Automation for scientific R&D

Self-driving labs for complex science.

Plexymer combines intelligent orchestration, robotic experimentation, and machine learning to build laboratories that learn—from biologic formulation to advanced materials.

Discuss an R&D program Explore the platform
Robotic liquid-handling workstation connected by a learning loop to antibody and polymer design inputs
Automation, experimental data, and model-guided selection operate as one learning system.

The Plexymer platform

A laboratory that learns from every experiment.

We connect experimental design, robotic execution, application-specific testing, structured data, and machine learning in one continuous loop.

Human-supervised at every stage; increasingly automated across the loop.

Explore the Design, Build, Test, and Learn automation loop
Four-stage scientific automation loop: molecular and experimental design, robotic build, analytical testing, and machine learning from structured data.
Select a stage in the image or the adjacent list to spotlight its role in the closed loop.

Applications

One platform. Two demanding R&D environments.

The same closed-loop architecture adapts to different experimental systems while preserving domain-specific assays, constraints, and scientific judgment.

Biologics & Formulation

Navigate coupled formulation objectives.

Automated formulation, characterization, and data analysis for challenging biologic products, including high-concentration antibody formulations.

  • High-concentration formulation exploration
  • Developability and stability studies
  • Material-efficient testing
  • Model-guided formulation selection

Polymers & Advanced Materials

Explore structure, process, and performance.

Automated workflows for synthesizing, characterizing, and modeling functional, water-soluble, and sustainable polymer materials.

  • Parallel polymer synthesis
  • Structure–property modeling
  • Multi-objective materials optimization
  • Active learning through chemical space

Team

Science, automation, and translation.

Built by scientists who work across robotic experimentation, machine learning, polymer chemistry, biologics, and enterprise R&D.

Matthew Tamasi

Matthew Tamasi, PhD

CEO & Co-Founder

Expert in self-driving laboratories and AI for material science and formulation. Building the platform.

Adam Gormley

Adam Gormley, PhD

Co-Founder

Associate Professor Biomedical Engineering Rutgers University. Leader on building self-driving labs.

Christopher Radford

Christopher Radford, PhD

Lead Scientist

Expertise in Formulation, AI, and Materials Science

Glenn Gormley

Glenn Gormley, MD PhD

Scientific Advisor

Former Head of R&D/Chair (Daiichi Sankyo), Global Head Clin Dev (Novartis), CMO (AstraZeneca).

Scientific foundations

Peer-reviewed research behind the platform.

Selected work underpinning Plexymer’s approach to automated experimentation, closed-loop learning, polymer chemistry, and biologic formulation.

Start a Program

What would you automate if every experiment became useful data?

Tell us about the R&D challenge, workflow, or decision you want to accelerate.

Let’s Work Together
01

Frame the objective

Define the target, constraints, experimental space, and measurable endpoint.

02

Automate the workflow

Configure synthesis or formulation, characterization, testing, and structured data capture.

03

Close the loop

Use experimental results and models to prioritize the next experiments and expand the program.

Self-driving labs for complex science.

Plexymer
303A College Road East
Princeton, NJ 08540

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