How to Use Crystal Relaxation
Commercially Available Online Web Server
Optimize periodic crystal geometries with ML potentials or PBE DFT
Crystal Relaxation minimizes the predicted potential energy of an ordered bulk inorganic crystal. Upload a CIF and choose atomic-position relaxation with a fixed lattice, or simultaneous atom and cell optimization at the selected external pressure. Choose one of eight methods: MACE-MPA-0 medium, MACE-MP-0b3 medium, conservative Orb-v3 OMat, MatterSim 5M, SevenNet-MF-ompa (mpa), Quantum ESPRESSO PBE, CP2K PBE, or ABINIT PBE. ML potentials use fixed server-selected checkpoints. Quantum ESPRESSO and ABINIT use CC-BY-4.0 PseudoDojo PBE scalar-relativistic standard potentials; CP2K uses its GTH-PBE and DZVP-MOLOPT data. DFT supports at most 128 atoms.
How Crystal Relaxation Works
Each selected engine supplies energy, forces, and stress. ML potentials approximate their respective reference datasets. DFT methods evaluate neutral PBE calculations with the selected spin initialization, optional two-body D3(BJ) correction, occupations, and a fixed starting k-point mesh; no Hubbard U, spin-orbit coupling, or magnetic-state search is included. Initial moments are specified per element; antiferromagnetic ordering within an element is unsupported. For plane-wave engines, the requested cutoff is raised to at least the highest PseudoDojo high-accuracy recommendation among the elements. CP2K uses a configurable density grid, default 600 Ry. Automatic optimizer selection uses FIRE for ML potentials and BFGS for DFT; Advanced Settings can override this with either optimizer. For cell relaxation, the optimizer adjusts the lattice through a cell filter. Cell convergence requires the projected force and stress imbalance in allowed atomic and cell directions to meet their tolerances. Target pressure is included in the stress imbalance; shape-preserving relaxation uses its mean normal component. Fixed-cell convergence requires only the force criterion.
CIF symmetry is expanded into the supplied cell before optimization. No primitive-cell conversion is imposed. Symmetry preservation, cell constraints, and frozen atoms are optional Advanced Settings. Symmetry is detected from the starting geometry and may refine small deviations within the selected tolerance; it cannot be combined with frozen atoms or XY/Z-only strain. Partial occupancies and mixed-species sites must be resolved into an ordered structure before submission. Cell heights must be at least 2 Å; lattice-vector lengths must be at most 200 Å.
The relaxed geometry is a local minimum candidate of the selected energy model. It is not proof of synthesizability or an energy-above-hull result. ML-potential results are not DFT calculations. Energies should not be mixed directly with results from other methods or used as Materials Project reference energies. A spin-polarized calculation uses one chosen element-level initialization and does not search magnetic states. D3(BJ), when selected for DFT, adds two-body dispersion consistently to energies, forces, and stress.
What is Neurosnap?
Neurosnap is the leading platform for bioinformatics and computational science focused on expanding access to powerful modeling and simulation tools. Because many state-of-the-art machine learning systems remain complex to install, configure, and scale, Neurosnap offers a clean, browser-based workspace that removes the burden of infrastructure management, dependency conflicts, and command-line tooling.
Built for biologists, chemists, and cross-disciplinary scientists, the platform enables advanced computational workflows without requiring expertise in software engineering or cloud architecture. Researchers can launch analyses through an intuitive interface, connect programmatically through a comprehensive API, and rely on automated resource management to scale workloads efficiently. By taking care of the underlying compute and operational complexity, Neurosnap allows teams to devote their energy to scientific progress and faster iteration. Security and data protection remain foundational principles, with clear safeguards outlined in our Terms of Use and Privacy Policy to ensure your work stays protected.
Advancing Discovery with Crystal Relaxation on Neurosnap
Using Crystal Relaxation on Neurosnap could drastically accelerate periodic inorganic crystal geometry preparation.
- Direct crystal input: Expand CIF symmetry without converting the crystal into a protein representation.
- Explicit convergence: Inspect physical forces and stress alongside the returned geometry.
- Reusable outputs: Carry the relaxed CIF into subsequent materials calculations.
How to Use Crystal Relaxation on Neurosnap
To harness the capabilities of Crystal Relaxation, researchers can follow this streamlined workflow within Neurosnap:
- Access Neurosnap: Start by logging in to the Neurosnap website.
- Select Tool: From the list of available tools, choose Crystal Relaxation.
- Provide Inputs: Provide all the inputs specified within the submission panel and optionally configure the tool as desired.
- Run Tool: Submit the Crystal Relaxation job and Neurosnap will execute it in the cloud, automatically notifying you as soon as your results are ready.
- Review Output: Explore your results through rich visualizations, including figures, plots, and interactive views designed to help you analyze findings with clarity and confidence.
Citations
Please cite the original work when using Crystal Relaxation in publications or research outputs.
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Grimme, S., Ehrlich, S. and Goerigk, L. Effect of the damping function in dispersion corrected density functional theory. Journal of Computational Chemistry 32, 1456-1465 (2011). https://doi.org/10.1002/jcc.21759. |
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Orbital Materials. Orb-v3: atomistic simulation at scale. https://arxiv.org/abs/2504.06231. |
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Microsoft Research. MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures. https://arxiv.org/abs/2405.04967. |
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Kim, J. et al. Data-Efficient Multifidelity Training for High-Fidelity Machine Learning Interatomic Potentials. https://doi.org/10.1021/jacs.4c14455. |
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Quantum ESPRESSO project. https://www.quantum-espresso.org. |
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CP2K Developers Group. CP2K quantum chemistry and solid-state physics software. https://www.cp2k.org. |
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ABINIT developers. ABINIT electronic-structure software. https://www.abinit.org. |
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Batatia, I. et al. A foundation model for atomistic materials chemistry. arXiv:2401.00096. https://arxiv.org/abs/2401.00096. |
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van Setten, M. J. et al. The PseudoDojo: Training and grading a 85 element optimized norm-conserving pseudopotential table. https://doi.org/10.1016/j.cpc.2018.01.012. Pseudopotentials are distributed under CC BY 4.0. |
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Larsen, A. H. et al. The atomic simulation environment—a Python library for working with atoms. Journal of Physics: Condensed Matter 29, 273002 (2017). https://doi.org/10.1088/1361-648X/aa680e. |
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Neurosnap Inc. (2022). Neurosnap: An online platform for computational biology and chemistry. Available at: https://neurosnap.ai/ |
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