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This year's Nobel Prizes in Chemistry and Physics are linked to AI and Machine Learning (ML), with a focus on deep learning models, which consist of computational units called neurons, including FFNN, CNN, and RNN/LSTM.* All branches of engineering, particularly mining and mineral processing, should emphasize these subjects significantly in their curricula, as they represent the future of engineering. The pace of digitalization in mining and mineral processing is notably slow, and having more qualified graduates in this area will accelerate progress in the field. #NobelPrize #chemistry #physics ======================================= *: FFNN: FeedForward Neural Network is commonly used in recognizing patterns in data and then classifying them. Used in supervised learning. CNN: Convolutional Neural Network is commonly used for processing images and it is the model used for facial recognition. RNN/LSTM: Recurrent Neural Network and Long Short-Term Memory which is used for data that has a sequence, for example, language translation.

Deep learning reveals molecular secrets of explosive perchlorate salts . Perchlorate compounds are known for their explosive nature. To understand what makes these compounds so explosive, a team of researchers developed a novel deep learning-based method that analyses their crystal structure and molecular interactions to elucidate their physical properties. This novel technique avoids dangerous laboratory-based experiments and uses data to study the nature of compounds. Overall, the study marks a significant step towards data-driven and artificial intelligence-based methods for chemical research. #ScienceDailynews #InnovativeResearch #NextGenScience #ExploringFrontiers

#Materials #News Tokyo university finds way to use deep learning to predict ‘exciting materials’: The method identifies magnetic materials solely based on their crystal structure, eliminating the need for time-consuming experiments and simulations Single-molecule magnets (SMMs) are exciting materials. In a recent breakthrough, researchers from Tokyo University of Science have used deep learning to predict SMMs from 20,000 metal complexes. The predictions were made solely based on the crystal […]

“Sharing is Caring” Hi #AI Community!!! “#Knowledge Booster” 📚 Expand your knowledge base with this helpful article..Keep #Learning !! 👍 NOTE : This is NOT a paid Advertisement #Robotics #artificialintelligence #dataanalytics #datascience #machinelearning #deeplearning #linkedinfamily #knowledgesharing #machinelearningalgorithms #neuralnetworks #deeplearning #deeplearningai #computervision Join me on my way into an exciting world of Data/Analytics/AI 🍁Let’s Connect Now

Prof. John Hopfield and Prof. Geoffrey Hinton have been awarded the Nobel Prize for Physics for their groundbreaking contributions to artificial intelligence (AI). This recognition highlights the significance of AI not just in technology but also in shaping the future of industries and academia. Their work, which dates back to the 1980s, laid the groundwork for today's AI systems that are implemented in various fields. Their foundational discoveries in artificial neural networks, which mimic the biological wiring of the human brain, have revolutionized the way machines process information. AI technology would not have been possible without the discoveries of these human-machine interactions. Businesses that leverage AI technologies stand to gain significantly. For educators and researchers, this is a call to integrate AI more deeply into curricula and academic inquiry. Academia will need to prepare students for an AI-driven future by updating courses to combine traditional disciplines with AI literacy. #AI #NobelPrize #Innovation #Business #Academia #MachineLearning #IILMLodhiRoad

“Why did AI researchers get the Nobel Prize in physics and chemistry?” That was the first reaction of most people, even some fellow researchers. In reality, we're seeing how interdisciplinary approaches are accelerating discoveries across fields. How do natural sciences inspire AI? 🔸 Strong mathematical foundations 🔸 First principles thinking 🔸 Symmetries and phase transitions 🔸 Concepts like diffusion being reinvented in ML How does AI advance physics and chemistry? 🔹 Simulations and predictive modeling  🔹 Big data analysis in complex systems 🔹 Accelerated discovery by automation These are now underlined by this week’s awards. 🧮 Congratulations to Geoffrey Hinton and John Hopfield, awarded for their groundbreaking work in neural networks. Their research bridged physics and AI, laying the foundation for today's deep learning revolution. 🧬 Congratulations to the Chemistry Nobel winners as well, split between two teams: 1. Demis Hassabis and John Jumper (DeepMind) for AlphaFold2, which solved the 50-year-old protein folding problem. 2. David Baker for his work on computational protein design, enabling the creation of novel proteins with new functions. We have followed and admired the laureates’ work for a long time. It’s not an overstatement to say: That their contributions have now been recognized in such a major way is an inspiration and a genuine joy for everyone working in the field of artificial intelligence.

Happily sharing my contribution to a review article entitled "Machine Learning Advancements in Organic Synthesis: A Focused Exploration of Artificial Intelligence Applications in Chemistry" during my stay at ECNU, China as visiting scholar. #ecnu #unicam #organicchemistry #reviewpaper #artificialintelligence #machinelearning #ai

Accelerating the discovery of single-molecule magnets with deep learning: Single-molecule magnets (SMMs) are exciting materials. In a recent breakthrough, researchers have used deep learning to predict SMMs from 20,000 metal complexes. The predictions were made solely based on the crystal structures of these metal complexes, thus eliminating the need for time-consuming experiments and complex simulations. As a result, this method is expected to accelerate the development of functional materials, especially for high-density memory and quantum computing devices. #ScienceDaily #Technology

In Issue 14 of The CATALYST, learn how computers can distinguish solid chemicals by using something called an artificial neural network! How does the neural network developed at ICReDD compare to the eye of an experienced chemist? Click below to find out. https://lnkd.in/giCzataf #scicomm #chemistry #neuralnetworks

AI revolutionizes material science! Scientists use AI to create unique "fingerprints" for materials, allowing for better prediction and design. Want to know more? Read the full story now!👉🏻https://bit.ly/4cHVGP2 #ai #machinelearning #materialscience #materialfingerprint #scientificdiscovery #innovation #nanotechnology #engineering #chemistry #physics #materialdesign #development #analysis #science #technology #hostingdailynews