Our Materials Informatics Pipeline
An integrated computational workflow from nanostructure design to property prediction, safety assessment, and Safe and Sustainable by Design (SSbD) optimisation
Digital Nanostructure Construction
Build nanoparticles, nanotubes, nanosheets, and crystal structures in silico using our web-based tools. Specify material composition, geometry (spherical, ellipsoidal, tubular), chirality, and dimensions through intuitive interfaces — no coding required.
Energy Minimization & Stability Analysis
Optimize nanostructure geometries through molecular dynamics energy minimization using LAMMPS with configurable force fields (OPLS-AA, CHARMM, ReaxFF, AMBER). Identify stable configurations and preferential crystal growth directions.
Atomistic Descriptor Calculation
Automatically extract 30+ structural and energetic descriptors — from potential energy distributions and coordination numbers to hexatic order parameters and lattice energies — providing the feature space for machine learning models.
Machine Learning & Property Prediction
Feed atomistic descriptors into validated ML models (Random Forest, XGBoost, LightGBM) for property prediction. OECD-compliant QSAR/QSPR models with SHAP-based interpretability and applicability domain analysis.
Safety Assessment & Nanotoxicology
Predict cytotoxicity, protein corona formation, and biological interactions using nanoinformatics models. Assess nanoparticle safety before synthesis, supporting safe-by-design and Safe and Sustainable by Design (SSbD) approaches for regulatory readiness and sustainable innovation.
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The platforms and frameworks behind our materials informatics tools
Enalos Cloud Platform
All molecular builders and simulation tools are deployed on the Enalos Cloud Platform — NovaMechanics' FAIR-compliant infrastructure for scientific web applications. Each tool provides user-friendly interfaces and REST APIs for integration into computational workflows.
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Isalos Analytics Platform
End-to-end workflow automation for data manipulation, machine learning model development, and predictive analytics. Isalos powers the QSAR/QSPR models and atomistic descriptor-enriched ML pipelines for materials property prediction — no programming required.
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Real-world applications demonstrating our materials modelling capabilities
ASCOT: Digital Construction of Energy-Minimized Spherical Nanoparticles
Automated web tool for digital construction and atomistic descriptor calculation of Ag, CuO, and TiO₂ spherical NPs, integrating LAMMPS with OpenKIM for nanosafety model development.
Read Case StudyNanoConstruct: Ellipsoidal NP Builder for Crystal Growth Investigation
Digital construction of ellipsoidal NPs from any material via CIF upload, with ZrO₂ crystal growth case study revealing preferential growth directions in rutile-like structures.
Read Case StudyNanoTube Construct: Digital Construction of Single-Layer Nanotubes
Web tool for building nanotubes from 16+ single-layer materials including graphene, MoS₂, and silicene, with energy minimization, chirality control, and atomistic descriptors.
Read Case StudyUANanoDock: Multiscale Protein–Nanoparticle Nanodocking
Web-based UnitedAtom nanodocking tool predicting protein adsorption orientations and binding energies on NP surfaces, with IgG immunoassay design demonstration.
Read Case StudyEasy-MODA: Standardised Simulation Workflow Documentation
First automated tool for generating CEN CWA 17284:2018-compliant MODA documentation, improving FAIRness and reproducibility of multiscale materials simulations.
Read Case StudyHydroNanoConstruct: Hydrated Metal Oxide NP Construction
Digital construction of realistic hydrated metal oxide nanoparticles in water, with surface tension calculation, crystal growth investigation, and atomistic descriptor extraction.
Read Case StudyPredictive Nanotoxicology & Safe and Sustainable by Design (SSbD)
Enabling SSbD-oriented safety assessment before synthesis through explainable AI
NovaMechanics has developed a comprehensive nanoinformatics workflow for predicting nanoparticle cytotoxicity. By combining atomistic-level structural descriptors with evidence-based experimental features, our OECD-compliant machine learning models achieve high predictive accuracy for cell viability outcomes.
The models incorporate explainable AI techniques (SHAP analysis, Partial Dependence Plots) to identify which structural and physicochemical properties drive toxicity — enabling researchers to make informed design decisions early in the development process.