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As an old Chinese saying goes, to do a good job, one must first sharpen one’s tools. For a concrete molecular simulation project, choosing suitable molecular dynamics simulation software is the most basic step before carrying out the subsequent calculations.
The problem is that judging what is “suitable” is not easy for beginners. There are many commonly used molecular dynamics software packages today, such as SPONGE, GROMACS, AMBER, LAMMPS, as well as GPUMD, NAMD, CHARMM, Material Studio, and Desmond. As a developer of SPONGE, I also often use GROMACS, LAMMPS, and AMBER when handling concrete problems. Therefore, this article will not simply turn the question into “why you should use SPONGE”. Instead, it aims to provide a more general methodology to help readers choose suitable molecular dynamics simulation software for their current systems.
Copyright and Licensing
The first factor to consider is not speed, features, or even the number of tutorials, but copyright and licensing. Many beginners assume that “downloadable, installable, and runnable” means “safe to use”, but once a concrete project begins, the situation is often not that simple. You need to confirm whether the software is allowed to be used for the current project, whether it is allowed to be used in industry collaboration projects, whether modified programs, scripts, images, or input files can be shared with collaborators, and how the software should be acknowledged and cited in papers, reports, or project deliverables. If these issues are not handled carefully, the lighter consequences may be supplementary authorization or clarification; the more serious consequences may include paper retraction, infringement compensation in commercial projects, and other severe outcomes.
From this perspective, molecular dynamics software can first be roughly divided into several categories:
| Software | Copyright and licensing | Main constraints |
|---|---|---|
SPONGE | Open source, Apache License 2.0 | License and copyright notices need to be preserved; specific binary packages may also involve third-party dependency terms. |
GROMACS | Open source, LGPL 2.1 | Modification, linking, and redistribution need to comply with LGPL requirements. |
LAMMPS | Open source, GPLv2 | Redistribution of modified versions or derivative programs needs to comply with GPLv2 requirements. |
GPUMD | Open source, GPLv3 | Redistribution of modified versions, derivative programs, or combined code needs to comply with GPLv3 requirements. |
OpenMM | Multiple licenses; core code is commonly under MIT or LGPL | Licenses need to be checked by component; it should not be treated as having a single unified license. |
AmberTools | Free; many major components are under GPL | It is not at the same licensing level as full AMBER, and different components may use different licenses. |
AMBER | Separate license; non-commercial use usually has no license fee, while commercial use requires authorization | Use, redistribution, third-party services, and commercial projects need to be checked against the license terms. |
NAMD | Usually free for non-commercial use; commercial use requires separate authorization | Commercial use, redistribution, and service provision require authorization to be confirmed in advance. |
CHARMM | Available to academic users; commercial use has separate channels | The licensing boundaries of the CHARMM program, the CHARMM force field, and the commercial CHARMm version need to be distinguished. |
Material Studio | Commercial software platform | Constrained by the licenses, modules, user count, and parallel resource limits purchased by the institution. |
Desmond | Common in a commercial ecosystem, with specific non-commercial or academic licensing paths in some cases | Scope of use depends on the specific version, license source, and institutional license. |
In simple terms, Material Studio is a typical commercial software package. Whether it is used for academic research or commercial projects, the institution needs to have the corresponding license. Software such as AMBER, NAMD, CHARMM, and Desmond is usually friendly to academic or non-commercial use, but once commercial use is involved, the corresponding authorization needs to be confirmed and purchased. Other open-source software generally does not distinguish between academic and commercial use and can itself be used for free, but the specific license terms still need to be followed. For example, when using software under a GPL license, personal or internal lab use is usually not a problem, but if modified programs or derivative programs are redistributed, the corresponding source code may need to be released according to the GPL requirements.
Inherited Code
The second factor is very simple: if your research group already has a stable “inherited code” or “inherited workflow” that has been running for years, then usually you should start with it. Here, “inherited” is not meant in a negative sense. It means that this workflow has already been run, modified, debugged, and tested repeatedly by previous group members, and it often comes with accumulated modeling scripts, parameter files, submission scripts, analysis programs, and experience for judging results.
In fact, if you really have such a workflow that can be directly inherited, you probably will not seriously struggle with whether to start from SPONGE, GROMACS, AMBER, or LAMMPS. For beginners, reproducing an existing project with guidance from senior group members is more realistic than comparing software advantages from scratch. Only when the group has no existing route, or when your system clearly exceeds the applicable range of the original workflow, do the following selection criteria become truly important.
Systems and Force Fields
Molecular dynamics software does not work independently of physical models. What really determines whether a simulation can start and whether its results can be interpreted is often whether your system has suitable force field parameters, and whether the software maturely supports that force field.
