Multiscale Modeling and Simulation Platform for Materials and Life Sciences
J-OCTA
Multiscale Modeling and Simulation Platform for Materials and Life Sciences
J-OCTA
Outstanding Features and Services of J-OCTA
J-OCTA is a multiscale simulation software platform that supports a wide range of applications—from materials science fields such as polymer materials, composite materials, energy materials, electronics, and functional thin films/coatings, to life science fields such as drug discovery and formulation, and biomaterials that bridge both domains. It predicts structures and properties from the atomistic/molecular scale to the micrometer scale, helping to understand complex phenomena and generate new design guidelines. Simulators can be linked on a common platform, and integration with machine learning and data science for materials informatics is also available. This enables greater accuracy and efficiency in research and development, accelerating innovation in both materials science and life sciences. With extensive adoption and robust support, J-OCTA can be used with confidence by both first-time users and experienced researchers.
Feature Highlights
- Simulation for Materials Science
- Simulation for Life Sciences
- Materials Informatics
- Support and Consulting Services
Simulation for Materials Science
In materials design, understanding and controlling multiscale structures—from the nano to the macro level—is essential. J-OCTA is an integrated simulation platform that spans from quantum theory to continuum theory, corresponding to each scale. It employs full-atomistic models, coarse-grained models, and continuum models to support everything from predicting structures and properties to process design. It enables evaluation of a wide range of physical properties, including mechanical, thermal, electrical, and optical characteristics, and allows multiscale coupling as well as integration with data science. With a wealth of case studies and tutorials, even first-time users can confidently get started, while researchers and engineers can deepen their understanding of structures and behaviors and apply these insights to design and development.
Full-Atomistic Modeling
From chemical structure input to generating 3D molecular structures, performing electronic state calculations for charge assignment, modeling diverse polymer conformations and phase separation, to simulating reactions such as surface modification of inorganic crystals and crosslinking—J-OCTA enables intuitive and efficient construction of full-atomistic models. It comes with a general-purpose force field for full-atomistic MD that can handle organic molecules, inorganic crystals, and interfaces, and also supports parameter tuning via first-principles calculations. The platform provides a multiscale modeling environment through mapping from coarse-grained models and coupling with other scales. Flexible modeling and high-throughput simulations are also supported via Python scripting.
Full-Atomistic Modeling
From chemical structure input to generating 3D molecular structures, performing electronic state calculations for charge assignment, modeling diverse polymer conformations and phase separation, to simulating reactions such as surface modification of inorganic crystals and crosslinking—J-OCTA enables intuitive and efficient construction of full-atomistic models. It comes with a general-purpose force field for full-atomistic MD that can handle organic molecules, inorganic crystals, and interfaces, and also supports parameter tuning via first-principles calculations. The platform provides a multiscale modeling environment through mapping from coarse-grained models and coupling with other scales. Flexible modeling and high-throughput simulations are also supported via Python scripting.
Amorphous Structure of Polymers
Inorganic–Organic Interface Structure
Coarse-Grained Modeling
Capturing mesoscale (tens to hundreds of nanometers) structural formation and emergence of properties—which are prominent in polymeric materials—requires the use of coarse-grained models that group multiple atoms into a single unit. Using coarse-grained molecular dynamics (CGMD), dissipative particle dynamics (DPD), mean-field methods, and reptation dynamics based on polymer tube models, J-OCTA enables the analysis of polymer entanglement structures, phase separation processes, and rheological properties. It supports estimation of coarse-grained potentials and Flory–Huggins χ parameters, as well as graphical modeling of coarse-grained molecular structures. Both the application of general parameter sets and bottom-up approaches using information from full-atomistic models are available.
Coarse-Grained Modeling
Capturing mesoscale (tens to hundreds of nanometers) structural formation and emergence of properties—which are prominent in polymeric materials—requires the use of coarse-grained models that group multiple atoms into a single unit. Using coarse-grained molecular dynamics (CGMD), dissipative particle dynamics (DPD), mean-field methods, and reptation dynamics based on polymer tube models, J-OCTA enables the analysis of polymer entanglement structures, phase separation processes, and rheological properties. It supports estimation of coarse-grained potentials and Flory–Huggins χ parameters, as well as graphical modeling of coarse-grained molecular structures. Both the application of general parameter sets and bottom-up approaches using information from full-atomistic models are available.
