Machine learning program for games helps develop algorithms for molecular modeling

Machine learning programs have paved the way for many research frontiers and developments. These programs work just like humans do, learning new techniques and skills using trial and error methods. The process involves repetition and reinforcement of learning, where good parts are selected for future use and bad consequences are avoided. Based on reinforcement learning, scientists at the US Department of Energy’s (DOE) Argonne National Laboratory have developed an algorithm that will help in modeling the properties of materials at the atomic and molecular level.

In a January 2022 article published in nature communicationScientists at the Argonne Center for Nanoscale Materials at DOE reported on the development of a reinforcement learning algorithm that could help speed up the process of material discovery. Through machine learning Algorithmically, the team aims to reduce the search time while obtaining high quality data.

“Our inspiration was AlphaGo, Having said Sukriti Manna, a research assistant at Argonne’s Center for Nanoscale Materials (CNM), said, “It is first computer program to defeat A world champion go player. ,

To test the algorithm, the team experimented with 54 elements in the periodic table. The algorithm learned the calculation of the force fields of the nanosized clusters for each element, and gave a successful performance within record time. The experimental calculations were also repeated on the alloys of the two elements. The complexity of these nanoclusters makes it difficult for scientists to use conventional methods to accurately model them.

“It’s akin to carrying out computations for many Ph.D.s. Do the research in a matter of days rather than in years,” said Rohit Batra, a CNM expert on data-driven and machine learning tools. And theoretical chemist Troy Loeffler believes this algorithm should help researchers tackle major challenges in many areas of materials science.

Previously, several artificial intelligence-based algorithms have been designed that use reinforcement learning. These algorithms help play games like chess and Go, automate chemical synthesis, and are even used for drug discovery.

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