Our materials informatics and nanoinformatics pipeline combines multiscale modeling, atomistic descriptor engineering, and machine learning to accelerate materials discovery, nanoparticle design, safety assessment, and Safe and Sustainable by Design (SSbD) decision-making. 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. 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. 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.
