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Chemistry

The computational model accurately represents the hard-to-detect transitional phases of chemical reactions.

An MIT research team has developed an approach that quickly calculates the structure of transition states fundamental in chemical reactions - the fleeting and typically unobservable point that determines whether a reaction proceeds. This new machine learning-based model could assist in developing new reactions and catalysts for creating materials like fuels or drugs, and might…

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The computational model grasps the hard-to-capture transition phases of chemical reactions.

During a chemical reaction, molecules gain energy until they reach what is known as the transition state — a point at which the reaction must proceed. This state is extremely short-lived and nearly impossible to observe experimentally. Its structures can be calculated using quantum chemistry techniques, but these methods are very time-consuming. Recently, a team of…

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The computational model successfully records the difficult-to-detect transition phases of chemical reactions.

Chemical reactions reach a 'transition state' when molecules gain enough energy for the reaction to proceed. This state is brief and hard to observe experimentally. The arrangement of these transition states can be calculated through quantum chemistry, but it is highly time-consuming. Scientists at MIT have developed a faster method using machine learning which computes…

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Computational model successfully identifies the hard-to-catch transitional stages of chemical reactions.

A team of researchers from the Massachusetts Institute of Technology (MIT) has developed a machine learning model that can quickly calculate the structures of transition states in chemical reactions. These fleeting moments occur when molecules have gained enough energy to proceed with a reaction, but are notoriously difficult to study due to their ephemeral nature.…

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The computational model successfully encapsulates the hard-to-catch transition stages of chemical reactions.

During a chemical reaction, molecules move towards a transition state, a high-energy state that dictates how the reaction will proceed. However, this transition state is difficult to predict and observe due to its fleeting nature. Traditionally, scientists use quantum chemistry methods like density functional theory to evaluate these transition states, though these calculations tend to…

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Computational model successfully identifies the elusive transitional phases of chemical processes.

MIT researchers have developed an approach based on machine learning that can calculate transition states of chemical reactions within seconds. The structures of these transition states, a temporary condition in the middle of a chemical reaction, can typically only be calculated using techniques based on quantum chemistry – a process that can be extremely time-consuming.…

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A computational model successfully depicts the hard-to-capture transitional stages of chemical reactions.

A group of MIT researchers has developed a new machine learning model which rapidly calculates the structure of transition states during chemical reactions. This fleeting moment is a crucial "point of no return" in reactions. Although this transition state is vital to understanding the pathway of the reaction, it has been notoriously difficult to observe…

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The computational model successfully records the hard-to-capture transition stages of chemical reactions.

A team of researchers at the Massachusetts Institute of Technology (MIT) has developed a machine learning-based method to swiftly calculate the structures of transition states, crucial moments in chemical reactions. This state, at which molecules attain the necessary energy for a reaction, is important but fleetingly transient and difficult to experimentally observe. Calculating these structures…

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Engineers at MIT have devised a method to ascertain the behaviour of material surfaces.

A group of MIT researchers has developed a machine learning (ML) approach that could revolutionize the way we design catalysts for chemical reactions. The method simplifies the intricate process of designing new compounds or alloys, traditionally dependent on the intuition of experienced chemists, by using ML to provide more detailed information than conventional techniques can. The…

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Engineers at MIT have devised a method to ascertain the behavior of material surfaces.

Researchers from MIT have developed a machine learning approach that could replace the intuition-based methods typically used in the creation of catalysts. The team, led by graduate student Xiaochen Du, devised a system that offers more detailed insights than conventional techniques, identifying previously undiscovered atomic configurations in a material that had been researched for three…

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Engineers from MIT have devised a method to decipher the behavior of material surfaces.

MIT researchers have developed a machine learning-based method for designing new compounds or alloys for use as catalysts in chemical reactions. Traditional methods of designing such materials rely on static observations of a single configuration, out of millions of possibilities, and the intuition of experienced chemists. However, the new method employs machine learning algorithms to…

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