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U.S. Study Identifies ML Framework to Increase Productivity

Technology Abstract with Futuristic Lines as Art

Researchers from the Brookhaven National Laboratory of the US Department of Energy have developed a novel machine learning (ML) framework that can pinpoint which phases of a multistep chemical reaction can be adjusted to boost productivity.

The method could aid in the development of catalysts, which are chemical “dealmakers” that speed up reactions. It was created to investigate the conversion of carbon monoxide (CO) to methanol using a copper-based catalyst.

The reaction is made up of seven relatively simple elementary steps and was used as an example by the researchers in their ML framework method. The goal was to determine which elementary step or subset of steps in the reaction network controls the catalytic activity.

Traditionally, researchers attempting to improve such a reaction would calculate how changing each activation barrier one at a time might affect the overall production rate. This type of analysis could determine which steps were “rate-limiting” and which steps determined reaction selectivity—that is, whether the reactants proceeded to the desired product or via an alternate pathway to an undesirable by-product.

The new machine learning framework is intended to improve these estimates so that scientists can more accurately predict how catalysts will affect reaction mechanisms and chemical output. The scientists began by collecting data to train their machine learning model. The data set was created using “density functional theory” (DFT) calculations of the activation energy required to transform one atom arrangement to the next over the course of the reaction’s seven steps.

The scientists then used computer simulations to see what would happen if they changed all seven activation barriers at the same time – some going up, some going down, some individually, and come in pairs.

They generated a comprehensive dataset of 500 data points by simulating variations in 28 “descriptors,” which included the activation energies for the seven steps as well as pairs of steps changing two at a time. This dataset forecasted how individual and pairwise tweaks would affect methanol production. The model then ranked the 28 descriptors in terms of their significance in driving methanol output.

The scientists retrained the ML model using only the six “active” descriptors after identifying the important descriptors. Based solely on DFT calculations for those six parameters, this improved ML model was able to predict catalytic activity.

The model, according to the team, can also be used to screen catalysts. The model predicts a maximum methanol production rate if it can design a catalyst that improves the value of the six active descriptors.

When the researchers compared their model predictions to the experimental performance of their catalyst—as well as the performance of alloys of various metals with copper—the predictions matched the experimental findings. Comparisons of the ML approach to the previous method for predicting alloy performance revealed that the ML method was far superior.

The data also revealed a lot about how changes in energy barriers might affect the reaction mechanism. The interaction of the various steps of the reaction was of particular interest—and importance. For example, the data showed that lowering the energy barrier in the rate-limiting step alone would not improve methanol production in some cases. However, increasing methanol output by adjusting the energy barrier of a step earlier in the reaction network while keeping the activation energy of the rate-limiting step within an ideal range.

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Qlik’s vision is a data-literate world, where everyone can use data and analytics to improve decision-making and solve their most challenging problems. A private company, Qlik offers real-time data integration and analytics solutions, powered by Qlik Cloud, to close the gaps between data, insights and action. By transforming data into Active Intelligence, businesses can drive better decisions, improve revenue and profitability, and optimize customer relationships. Qlik serves more than 38,000 active customers in over 100 countries.

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Planview has one mission: to build the future of connected work. Our solutions enable organizations to connect the business from ideas to impact, empowering companies to accelerate the achievement of what matters most. Planview’s full spectrum of Portfolio Management and Work Management solutions creates an organizational focus on the strategic outcomes that matter and empowers teams to deliver their best work, no matter how they work. The comprehensive Planview platform and enterprise success model enables customers to deliver innovative, competitive products, services, and customer experiences. Headquartered in Austin, Texas, with locations around the world, Planview has more than 1,300 employees supporting 4,500 customers and 2.6 million users worldwide. For more information, visit www.planview.com.

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SIRIM is a premier industrial research and technology organisation in Malaysia, wholly-owned by the Minister​ of Finance Incorporated. With over forty years of experience and expertise, SIRIM is mandated as the machinery for research and technology development, and the national champion of quality. SIRIM has always played a major role in the development of the country’s private sector. By tapping into our expertise and knowledge base, we focus on developing new technologies and improvements in the manufacturing, technology and services sectors. We nurture Small Medium Enterprises (SME) growth with solutions for technology penetration and upgrading, making it an ideal technology partner for SMEs.

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HashiCorp provides infrastructure automation software for multi-cloud environments, enabling enterprises to unlock a common cloud operating model to provision, secure, connect, and run any application on any infrastructure. HashiCorp tools allow organizations to deliver applications faster by helping enterprises transition from manual processes and ITIL practices to self-service automation and DevOps practices. 

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IBM is a leading global hybrid cloud and AI, and consulting services provider, helping clients in more than 175 countries capitalize on insights from their data, streamline business processes, reduce costs and gain the competitive edge in their industries. Nearly 3,800 government and corporate entities in critical infrastructure areas such as financial services, telecommunications and healthcare rely on IBM’s hybrid cloud platform and Red Hat OpenShift to affect their digital transformations quickly, efficiently, and securely. IBM’s breakthrough innovations in AI, quantum computing, industry-specific cloud solutions and business services deliver open and flexible options to our clients. All of this is backed by IBM’s legendary commitment to trust, transparency, responsibility, inclusivity, and service. For more information, visit www.ibm.com