Coarse-Grained Molecular Dynamics
Phase Separation of Polymer Electrolytes and Water
Micromechanics, Microscale Fluid Dynamics, and Process Simulation
By using the representative volume element (RVE) modeler, it is possible to import phase-separated structures obtained from mean-field method or dissipative particle dynamics (DPD), or to create virtual filler dispersion structures with complex geometries. These can be converted into mesh data of the microstructure for finite element analysis (FEA) using Muffin. For more advanced nonlinear analyses, an interface with Ansys LS-DYNA is also available. Furthermore, by using the particle-based fluid, powder, and heat transfer analysis engine VSOP-PS, it is possible to investigate process behaviors for microscopic structures such as battery slurries or composite resins. This capability is well-suited for assisting the early design stages of material manufacturing processes.
Micromechanics, Microscale Fluid Dynamics, and Process Simulation
By using the representative volume element (RVE) modeler, it is possible to import phase-separated structures obtained from mean-field method or dissipative particle dynamics (DPD), or to create virtual filler dispersion structures with complex geometries. These can be converted into mesh data of the microstructure for finite element analysis (FEA) using Muffin. For more advanced nonlinear analyses, an interface with Ansys LS-DYNA is also available. Furthermore, by using the particle-based fluid, powder, and heat transfer analysis engine VSOP-PS, it is possible to investigate process behaviors for microscopic structures such as battery slurries or composite resins. This capability is well-suited for assisting the early design stages of material manufacturing processes.
Stress distribution in phase-separated structure under deformation
Powder compression process
Case study
- Simulation of phase separation process in polymeric membranes
- Calculation of pressure and porosity in the forming process (calendaring) of battery electrodes
- Simulation of evaporation of liquid film
- Thermal conductivity calculations for filler resin composites
- Nonlinear Mechanical Properties of Composites (Linked with Ansys LS-DYNA)
Wide Range of Simulation Engines
For full-atomistic and coarse-grained molecular dynamics, users can choose from COGNAC, VSOP, GENESIS, LAMMPS, GROMACS, and HOOMD-blue. By switching according to the time step, it is possible to leverage each engine’s strengths—such as computational speed or specialized functions—for optimal performance. For coarse-grained models, available tools include SUSHI (mean-field method), PASTA and NAPLES (rheology analysis), MUFFIN (microscale fluid dynamics and mechanics), and VSOP-PS (particle method). For quantum chemistry and first-principles calculations, SIESTA, ABINIT-MP, MOPAC, Gaussian, and Firefly can be executed. Cross-scale coupling is also supported, enabling the platform to meet diverse simulation needs.
Wide Range of Simulation Engines
For full-atomistic and coarse-grained molecular dynamics, users can choose from COGNAC, VSOP, GENESIS, LAMMPS, GROMACS, and HOOMD-blue. By switching according to the time step, it is possible to leverage each engine’s strengths—such as computational speed or specialized functions—for optimal performance. For coarse-grained models, available tools include SUSHI (mean-field method), PASTA and NAPLES (rheology analysis), MUFFIN (microscale fluid dynamics and mechanics), and VSOP-PS (particle method). For quantum chemistry and first-principles calculations, SIESTA, ABINIT-MP, MOPAC, Gaussian, and Firefly can be executed. Cross-scale coupling is also supported, enabling the platform to meet diverse simulation needs.
Analysis of Simulation Results and Property Estimation
A wide range of physical quantities can be analyzed from simulation results, including electronic states and conductivity, intermolecular interactions, molecular geometry, polymer chain entanglement, free volume distribution, phase-separated structures, interfacial energy, stress relaxation, as well as diffusion and heat flow. Various property evaluations in thermal, mechanical, optical, and electrical domains can be performed easily with intuitive operations. Rich visualization capabilities allow even beginners to generate persuasive images effortlessly. The system also supports large-scale computations using supercomputers, enabling the rendering of models containing several million to tens of millions of particles.
