Sunthetics

Our Technology

Co-Founder & CEO of Sunthetics — the AI optimization platform that helps chemists reach breakthroughs in fewer experiments.

Why machine learning?

‍Optimizing chemical processes, reactions, or formulations is often a trial-and-error process guided by expert intuition. But when you’re dealing with many variables and complex goals, it’s easy to head in the wrong direction—wasting valuable time and resources.

Machine learning accelerates this process by revealing complex relationships between variables, predicting outcomes, and guiding you toward the most promising conditions—faster and with fewer experiments.

A platform technology

Our platform is reaction agnostic, meaning we can enter a variety of verticals in the chemical industry and have a large impact in a short amount of time. Whether it's pharmaceuticals, specialty chemicals, or anything in between, we can help accelerate innovation.

The platform is accessible via internet browser and doesn't require any machine learning knowledge from the user, making it truly easy-to-use.

What SuntheticsML does

SuntheticsML has been successfully implemented across a broad range of scientific fields and industries.

Applications include catalysis, electrochemistry, photochemistry, mechanochemistry, biocatalytic cascades, separation processes, analytical chemistry, enzymatic reactions, biologics, and both organic and inorganic chemistry. These implementations span industries such as pharmaceuticals, specialty and basic chemicals, food and beverages, advanced materials, cosmetics, personal care, and more.

Reaction understanding and engineering (e.g., modeling of reaction behavior, parameter effects and interactions)
Map how each variable influences your reaction outcomes — yield, selectivity, conversion — and uncover hidden interactions your team would never catch through manual experimentation. SuntheticsML builds a predictive model from your existing data, so you understand the chemistry before you run the next experiment.
Process characterization (e.g., prediction of failure zones, identification of safe operation ranges for manufacturing)
Define your design space with confidence. SuntheticsML identifies safe operating windows, predicts where processes are likely to fail, and surfaces the parameter boundaries that matter most for scale-up and manufacturing transfer — all without requiring a data science team.
Process development and optimization
Compress months of process development into weeks. SuntheticsML recommends the highest-value experiments to run next, visualizes high-dimensional parameter spaces, and quantifies the effect of each variable — so your team can lock in optimal conditions faster and with fewer runs.
Formulation optimization
Optimize complex multi-component formulations with fewer iterations. SuntheticsML models the relationship between ingredients, concentrations, and performance outcomes — enabling your team to collaborate in the platform, share campaign results, and reach target formulations without trial-and-error.
Analytical method development and optimization
Develop and optimize analytical methods in a fraction of the time. SuntheticsML recommends experiment conditions to maximize method sensitivity, selectivity, and robustness — replacing manual method scouting with data-driven guidance your full team can access and act on together.
Process scale-up (parameter tuning and optimization)
Translate lab-scale results to pilot and manufacturing scale with confidence. SuntheticsML models how process parameters shift across scales, recommends targeted experiments to close the gap, and helps your team identify the tuning conditions needed for a successful tech transfer.

Technology features

Process characterization
Unlocks unprecedented performance
No expertise in ML or statistics required
Only 5 data points required to begin analysis
Easy visualization and exploration of complex reaction trends
In small molecule development, crystallizations, biocatalysts, electrochemistry, catalytic processes, formulations, biologics, and more.
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Case studies

Electrochemical Reaction Optimization

Performance Improvement:
Sunthetics identified a 7% higher faradaic efficiency, optimizing flow rate, current density, and reactant concentration.
Data reduction:
Sunthetics' campaign used 75% less experiments, time, and resources.
Total number of experiments required: 9

Controlling aspect ratio of crystals

Performance Improvement:
Sunthetics identified a 13% better aspect ratio optimizing 5 crystallization variables.
Data reduction:
Sunthetics' campaign used 70% less experiments, time, and resources than the company's Design of Experiments.
Total number of experiments required: 5

Electrochemical Reaction Optimization

Performance Improvement:
Sunthetics identified a 40% higher fracture toughness in polymer double networks optimizing formulation and polymerization conditions.
Data reduction:
Sunthetics' campaign used 80% less experiments, time, and resources.
Total number of experiments required: 4

FAQ

How do I get started with SuntheticsML?
Do I need to write code or hire a data science team?
How is it different from traditional DoE?
What kinds of reactions does it work with?
How much data does it need?
What is SuntheticsML?