Every performance claim in IntelliForm traces back to validated research. Our methodology is peer-reviewed, openly available, and actively expanding — from Bayesian formulation optimization to QSAR biodegradability prediction.
NewNow published in RSC Digital Discovery · June 2026 →AI-driven optimization in industrial chemistry faces a challenge benchmark studies rarely address: handling the combinatorial complexity, competing objectives, and high evaluation cost of real-world formulation problems. This paper presents a surrogate-assisted multi-objective optimization framework built to carry formulation intelligence from benchmark performance to production deployment at industrial scale.
Surrogate-assisted multi-objective optimization framework for industrial chemical formulation — addressing combinatorial complexity, competing objectives, and evaluation cost at production scale. Published 23 June 2026.
Connects ExtraTrees-based ML molecular screening to physics-based spacecraft qualification — outgassing (NASA MSFC-SPEC-1238), radiation stability, and thermal cycling — in a single pipeline for space-grade organic photovoltaic candidates. Freely available on Hugging Face.
IntelliForm's three-engine optimization core — LP baseline (PuLP/CBC), constrained NSGA-III, and multi-objective Bayesian optimization (qNEHVI via BoTorch) — exposing the full trade-off across cost, bio-based %, performance, and EcoMetrics™ sustainability. Introduces sparse-prior population seeding, which restores NSGA-III feasibility on ingredient pools above ~150 candidates. Also benchmarks GNN QSAR/QSPR (5-fold CV R² up to 0.93), CertificationOracle™, an ISO 14067 carbon-credit calculator, and conformal-prediction shelf-life modeling.
Two-stage surrogate-assisted NSGA-III pipeline, EcoMetrics sustainability scoring (ρ = 0.83 vs. 47 ISO 14040 datasets), QSAR biodegradability R² = 0.89, Circular Formulation Engine. ~8,000× optimization speedup.
Examines how AI, smart sensors, and digital twins are transforming chemical manufacturing. Presents case studies in process optimization, predictive maintenance, and sustainable production scale-up using integrated AI-equipment systems.
Explores the convergence of AI and chemistry for sustainable industrial innovation — from natural language interfaces to autonomous formulation systems. Presents a framework for responsible AI deployment in specialty chemical production.
Presents a framework for AI-mediated specialty chemical procurement, enabling SMEs to access low minimum order quantities through intelligent supplier matching, automated COA/SDS verification, and REACH compliance screening.
Explores how integrating AI, green chemistry, and circular economy innovation can accelerate sustainable transformation in the chemical industry, reviewing 2024–2025 advances such as AI-driven catalyst design.
Examines how specialty chemical supply chains can respond to growing environmental pressure through circular chemistry practices and smart, data-driven sourcing.
Accessible overview of the path from laboratory formulation research to commercial green chemistry products — covering scale-up challenges, regulatory hurdles, and the role of AI in accelerating go-to-market timelines.
NSGA-III surrogate-assisted optimization for simultaneous cost, performance, and sustainability objectives. Bayesian Gaussian process models as surrogates for computationally expensive property evaluations.
Machine learning models for quantitative structure-activity relationships in specialty chemicals. 1,613 Mordred molecular descriptors combined with ensemble regression for property prediction from SMILES strings.
Automated carbon footprint calculation and sustainability scoring aligned with ISO 14040/14044. EcoMetrics validation against industry LCA datasets and integration of Scope 1/2/3 emissions into formulation optimization.
Knowledge graph architectures for retaining institutional formulation knowledge across R&D cycles. Negative knowledge capture — encoding what NOT to formulate — from real experimental failure data.
Multi-model AI architectures for autonomous chemical formulation. Natural language parsing, regulatory compliance verification, and consensus intelligence across specialized chemistry AI models.
Computational approaches to formulating materials for space, aerospace, and hypersonic applications. Outgassing prediction, radiation dose tolerance modeling, and cryogenic property estimation from molecular structure.
Newark, NJ · Tech MBA Program
STEM for Success Showcase
Smart Manufacturing research collaboration
Chicago, IL · NSF-START Program
CIMSEPP IUCRC
Pharmaceutical manufacturing · UIC Indigo repository
Interested in a research collaboration? We actively seek partnerships with academic institutions, national laboratories, and industry research groups working at the intersection of AI and chemistry. Contact us →