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image:INL researchers perform experiments using the Temporal Analysis of Products (TAP). view more

Credit: Idaho National Laboratory

If you follow the directionsin a cake recipe, you expect to end up with a nice fluffy cake.In Idaho Falls,though, the elevation can affecttheseresults.When baked goods dont turn outas expected, the troubleshooting begins.This happens in chemistry,too.Chemistsmustbeable to account for how subtle changes or additions may affect the outcome for better or worse.

Chemists maketheir version ofrecipes, known as reactions,to create specific materials.These materialsare essential ingredients to an array of products found in healthcare, farming, vehicles andother everyday productsfrom diapers to diesel.When chemists develop new materials, they rely on information from previous experiments and predictions based onpriorknowledge ofhowdifferent starting materials interact with others and behave underspecificconditions.There are a lot of assumptions, guesswork and experimentation in designing reactions using traditional methods.New computational methods like machine learning can help scientists better understand complex processes like chemical reactions.While it can be challenging forhumans topick outpatternshiddenwithin the data from many different experiments, computers excel at this task.

Machine learning isan advancedcomputational toolwhereprogrammers givecomputerslots ofdata andminimalinstructions about how to interpret it. Instead of incorporatinghuman bias into the analysis, the computer isonly instructed to pull out what it finds to be important from the data. This could be an image of a cat (if the input is all the photos on the internet) orinformation about how a chemical reactionproceeds through a series ofsteps, as is thecasefora set of machine learning experiments that are ongoing at Idaho National Laboratory.

At the lab,researchersworking with the innovative Temporal Analysis of Products (TAP)reactorsystemaretryingto improveunderstanding of chemical reactions by studying the role of catalysts,whicharecomponentsthat can be added toamixture of chemicals to alter thereactionprocess.Oftencatalystsspeed up thereaction,but they can do other things,too. In baking and brewing,enzymesact as catalyststo speed up fermentationandbreakdown sugars in wheat (glucose) into alcohol and carbon dioxide,which creates the bubbles that make bread riseand beer foam.

In the laboratory,perfectinga new catalystcan be expensive, time-consuming and even dangerous.According toINLresearcher Ross Kunz, Understanding how and why a specific catalyst behavesin a reaction is theholygrail ofreaction chemistry.To help find it,scientists arecombiningmachine learningwith a wealth of new sensor datafrom the TAP reactorsystem.

The TAP reactor system uses an array of microsensors to examine the different componentsof a reaction in realtime.For the simplestcatalytic reaction,the system captures8uniquemeasurementsin each of 5,000timepointsthat make up the experiment.Assembling the timepoints into a single data set provides 165,000 measurements foroneexperiment on a very simple catalyst.Scientiststhenuse the datatopredict what is happening in the reaction at a specific timeand how different reaction steps work together in a larger chemical reaction network.Traditional analysis methods canbarelyscratch the surfaceofsuch a large quantity of datafor a simple catalyst, let alonethe many more measurements thatare produced by acomplex one.

Machine learning methods can take theTAP dataanalysis further. Using a type of machine learning called explainableartificial intelligence, orAI,theteam caneducatethe computer about known properties of thereactionsstarting materialsand the physics that govern these types of reactions, a process called training.The computer can apply thistrainingand the patterns that it detects in the experimental data to better describe theconditions inareactionacross time.The team hopes that theexplainable AI method will produce adescription of the reaction that can be used toaccuratelymodelthe processes that occur during theTAP experiment.

In most AI experiments, a computer is given almost no trainingon the physicsand simply detects patterns in the data based upon what it can identify,similar tohow a baby might react to seeing something completely new.By contrast,the value of explainable AI lies in the fact that humanscan understand the assumptions and information that lead to the computers conclusions.This human-level understanding can make it easier for scientists to verify predictions and detect flaws and biases in the reaction description produced by explainable AI.

Implementing explainable AIis not as simple or straightforward as it might sound.With support from the Department of Energys Advanced Manufacturing office, theINLteam has spent two years preparing theTAPdata for machine learning,developing andimplementingthe machinelearning program, andvalidating the results for a common catalyst in a simple reaction that occursinthe car you driveeveryday. This reaction,the transformation of carbon monoxideinto carbon dioxide,occurs ina carscatalytic converter andrelies onplatinumasthe catalyst. Since this reaction is well studied,researcherscan checkhow well the results of the explainable AI experiments match known observations.

In April 2021, the INL team published their results validating the explainable AI method with the platinum catalyst in the article Data driven reaction mechanism estimation via transient kinetics and machine learninginChemical Engineering Journal.Now that the team has validated the approach, they are examining TAP data frommore complex industrialcatalystsused in the manufacture of smallmolecules like ethylene, propylene and ammonia. They are also working with collaborators at Georgia Institute of Technologyto applythemathematical models that result from themachine learningexperiments tocomputersimulationscalled digital twins. This type of simulation allows the scientists topredict what will happen if they change an aspectof the reaction. When a digital twin is based on avery accurate model of a reaction, researcherscanbe confident in itspredictions.

Bygivingthe digital twinthe taskto simulate a modification to a reaction or new type of catalyst, researchers can avoid doing physical experiments for modifications that are likely to lead to poor results or unsafe conditions. Instead,the digital twin simulation can savetime and moneyby testing thousands of conditions,while researchers can testonly a handful of the mostpromising conditions in the physical laboratory.

Plus, this machine learning approach can produce newer and more accurate modelsfor each new catalyst and reaction condition testedwith the TAP reactorsystem.In turn, applying these models to digital twin simulations gives researchers the predictive power to pick the best catalysts and conditions to test next in the TAP reaction. As a result, each roundof testing, model development and simulationproducesa greater understanding of how a reactionworksand howtoimprove it.

These toolsarethe foundation of a new paradigm incatalyst science but alsopave the way for radical new approaches inchemical manufacturing,said Rebecca Fushimi, who leads the project team.

About Idaho National LaboratoryBattelle Energy Alliance manages INL for the U.S. Department of Energys Office of Nuclear Energy. INL is the nations center for nuclear energy research and development,and alsoperforms research in each of DOEs strategic goal areas: energy, national security, science and the environment. For more information, visitwww.inl.gov. Follow us on social media:Twitter,Facebook,InstagramandLinkedIn.

Chemical Engineering Journal

Data driven reaction mechanism estimation via transient kinetics and machine learning

18-Apr-2021

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Learning to improve chemical reactions with artificial intelligence - EurekAlert