Free Alternatives to Gaussian

'Free software' here not means 'libre software'.


Most similar to Gaussian, and code is really clean and easy to read, even have documents for developers.

Opensource License

2. PSI

An Open-Source electronic structure program emphasizing automation, advanced libraries, and interoperability.

Opensource License
Yes GNUv3


Has potentials for solid-state materials (metals, semiconductors) and soft matter (biomolecules, polymers) and coarse-grained or mesoscopic systems. It can be used to model atoms or, more generically, as a parallel particle simulator at the atomic, meso, or continuum scale.

Opensource License
Yes GPLv3


Specializes in periodic systems with plane wave basis sets.

Opensource License
No commercial


Opensource License
Yes GPLv3

Specializes in high level quantum chemistry calculations.

Taken the best features of parallel implementations of quantum chemistry methods for electronic structure.


Opensource License
No Unknown

Oriented towards relativistic quantum chemistry problems.

7. NWChem

Opensource License

Can calculate a smaller set of properties but it can handle mixed QM/MM calculations and periodic systems like solids.

2017/11/11 posted in  Material

Advanced Materials Through AI & Computational Materials Science

A recent Nature article examines how materials researchers are using artificial intelligence to make quantum-mechanical calculations in only a few seconds that once took supercomputers hours to complete.

These computer modeling and machine-learning techniques are generating enormous libraries of materials candidates. Researchers hope that this approach will produce a giant leap in the speed and usefulness of materials discovery. British materials scientist Neil Alford observes, “We are now seeing a real convergence of what experimentalists want and what theorists can deliver.”

The most promising results so far have been achieved with lithium compounds, used in batteries and other applications..

The Nature article also argues that “artificial intelligence will help researchers comb through vast numbers of materials to find just the one they need for the application at hand.” The standard process starts with researchers applying machine learning to lab data and computer modeling in order to extract common patterns and predict new materials. Researchers then look for a material with specific properties and pass along their findings to chemists, who try to produce the theoretical material for testing.

Personally, I think huge opportunities are available from these types of materials databases — the potential is almost limitless. The advances made so far remind me of the robotic discovery efforts at Dow, the advances made by Bristol-Myers Squibb and other pharmaceutical companies, and recent virus discoveries, such as the ones made by Angie Belcher’s group. These discoveries have resulted in everything from catalysts for oxidative coupling of methane to battery electrode materials. These types of efforts are the physical analog to the computational approaches described in these material databases.

Transforming computer predictions to real-world technologies, however, is difficult. Existing databases include a small fraction of all the known materials and only a few possible ones. Researchers have also learned that data-driven discovery works well for some materials, but not for others. And even when researchers successfully isolate a material with potential, it can take years for chemists to synthesize it in a lab.

Despite these challenges, researchers remain confident that they will discover many useful materials that could lead to innovations in electronics, robotics, healthcare, and other fields. In my opinion, the key for researchers is to avoid the scattershot approach. If scientists can try everything, how do we decide where to focus our efforts? To focus the research there must still be brains behind the computational or robotic synthesis efforts. We need to ensure that we aren’t trying to boil the ocean.

I believe that success will require collaboration between different disciplines and groups. For example, people who understand the computational work may not completely understand the physical impact on materials. We must combine those two areas to provide meaningful information that can be used to impact physical materials. Information inside a computer is only useful if we can translate it to the physical world.

2017/5/19 posted in  Material