Analysis of Simulation Results and Property Estimation
A wide range of physical quantities can be analyzed from simulation results, including electronic states and conductivity, intermolecular interactions, molecular geometry, polymer chain entanglement, free volume distribution, phase-separated structures, interfacial energy, stress relaxation, as well as diffusion and heat flow. Various property evaluations in thermal, mechanical, optical, and electrical domains can be performed easily with intuitive operations. Rich visualization capabilities allow even beginners to generate persuasive images effortlessly. The system also supports large-scale computations using supercomputers, enabling the rendering of models containing several million to tens of millions of particles.
Trajectory of water molecules in zeolite
Free volume in amorphous polymer
Data Science Integration
By leveraging the modeling API function, the entire workflow—from generating input files for MD simulations, to executing the calculations, to analyzing the results—can be automated. This enables efficient execution of high-throughput simulations, with the resulting data stored in a database (DB). In materials informatics, where data scarcity is often a challenge, J-OCTA allows the construction of simulation-derived datasets that include not only physical property values but also molecular structural descriptors. In addition, it features the MD-GAN capability, which can estimate long-timescale behavior from short MD trajectories.
Data Science Integration
By leveraging the modeling API function, the entire workflow—from generating input files for MD simulations, to executing the calculations, to analyzing the results—can be automated. This enables efficient execution of high-throughput simulations, with the resulting data stored in a database (DB). In materials informatics, where data scarcity is often a challenge, J-OCTA allows the construction of simulation-derived datasets that include not only physical property values but also molecular structural descriptors. In addition, it features the MD-GAN capability, which can estimate long-timescale behavior from short MD trajectories.
High-throughput simulation
Long-timescale molecular dynamics prediction using MD-GAN
Simulation for Life Science
J-OCTA leverages the technologies cultivated in materials science to deliver powerful capabilities for the life sciences as well. In pharmaceutical formulation design, it supports the evaluation of lipid nanoparticles (LNP) for drug delivery systems (DDS), solid dispersions, and drug solubility. In biomaterials, it enables property prediction based on the molecular structures of polysaccharides such as cellulose and of lipids, along with the evaluation of biocompatibility and interfacial activity. In drug discovery, it enables high-accuracy exploration of protein–ligand binding structures and evaluation of binding affinity. J-OCTA is also advancing integration with AI technologies, continuing to evolve as a platform that supports the entire workflow from structure prediction to simulation.
Full-Atomistic Modeling
In addition to generating three-dimensional structures from chemical formulas and performing electronic state analyses, J-OCTA enables efficient and intuitive construction of full-atomistic models for drug molecules, additives, protein structures in aqueous environments, and interfaces with surface-modified inorganic particles. It provides general-purpose force fields capable of handling organic, inorganic, and interfacial systems for molecular dynamics engines such as GENESIS and GROMACS, with the option to refine parameters through first-principles calculations. J-OCTA also supports reverse mapping from coarse-grained models to full-atomistic models, as well as coupling with other scales, providing a multiscale modeling environment applicable to drug discovery and formulation processes. Automation via Python and high-throughput structure generation are also supported.
Full-Atomistic Modeling
In addition to generating three-dimensional structures from chemical formulas and performing electronic state analyses, J-OCTA enables efficient and intuitive construction of full-atomistic models for drug molecules, additives, protein structures in aqueous environments, and interfaces with surface-modified inorganic particles. It provides general-purpose force fields capable of handling organic, inorganic, and interfacial systems for molecular dynamics engines such as GENESIS and GROMACS, with the option to refine parameters through first-principles calculations. J-OCTA also supports reverse mapping from coarse-grained models to full-atomistic models, as well as coupling with other scales, providing a multiscale modeling environment applicable to drug discovery and formulation processes. Automation via Python and high-throughput structure generation are also supported.
Protein and ions in water
Aqueous solution near a substrate
Cellulose
External link
Coarse-Grained Modeling
To address mesoscale (tens to hundreds of nanometers) structure formation and functional manifestation, coarse-grained models—where multiple atoms are grouped into a single unit—are highly effective. Using coarse-grained molecular dynamics (CGMD), dissipative particle dynamics (DPD), and mean-field methods, J-OCTA can analyze phenomena such as phase separation and dispersion states of polymers and lipid membranes, as well as their rheological properties. It also supports analysis of lipid nanoparticles (LNPs), cell membranes, and organelle-like structures formed via liquid–liquid phase separation. J-OCTA enables estimation of coarse-grained potentials, configuration of MARTINI parameters, and bottom-up design in conjunction with full-atomistic models.
