.

A very basic explanation

An electron orbital is a mathematical function that describes the regions in an atom where electrons are likely to be found. These regions are defined by quantum mechanics and are characterized by specific energy levels and shapes. Key points about electron orbitals include:

1. Quantum mechanics: orbitals are derived from the solutions to the Schrödinger equation, which provides a probability distribution for finding an electron in a given space around the nucleus.
2. Shapes: orbitals have distinct shapes, including:

s orbitals: spherical in shape. Each energy level has ones orbital.

p orbitals: dumbbell shaped and oriented in the X, Y, and Z axes.

d orbitals: more complex shapes, with five different orientations.

f orbitals: even more complex, with seven orientations.

(See your general chemistry textbook for visual representations of these.)

3. Quantum numbers: Electronic orbitals are described by a set of quantum numbers:

 Principle quantum number (n): This number determines the energy level of the orbital and its size. Higher n values correspond to larger orbitals.

Azimuthal quantum number (l): Determines the shape of the orbital (zero corresponds to an s orbital, one to a p orbital, etc.).

Magnetic quantum number (m_l )Determines the orientation of the orbital.

Spin quantum number (m_s ): Describes the intrinsic spin of the electron.

 energy of the atom, typically filling lower energy levels first (Afbau principle) and following Pauli’s exclusion principle (no two electrons can have the same set of four quantum numbers), and Hund’s rule (every orbital in a subshell is singly occupied before any orbital is doubly occupied).

5. Subshells: orbitals are grouped into subshells (s,p,d,f), which help explain the electron configuration of elements in the periodic table.
6. Visualizing orbitals: orbitals are often visualized as electron cloud diagrams, where the density of the cloud represents the probability of finding an electron in that region.
7. Given all of this, this isn’t to say electrons cannot leave an electron cloud. For example, ions form when an atom does not have equal numbers of electrons and protons.

Understanding electron orbitals is fundamental to the study of chemistry and atomic physics, as they influence how atoms bond and interact with each other.

What is a basis set in computational chemistry?

In computational chemistry, a basis set is a collection of mathematical functions used to describe the electron wavefunction of atoms and molecules. These functions form the building block for approximating the molecular orbitals in quantum chemical calculation, such as those performed using methods like Hartree-Fock or DFT (Density Functional Theory).

Here is a simple analogy: think of the basis set as the alphabet you use to write a sentence (the sentence being the wavefunction).  The more letters you have and the more versatile they are, the better you can express complex ideas. (accurately model electron behavior).

Common characteristics

-Type of functions: Usually Gaussian type orbitals (GTOs) to Slater type orbitals (STOs).  GTOs are more common due to computational efficiency.

-Size of the set: minimal basis set (e.g., STO –3G): uses the smallest number of functions possible.

-Double zeta , triple zeta , etc. (e.g., 6-31G, cc-pVTZ): use multiple functions per orbital to improve accuracy.

-Polarization functions (e.g., 6–31G*): add flexibility for modeling directional bonding.

-Diffuse functions (e.g., 6-31+ G*): useful for describing anions or excited states with electrons far from the nucleus.

In Summary

When you run a quantum chemistry calculation (e.g., using Gaussian), you must choose a basis set and your results can vary significantly depending on which one you use. A larger, more flexible basis usually gives more accurate results, but at a higher computational cost.

Self Consistent Field

In computational chemistry, a self consistent field (SCF) refers to an iterative computational procedure used to find a stable solution to the equation describing electron distributions and energies within molecules or solid. SCF methods are fundamental in quantum chemistry, especially in Hartree-Fock (HF) theory and Density Functional Theory (DFT). 

“Self-consistent” indicates that the electronic potential field calculated for electrons is consistent with the resulting electronic density. The potential and electron distribution mutually defined each other.

How SCF Works

1. Initial Guess:

          Begin with an approximate electron density or a wavefunction.

2. Calculation of potential:

          Using this electron density, compute the effective potential                                experienced by electrons (include electron – electron interactions,                           nuclear attraction, etc.)

3. Solve the Schrödinger equation:

          Use the calculated potential to solve for electron orbitals                                   (wavefunctions).

4.  Update electron density:

          Compute the new electron density from these orbitals.

5. Check for convergence:

          If the difference between the previous and new electron density                       (or total energy) is below a defined threshold, the calculation has                            converged. Otherwise, repeat steps 2-4 using the updated density.

Application

• Hartree-Fock (HF) calculations to approximate molecular orbitals.
• Density Functional Theory (DFT) calculations to obtain ground-state energies and electron densities.
• Basis for advanced methods such as post-Hartree-Fock
  (MP2, CCSD, CI) that refine electron correlation effect.

Importance

• SCF provides a baseline approximation of electron structure, from which more sophisticated computational techniques can be developed.
• Ensures internal consistency between electron distributions and the potentials that define them.

In Summary

A self-consistent field in computational chemistry is an iterative computational method used to achieve consistency between electron density and electron potentials, foundational to most quantum chemical calculations.

Hartree Energy Conversions

 Hartree =

• 5 kJ/mol
• 5 kCal/mol
• 211 eV
• 6 cm^-1

The Hartree (symbol: Eₕ) is the atomic unit of energy used in quantum chemistry. Gaussian reports energies in Hartrees by default. It is named after Douglas Hartree, whose work in the 1920s and 30s laid foundational groundwork for modern quantum chemistry.

Conversion factors:

Practical note: When comparing energies between two structures (for example, a reactant and a product, or a ground state and a transition state), the absolute Hartree values are large numbers — but the differences are what matter chemically. A difference of 0.001 Hartree is approximately 2.6 kJ/mol or 0.6 kcal/mol, which is meaningful in a thermochemical context.

Keep these conversions handy when interpreting Gaussian output files, particularly for reaction energy profiles, geometry optimizations, and frequency calculations.