Dr. Rishab Handa
Principal R&D Scientist for Advanced Materials & Molecular AI @ ecopals
About
Bridging physics, chemistry, and Molecular AI to accelerate sustainable materials innovation. I lead a €3M+ portfolio across 9 industry partners to deliver next-generation materials for construction, packaging, paints, and medical applications. As the inventor of Virtual Lab® and creator of Molecular AI®, I build platforms that integrate physics-informed simulations (LAMMPS/DFT/CFD) with AI/ML for inverse materials design. These tools enable autonomous structure–property optimization, in-silico testing, and rapid discovery of sustainable polymers, composites, adhesives, and reactive binders. Expertise (Experimental, Theory & Simulations): • Polymer physics, rheology, nonlinear fluid dynamics, polymer chemistry, microfluidics • Compatibilization, reactive extrusion, recycled plastics, phase separation, granular matter • Bio-elastomers, nanocomposites, coatings, adhesives, thin films, colloids • AI/ML for materials design (PyTorch, Keras, Tensorflow, MatSciBERT, GnOME) • Regulatory & compliance (PFAS, REACH, RoHS, ISO/ASTM) • Cloud-deployed scientific software (GitLab, Azure, Docker) I also co-author scientific publications and book chapters in green & supramolecular chemistry, biosensing, bio-elastomers and rheophysics through collaborations with CIQA (Mexico), Saarland University, and the University of Luxembourg. I’m driven by a deep curiosity for complex systems, how emergent order and self-organization shape materials, phase transitions, machines, and markets.
Germany
Berlin
Renewables & Environment
Physics-informed Simulations & Molecular AI/ML, Polymer Physics, Rheology & Nonlinear Mechanics, R&D Leadership, Commercialization & IP Strategy, Advanced Materials & Sustainable Polymer Engineering, Computational Physics and Molecular Modeling, Cross-functional Team Leadership, Rheophysics and Nanomechanics, Interdisciplinary Project Management, Sustainable Process Engineering, Material Science and Process Optimization, Granular Rheology and Flow Optimization, Powder Handling and Processing, Data Science and Project Leadership, Analytical and Experimental Techniques, Cheminformatics, Rheology and Nanomechanics, Scientific Advisor, Polymer Chemistry, Chemical Synthesis, Computational Physics and Molecular Machine Learning
Experience

Principal R&D Scientist for Advanced Materials & Molecular AI
Berlin, Germany
Spearheading Molecular AI, a physics-informed machine learning platform serving as a closed-loop discovery engine for inverse material design and optimization of next-generation polyolefins, elastomers, and functional additives. The system integrates CUDA-accelerated LAMMPS simulations with convolutional and graph neural networks to predict and tune rheological, structural, and physicochemical properties with high precision. In parallel, I lead external outreach and ecosystem building for Virtual Lab, successfully securing multiple LOIs with companies across the construction, packaging, and chemical industries, positioning the platform as a modular, scalable solution for data-driven materials design. Developing reactive extrusion and cryogenic processing protocols to address incompatibility and phase separation in the production of HFFR recycled polyolefin-elastomer blends (including product validation under ASTM/ISO standards. Guiding cross-functional collaboration between R&D, product, and supply chain teams using data-driven material informatics, achieving a 15% reduction in production costs and 70% in product batch non-conformity while saving over 10k tonnes of CO2 annually. Approving all scientific legal documentation under multiple regulatory compliances (REACH, POPs, RoHS), and providing technical guidance to the CEOs on IP & patent strategy. Overseeing advanced experimental design (DoE), materials characterization, and raw material selection to ensure continuous innovation, scalability, and reproducibility in product development.

Research Scientist
Berlin, Germany
Discovered two functional additives via NEMD molecular simulations and quantum chemical modeling, now used as viscoelasticity enhancers in industrial construction materials. Designed a computational–experimental framework to optimize the chemical composition of recycled plastics and additives, enabling the development of new materials with improved durability and processability. This innovation led to annual savings of €33,000 and 5,000 labour hours in material testing, and ~15k tonnes of material waste. Technologies: Multi-scale simulations (CUDA-LAMMPS, DFT), cheminformatics (MOSCED, UNIFAC, CALPHAD), and advanced characterization (rheometry, MS-GC, SEC, XRF, FTIR, DSC). Additional roles: Represented Ecopals GmbH in academic–industrial collaborations Coordinated IP/patent activities Managed cross-functional R&D projects Led data-driven formulation design and cost optimization

Scientific Consultant
Germany
Average project duration: 4-6 months -Directed blood rheology experiments for Neurodegenerative diagnostics (Biophysics, UniSaar) -Engineered a Rheo-microscope to study complex fluid interfaces (Biophysics, UniSaar). -Trained engineers on the rheology of metallic powders at CERATIZIT (Luxembourg). -Optimized extrusion-induced microfibrillation of bio-composites at 12% lower energy cost. -Designed advanced rotating drum set-up for powder segregation (Liege University). -Trained PhDs in "handling particulate solids for compounding oral dosage forms" ((LIBio, Lorraine)). -Designed formulations to "optimize lactose powder flows in drugs production." (LIBio, Lorraine).

Doctoral Researcher
Saarbrücken, Saarland, Germany
Developed theoretical models and experimental setups to study the nonlinear fluid dynamics and multi-scale (micro-meso-macro) structure-property relationship of complex materials, such as granular matter, glasses, powders, polymers, foams and colloidal suspensions. Designed state-of-the-art experimental setups to resolve common challenges in granular rheometry, specifically addressing phenomena like jamming, segregation, and compaction in dry and wet granulation. Tech.: Rheometers (MARS, CaBER, MCR, Tube, DSR), Tensiometers, Particle Image Velocimetry (PIV), Confocal- and Rheo-Microscope, Numerical Simulations, Glass Physics, Thermodynamics and Statistical Physics.
Education

Condensed Matter Physics
Thesis: Non-linear rheology of granular matter. *Established an experimental framework on the soft glassy rheology of rapid granular and powder flows. *Developed a constitutive model to capture the non-linear structure-property relationships across various length scales for densely driven granular matter, powders, and polymers, unified by a non-thermodynamic temperature.
Dr. Rishab Handa's Contact Information
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