Chemical engineering

Researchers from MIT have used machine learning to expedite the calculation of transient molecular states that occur during chemical reactions. The team's innovative new model streamlines the process, from a previously time-consuming task, performed using quantum chemistry techniques, to a few seconds. Applied, it could assist chemists to design new reactions and catalysts to create…

The process of identifying the fleeting chemical transition states that occur during reactions could be significantly sped up thanks to a machine learning system developed by researchers from MIT. At present, these states can be calculated using quantum chemistry, but this process is time and computing power intensive, often taking days to calculate a single…

Scientists from the Massachusetts Institute of Technology have used machine learning to expedite calculations of transition states in chemical reactions, a process that could support the invention of new reactions and catalysts with applications in fuels, pharmaceuticals and understanding the origins of life. Using a method known as density functional theory to compute transition states…

Scientists at MIT have developed a machine learning-based technique for rapidly calculating the transition state of a chemical reaction, a step that was previously extremely time-consuming using traditional quantum chemistry methods. The transition state is a crucial yet fleeting phase in any reaction, marking the point where molecules have gained enough energy for a reaction…

A team of MIT scientists has developed a machine learning-based model to calculate transition states during chemical reactions, a process which normally requires quantum computing and can take hours or even days to complete. Transition states, which inevitably occur during reactions when molecules reach a particular energy threshold, were previously calculated through quantum chemistry’s density…

MIT researchers have developed a machine learning-based technique that can rapidly calculate the structures of fleeting transition states during chemical reactions. Identifying and understanding these quasi-instantaneous moments, when molecules have collected enough energy to proceed with reaction, is crucial to fields such as catalyst design and natural system research. With traditional quantum chemistry-based techniques, it…

In a breakthrough study at MIT, researchers have used machine learning (ML) to calculate the ephemeral transition state in chemical reactions, representing a significant step forward for computational chemistry. The transition state occurs when molecules in a reaction gain energy to the point where the reaction becomes irreversible. Researchers have struggled to observe this pivotal…

Scientists at MIT have devised a machine learning-based method that can rapidly calculate the transitional states of molecules during a chemical reaction. The transient nature of these states has made observation particularly challenging. Understanding these states is key to developing catalysts or deciphering how natural systems induce specific changes. The MIT team constructed their computational approach…

During a chemical reaction, molecules gain energy until they reach a point known as the transition state, a pivotal moment where the reaction must proceed. The structures of these states can be determined using quantum chemistry methods, but these calculations are time-intensive. To tackle this issue, a team of MIT researchers developed a machine learning-based…

During a chemical reaction, molecules gain energy until they reach a position termed the transition state. Existing at a level of energy where the reaction has no choice but to proceed, the transition state's transient nature makes it extremely difficult to observe experimentally. Its structures can be calculated using quantum chemistry-based techniques, but these are…