Coarse-Grained Modeling
To address mesoscale (tens to hundreds of nanometers) structure formation and functional manifestation, coarse-grained models—where multiple atoms are grouped into a single unit—are highly effective. Using coarse-grained molecular dynamics (CGMD), dissipative particle dynamics (DPD), and mean-field methods, J-OCTA can analyze phenomena such as phase separation and dispersion states of polymers and lipid membranes, as well as their rheological properties. It also supports analysis of lipid nanoparticles (LNPs), cell membranes, and organelle-like structures formed via liquid–liquid phase separation. J-OCTA enables estimation of coarse-grained potentials, configuration of MARTINI parameters, and bottom-up design in conjunction with full-atomistic models.
Lipid bilayer containing cholesterol
Lipid nanoparticles
Micelle structure of surfactants
Microscale Fluid Dynamics and Process Simulation
For fluid analysis at the micrometer scale, J-OCTA offers particle methods such as MPS and mean-field methods to evaluate the flow behavior and properties of complex dispersed systems including drug slurries, colloidal suspensions, gels, and emulsions. Application targets include assessing mixing, separation, concentration distributions, and interfacial behavior within microfluidic chips, as well as optimizing formulation processes and biomaterial preparation for systems such as cell suspensions and polymer solutions.
Microscale Fluid Dynamics and Process Simulation
For fluid analysis at the micrometer scale, J-OCTA offers particle methods such as MPS and mean-field methods to evaluate the flow behavior and properties of complex dispersed systems including drug slurries, colloidal suspensions, gels, and emulsions. Application targets include assessing mixing, separation, concentration distributions, and interfacial behavior within microfluidic chips, as well as optimizing formulation processes and biomaterial preparation for systems such as cell suspensions and polymer solutions.
Concentration distribution of components generated by a chemical reaction on a microfluidics chip
Case study
External link
Wide Range of Simulation Engines
For full-atomistic and coarse-grained molecular dynamics, you can choose from COGNAC, VSOP, GENESIS, GROMACS, and LAMMPS. By switching according to the time step, simulations can take advantage of each engine’s strengths, such as GPU-accelerated high-speed computation and specialized features. For other coarse-grained models, SUSHI (mean-field method) is available, while ABINIT-MP, MOPAC, Gaussian, and Firefly can be used for quantum chemical calculations. Interoperability across different scales is also supported, enabling the platform to address a wide range of needs.
Analysis of Simulation Results
For targets such as proteins, drug molecules and additives, lipid membranes, and biomaterials, it is possible to analyze a wide range of physical quantities, including intermolecular interactions, aggregation structures, diffusion coefficients, distributions of free volume and hydrophobicity/hydrophilicity, phase-separated structures, and interfacial properties. It also supports free energy evaluation and molecular position/orientation analysis, making it applicable to lipid nanoparticles (LNP) and formulation design. Intuitive operations allow for easy visualization and analysis, even for beginners, and it is capable of rendering and analyzing large-scale systems using supercomputers.
Materials Informatics
We offer the data science capabilities necessary for materials informatics in a dedicated software package called MI-Suite. By connecting J-OCTA’s simulation technologies with data science techniques, it supports data-driven materials design.
MI-Suite
Using machine learning–based QSPR (Quantitative Structure–Property Relationships), material properties can be rapidly predicted from molecular or inorganic crystal structures. Various molecular descriptors can be calculated from SMILES notation, including predictions of intramolecular charge distributions (σ-profiles). By leveraging these as features, efficient and highly accurate property prediction becomes possible. The system also supports integration with pre-trained libraries and public databases, as well as the use of user-provided data. Additionally, it offers inverse analysis capabilities to predict molecular structures from target properties.
