Robots in Chemistry: Finding Reactions That Fail

Updated 7 November 2026 · Standard formula, shown below

Automated chemistry platforms can systematically test hundreds of reaction conditions and identify which combinations fail, saving researchers time and revealing unexpected limitations. This guide explains how robotic systems are used to probe reaction space and flag unsuccessful outcomes, and what you need to set up such an experiment.

What You’ll Need

A liquid-handling robot or automated synthesis platform (e.g., Chemspeed, Unchained Labs, or a custom-built system)
Reagents and solvents appropriate for your reaction
Reaction vessels (e.g., vials, well plates) compatible with your robot
An analytical method to assess reaction outcome (e.g., HPLC, GC, LC-MS, or TLC with a camera)
Software to control the robot and record data (often provided by the instrument vendor)
A data analysis tool (e.g., Python, R, or Excel) to process results
Basic programming skills (optional but helpful for custom scripts)

Step-by-Step

Define the reaction space. List the variables you want to explore: catalysts, ligands, bases, solvents, temperature, time, stoichiometry, and concentration. Decide on a range for each variable (e.g., three different bases, two solvents, five temperatures). The product of these choices is your total number of experiments.
Design the experiment matrix. Use a design-of-experiments (DoE) approach to select a representative subset of conditions rather than testing every possible combination. Software like JMP or Python’s DoE libraries can generate a balanced set of runs that still covers the space efficiently.
Program the robot. Write a script that instructs the robot to dispense the correct volumes of reagents and solvents into each vessel, in the order you specify. Include steps for mixing, heating, cooling, and sampling. Ensure the script includes a “dummy” run to test the liquid handling accuracy.
Prepare stock solutions. Make concentrated stock solutions of each reagent in a solvent that is compatible with your robot’s tubing and pipettes. This reduces the number of dispense steps and improves reproducibility.
Run the automated experiments. Load the robot with the stock solutions, vessels, and any consumables. Start the run and monitor it for errors (e.g., clogs, spills, or sensor alarms). Most robots allow you to pause and correct issues without losing the run.
Collect analytical data. After the reactions are complete, analyze each vessel using your chosen method. For high-throughput, you might use a plate reader or rapid LC-MS. Record the raw data (e.g., peak areas, conversion percentages) in a structured file.
Identify failures. Define a threshold for success (e.g., >50% product yield or >90% conversion). Any run that falls below the threshold is a “failure.” List these conditions and look for patterns—for example, all failures might share a specific solvent or temperature.
Validate the failures. Pick a few representative failed conditions and repeat them manually or on the robot to confirm they are reproducible. This step is crucial because robotic errors can cause false negatives.
Analyze and report. Use data analysis tools to visualize the results (e.g., heatmaps, scatter plots). Summarize the failed conditions and any structural or chemical reasons that might explain them, such as reagent incompatibility or thermal decomposition.

Common Mistakes

Overlooking solvent compatibility: Some solvents (e.g., DMSO) can dissolve plastics or cause tubing swelling. Always test your solvent with the robot’s wetted parts.
Ignoring evaporation: In open vessels, solvents may evaporate during long runs, changing concentrations. Use sealed vials or add a solvent replenishment step.
Setting an arbitrary success threshold: A 10% yield might be a success for a challenging reaction. Define your threshold based on the reaction’s known difficulty.
Not including replicates: Without replicates, you cannot distinguish random variation from true failure. Include at least one duplicate for a subset of conditions.
Poor calibration: If the robot’s pipette calibration is off, volumes will be wrong, leading to false failures. Calibrate before each run.

Tips

Start with a small pilot run (e.g., 24 reactions) to debug your workflow before scaling up.
Use a robotic arm with a gripper to move vessels between stations if your platform supports it.
Incorporate internal standards in your analytical method to correct for injection volume variations.
Store your data in a consistent format (e.g., CSV) with clear column names to make analysis easier.
Consider using machine learning to predict failures based on your data, but only after you have collected a robust dataset.

Troubleshooting

If the robot stops mid-run: Check the error log. Common causes are a clogged tip, an empty reservoir, or a sensor misalignment. Clear the issue, then resume or restart from the last completed step.
If you see inconsistent results: Verify that the stock solutions are homogeneous (some reagents precipitate). Also check the robot’s pipetting accuracy by running a dye test.
If analytical data is missing for some wells: The robot may have skipped a vessel due to a sensor error. Review the run log and re-run those specific conditions.
If the robot cannot handle viscous liquids: Dilute the stock solutions or use a positive-displacement pipette if available.
If the reaction fails even in control experiments: Your reagent quality or storage might be the issue. Run a manual control with fresh reagents to confirm.

Frequently Asked Questions

How many reactions can a robot test in a day?

Depends on the platform and reaction time. A simple liquid-handling robot can set up hundreds of reactions per hour, but if each reaction needs hours of heating, the total throughput is limited by the number of parallel reaction blocks.

Can a robot tell me why a reaction fails?

No, a robot only provides data. The chemist must interpret the results to hypothesize why certain conditions fail, often using additional analytical data or computational models.

Do I need to know programming to use a reaction robot?

Many commercial systems come with graphical software that lets you design runs without code. However, basic scripting skills help for custom workflows and data analysis.

What is the cost of setting up such a system?

Costs vary widely: a used liquid handler might start around $50,000, while a full high-throughput synthesis platform can exceed $500,000. Leasing is an option for short-term projects.

How do I avoid false positives (reactions that appear successful but aren’t)?

Always confirm with a secondary analytical method (e.g., NMR) and run a control without catalyst to ensure the product is not formed non-catalytically.

Can this method be used for any type of reaction?

Yes, as long as the reaction can be performed in a vial or well plate and analyzed by a compatible method. Solid-state reactions or those requiring high pressure are more challenging but possible with specialized equipment.

Method: the standard formula for this calculation, rounded as each result states. See how the calculators work or report an error.