For biomolecular systems, proteins, nucleic acids, lipid membranes, carbohydrates, small-molecule ligands, and similar objects usually first fall into force-field ecosystems such as AMBER, CHARMM, OPLS, and GROMOS. Therefore, software such as GROMACS, AMBER, NAMD, CHARMM, OpenMM, and SPONGE more naturally enters the candidate list. There are of course differences between them in input formats, runtime efficiency, free energy tools, and community habits, but at least at the beginning of the problem, the system and force field can be matched.
Materials, inorganic solids, and metallic systems are another kind of problem. Common keywords here become potentials such as EAM, MEAM, Tersoff, and Stillinger-Weber, and software choices more easily turn toward LAMMPS, GPUMD, Material Studio, or specific materials simulation platforms. If one forcibly applies biomolecular software thinking here, unnecessary difficulties may arise in parameters, boundary conditions, neighbor lists, long-range interactions, or output analysis.
Polymers, coarse-grained models, reactive force fields, and machine-learning potentials need to be considered separately again. These methods are usually more software-specific, and there are even many programs developed specifically around a certain class of models or potentials. Polymer and coarse-grained simulations often require more flexible topologies, interaction forms, and modeling workflows; reactive force fields involve potentials such as ReaxFF, which can describe bond formation and breaking; machine-learning potentials may involve models such as DP and NEP and their corresponding inference backends. They do not always belong to the same “materials simulation” problem. What should really be checked is whether the target potential, input format, parallel mode, and analysis tools have already formed a mature workflow in the candidate software.
Of course, the classification above is only a reference. It does not mean that a certain software package can only handle one class of systems. In practice, using LAMMPS to run proteins or using GROMACS to run metallic systems can both be done technically. Starting from version 2.0, SPONGE has also introduced support for common materials force fields, such as Tersoff, EDIP, and DP.
Advanced Method Support
At the basic molecular dynamics level, many software packages can complete energy minimization, heating, equilibration, and production simulations. What really differentiates them is often whether you later need more advanced methods, such as enhanced sampling, free energy calculations, path sampling, constant-pH simulations, QM/MM, coarse graining, multiscale simulation, reactive force fields, machine-learning potentials, or simulation workflows with special restraints and biases.
What matters here is not whether a method appears on a promotional page, but whether it is mature, stable, and reproducible in that software. If a method has native support, clear input formats, complete examples, inspectable outputs, and enough literature cases, then beginners can more easily locate problems when they occur. Conversely, if a method can only be barely implemented through external scripts, format conversion, temporary patches, or plugins maintained by a small number of people, it may not be suitable as the first choice for an entry-level project.
Different software packages also emphasize different advanced methods. AMBER has accumulated substantial experience in biomolecular free energy calculations; GROMACS is often used together with tools such as PLUMED; LAMMPS is very flexible for custom interactions and materials-related methods; OpenMM is suitable for building customized workflows through its Python interface; SPONGE continues to develop in enhanced sampling, free energy calculations, and extensions for new workflows. When choosing, there is no need to pursue software that “supports everything”. It is more important to confirm whether the advanced method you truly need has already formed a reliable workflow in the candidate software.
There is also a situation that needs to be viewed in reverse: if your core requirement is itself an advanced method, and molecular dynamics is only an auxiliary step, then software selection does not necessarily have to start from traditional molecular dynamics software. For example, in QM/MM or first-principles-related problems, the quantum chemistry or electronic structure component may be what truly determines result quality and workflow complexity. In such cases, software such as Gaussian, PySCF, VASP, and ABACUS may also enter the candidate list. In other words, do not assume that the main software must be a traditional molecular dynamics program simply because the word “dynamics” appears in the problem.
Community Environment and Usability
For beginners, community environment and usability are often more important than expected. A software package may be powerful in theory, but if documentation is hard to find, tutorials are outdated, error messages are difficult to understand, and examples are far from your own system, then a large amount of time will be consumed fighting the tool itself. Conversely, even if a software package is not the strongest by every metric, clear tutorials, rich examples, searchable common errors, and people around you who have used it can significantly lower the entry barrier.
This is also why software such as GROMACS, AMBER, and LAMMPS has long occupied important positions. It is not only that the programs themselves are mature. More importantly, a large amount of tutorials, literature cases, user Q&A, script snippets, and research group experience has formed around them. When you encounter a problem, you are likely not the first person to encounter it. Search engines, forums, mailing lists, GitHub issues, and old directories left by senior group members may all provide clues.
A graphical user interface (GUI) is also an important factor. For beginners, a friendly GUI can significantly lower the barriers to modeling, parameter setting, job submission, and result inspection. Material Studio and Desmond do this well: they wrap many complex steps into relatively complete graphical workflows. SPONGE is also developing SPONGE Mokda, hoping to provide a friendlier entry point for modeling, simulation setup, and result analysis.
However, a larger community does not always mean easier use. LAMMPS commands and models are extremely flexible, which also means the same problem may have many different ways to write it; the AMBER ecosystem is complete, but its toolchain and file formats need to be learned step by step; GROMACS has many resources, but version differences and the age of tutorials also need to be judged carefully. For beginners, what is truly useful is not simply “the more resources, the better”, but whether reliable examples can be found that are close enough to your system, software version, and research goal.