MI-Suite
Using machine learning–based QSPR (Quantitative Structure–Property Relationships), material properties can be rapidly predicted from molecular or inorganic crystal structures. Various molecular descriptors can be calculated from SMILES notation, including predictions of intramolecular charge distributions (σ-profiles). By leveraging these as features, efficient and highly accurate property prediction becomes possible. The system also supports integration with pre-trained libraries and public databases, as well as the use of user-provided data. Additionally, it offers inverse analysis capabilities to predict molecular structures from target properties.
Molecular charge distribution (σ-profile)
SHAP plot visualizing the influence of each feature on the prediction results
Case Study Database, Support, and Consulting Services
In many cases, molecular simulations require advanced knowledge in multi-scale and multi-physics approaches, and often involve building hypothetical microscopic structures whose actual states are unknown. This can make the work challenging for beginners. However, by utilizing J-OCTA’s case study database (DB), tutorials, and scenario functions, users can proceed with confidence. The scenario function allows operation procedures to be visualized as flowcharts for reuse, helping to prevent operation errors, facilitate team sharing, and enable batch processing of large numbers of input files.
Extensive Case Study Collection and Scenario Function
By using J-OCTA’s scenario function to visualize analysis procedures as flowcharts and record/reuse calculation conditions and operations, it becomes possible to efficiently apply them to different materials and share them within a team. J-OCTA’s case study DB includes several preconfigured analysis flows, allowing users to immediately start simulations for property evaluation.
Extensive Case Study Collection and Scenario Function
By using J-OCTA’s scenario function to visualize analysis procedures as flowcharts and record/reuse calculation conditions and operations, it becomes possible to efficiently apply them to different materials and share them within a team. J-OCTA’s case study DB includes several preconfigured analysis flows, allowing users to immediately start simulations for property evaluation.
Scenario function for tracing analysis procedures
Support and Consulting Services
With a support contract, dedicated JSOL staff provide assistance during the initial implementation phase and respond to inquiries via email, as well as offer various seminars covering topics from basic theory to software operation. We also provide consulting services for multiscale simulation and materials informatics using J-OCTA, covering everything from case study research to model construction, computation, analysis, and reporting. Customization is also available. Please feel free to contact us.
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J-OCTA is the commercial version of the integrated simulator for soft materials, called "OCTA," that was developed through an industry-university cooperative project. OCTA is open-source software.
See more details for OCTA >> [https://octa.jp/] - ※
SIESTA is open source Density Functional Theory (DFT) software developed by a group including Universities mainly in Spain. SIESTA is a registered trade-mark of SIMUNE.
SIMUNE Website >> [https://www.simuneatomistics.com/]
SIESTA Open Source Software Website >> [https://siesta-project.org/siesta/] - ※
GENESIS is molecular dynamics software developed mainly by RIKEN and distributed as free software (LGPLv3).
GENESIS Website >>[https://mdgenesis.org/]
Reference:
[1] C. Kobayashi, J. Jung, Y. Matsunaga, T. Mori, T. Ando, K. Tamura, M. Kamiya, and Y. Sugita, J. Compute. Chem. 38, 2193-2206 (2017).
[http://dx.doi.org/10.1002/jcc.24874]
[2] J. Jung, T. Mori, C. Kobayashi, Y. Matsunaga, T. Yoda, M. Feig, and Y. Sugita, WIREs Comput. Mol. Sci., 5, 310-323 (2015).
[http://dx.doi.org/10.1002/wcms.1220]
[3] J.Jung, K.Yagi, C.Tan, H.Oshima, T.Mori, I.Yu, Y.Matsunaga, C.Kobayashi, S.Ito, D.Ugarte La Torre, Y.Sugita, J. Phys. Chem. B 128, 25, 6028-6048 (2024).
[https://doi.org/10.1021/acs.jpcb.4c02096] - ※
References for the fragment molecular orbital calculation engine ABINIT-MP:
S. Tanaka, Y. Mochizuki, Y. Komeiji, Y. Okiyama, K. Fukuzawa, Phys. Chem. Chem. Phys. 16 (2014) 10310-10344 [https://doi.org/10.1039/C4CP00316K]
References for FCEWS (FMO-DPD):
K. Okuwaki, Y. Mochizuki, H. Doi, T. Ozawa, J. Phys. Chem. B, 122 (2018) 338-347[https://doi.org/10.1021/acs.jpcb.7b08461]
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