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School of Science

There is potential in deep neural networks as they could effectively serve as models for human auditory perception.

A new MIT study has found that computational models derived from machine learning that mimic the structure and function of the human auditory system could help improve the design of hearing aids, cochlear implants, and brain-machine interfaces. Its research explored deep neural networks trained to perform auditory tasks and showed that these models generate internal…

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The computational model accurately identifies the hard-to-detect transition stages of chemical reactions.

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…

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Computational model accurately identifies the hard-to-detect transitional stages of chemical reactions.

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…

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

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…

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The potential of deep neural networks as models for human auditory perception is quite promising.

A recent study from MIT has shown that computational models that mimic the structure and function of the human auditory system could significantly aid research into more sophisticated hearing aids, cochlear implants, and brain-machine interfaces. Modern computational models that use machine learning have already made progress in this area. The MIT team carried out the…

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A computational model successfully grasps the hard-to-detect transitional phases of chemical reactions.

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…

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Deep neural networks demonstrate potential in simulating human auditory perception.

A new study from MIT reveals that modern computational models based on machine learning, which mimic the structure and function of the human auditory system, are coming closer to potentially aiding the design of improved hearing aids, cochlear implants, and brain-machine interfaces. The MIT team’s research is the most extensive to date on deep neural networks,…

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A computational model successfully records the hard-to-track transitional phases of chemical reactions.

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…

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Deep neural networks exhibit potential for modelling human auditory processes.

A new study from MIT suggests that modern computational models powered by machine learning could potentially aid the design of better hearing aids, cochlear implants, and brain-machine interfaces. These models, specifically deep neural networks, are starting to encompass functions that replicate the structure of the human auditory system.  The study further illuminates how to best train…

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The computational model successfully encapsulates the hard-to-capture transitional stages of chemical reactions.

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…

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Human hearing can potentially be modeled effectively by deep neural networks.

MIT researchers have conducted the largest study to date of deep neural networks trained for auditory tasks. These computational models, which mimic the structure and function of the human auditory system, have the potential to improve hearing aids, cochlear implants, and brain-machine interfaces. The study shows that the majority of the models generate representations which…

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