At present, the advantages of SPONGE are its Chinese-language documentation, tutorials, and relatively low communication cost with developers, while the accompanying Xponge also tries to reduce the barrier to modeling and pre/post-processing. But it must also be admitted that compared with software such as GROMACS, AMBER, and LAMMPS, which have accumulated large international communities over a long time, the public cases, third-party tutorials, and user community of SPONGE still need to continue growing. Therefore, if you choose SPONGE, it is best to make full use of the official documentation, examples, user group, and GitHub feedback channels, rather than relying only on scattered searches.
Hardware
Hardware is also a factor that must be considered when choosing molecular dynamics software. Molecular dynamics simulations are usually computationally intensive, and different software packages differ in their support for CPU, GPU, multi-GPU parallelism, cluster scheduling, and specific hardware platforms. A software package being fast in a paper or benchmark does not necessarily mean that it will run fast on the machines you actually have.
The most common issue is GPU support. Many molecular dynamics software packages now support GPU acceleration, but the support mechanisms differ greatly: some are mainly optimized for NVIDIA CUDA, some support more backends, and some can only run certain algorithms or force fields on GPU. If your research group mainly uses NVIDIA GPUs, then SPONGE, GROMACS, AMBER, NAMD, OpenMM, LAMMPS, GPUMD, and others may all enter the candidate list. But if other hardware platforms are used, you need to additionally confirm whether the corresponding version is mature.
When applying for projects in China, promoting technology transfer, or deploying for enterprises, domestic hardware and trusted IT ecosystem factors may also need to be considered. Not every project will raise such requirements, but if the proposal, acceptance criteria, or application scenario explicitly emphasizes controllability, domestic hardware adaptation, and a domestic software ecosystem, then choosing software that already has adaptation work on related platforms is a plus. For example, when using domestic hardware or platforms such as Huawei, Moore Threads, and Sugon, one cannot assume that all molecular dynamics software can run directly as it does in an NVIDIA CUDA environment. SPONGE already has adaptation work for domestic CPU and domestic GPU platforms, while other software needs to be investigated case by case according to the specific version and hardware platform. On the domestic software side, SPONGE, GPUMD, and others also more naturally enter the discussion.
Another easily overlooked issue is scale. Single-machine single-GPU jobs, small short simulations, multi-GPU large systems, long production simulations, and large batches of jobs on supercomputers place different requirements on software. Some software packages have excellent single-GPU efficiency, some have more mature multi-GPU scaling, some are suitable for quickly building small workflows, and some are better suited for running many jobs on clusters. Instead of asking only “which software is fastest”, it is better to first ask: can my system size, simulation length, and hardware resources be effectively used by this software?
Installation and maintenance costs are also part of the hardware issue. Prebuilt packages, conda installation, container images, source compilation, CUDA versions, driver versions, and MPI environments all affect whether beginners can start smoothly. A theoretically high-performance software package may not be the best choice for an entry-level project if installing it on the current server takes several days.
Extensibility
The final factor is extensibility. Many beginners initially only want to run a standard workflow, in which case extensibility may not seem important. But if your project eventually involves new algorithms, new potentials, new collective variables, special restraints, automated high-throughput workflows, coupling with machine-learning models, or embedding a simulation program into a larger software system, then extensibility becomes a very practical issue.
Extensibility first depends on whether the software itself provides clear extension entry points. Some software packages provide stable APIs, plugin mechanisms, or scripting interfaces, making it possible to extend functionality without heavily modifying the core code. Some software packages are open source, but their internal structure is complex and not easy to modify in practice. Some commercial software packages have friendly interfaces, but their underlying algorithms and core modules may not allow users to modify them freely. Here, one cannot only look at whether the software is open source. Code structure, documentation quality, test systems, compilation difficulty, and whether the developer community is willing to accept external contributions also matter.
Development styles differ greatly between software packages. One advantage of OpenMM is that its Python API is very natural, making it suitable for building customized simulation workflows. The modular design of LAMMPS is suitable for adding new interaction forms and materials simulation features. GROMACS and AMBER have mature ecosystems, but deeply modifying their core code usually requires a higher threshold. The advantage of SPONGE in this respect is that, starting from version 2.0, it places more emphasis on modular design and low-level hardware API abstraction: upper-level algorithm modules do not need to be tightly bound to specific hardware backends, making them easier to migrate to domestic CPU and domestic GPU platforms. At the same time, SPONGE also provides a Python interface, and some customized workflows can be handled in a way similar to OpenMM.
If you are only completing an entry-level exercise, extensibility can be placed relatively low in priority. But if you are designing a method, platform, or software package that needs to be maintained in the long term, then it should be considered early, just like licensing, hardware, and community. Otherwise, discovering late in the project that a core function cannot be extended, integrated, or redistributed compliantly can lead to very high rework costs.