Researchers at MIT have made significant strides in the application of machine learning to predict chemical reactions and investigate material behaviors at their surfaces. The approach could aid in the development of catalysts, semiconductors, and battery components. The use of machine learning in predicting drug combination synergy and classifying inorganic perovskite materials for property prediction has also been reported in recent studies. Additionally, the integration of generative machine learning with crystal structure prediction and the acceleration of predicting inorganic surfaces using machine learning interatomic potentials have been explored in the field of computational chemistry.
Poisson-Boltzmann based machine learning (PBML) model for electrostatic analysis. (arXiv:2312.11482v1 [physics.chem-ph]) #machinelearning #compchem https://t.co/M9lOvTpLP9
Accelerating the prediction of inorganic surfaces with machine learning interatomic potentials. (arXiv:2312.11708v1 [cond-mat.mtrl-sci]) #machinelearning #compchem https://t.co/Q7tRhqokGZ
Integration of generative machine learning with the heuristic crystal structure prediction code FUSE #machinelearning #compchem https://t.co/r1pqmcIybe
Comparing the Catalytic Effect of Metals for Energetic Materials: Machine Learning Prediction of Adsorption Energies on Metals #machinelearning #compchem https://t.co/J4CfoDlolF
PotentialMind: Graph Convolutional Machine Learning Potential for Sb–Te Binary Compounds of Multiple Stoichiometries #machinelearning #compchem https://t.co/05PUiklzDo
MIT's Breakthrough in Chemical Reaction Prediction with Machine Learning #AI #artificialintelligence #catalysts #chemicalreactiontransitionstates #chemicalreactions #fuelindustry #llm #machinelearning #Machinelearningmodel #MIT #MITresearchers https://t.co/Q1GeSVxiGf https://t.co/G1LGEDeSbI
An inorganic ABX3 perovskite materials dataset for target property prediction and classification using machine learning. (arXiv:2312.11335v1 [cond-mat.mtrl-sci]) #machinelearning #compchem https://t.co/ljkdLin29r
A multi-task learning model for predicting drugs combination synergy by analyzing drug–drug interactions and integrated multi-view graph data - Scientific Reports https://t.co/Z1TlQtTbE4 #DL #AI #ML #DeepLearning #ArtificialIntelligence #MachineLearning #ComputerVision
🤖 From this week's issue: MIT researchers devised an ML-based method to investigate how materials behave at their surfaces. The approach could help in developing compounds or alloys for use as catalysts, semiconductors, or battery components. https://t.co/IwXl6GktW2
Transferable Machine Learning Interatomic Potential for Bond Dissociation Energy Prediction of Drug-like Molecules #machinelearning #compchem https://t.co/tU4Cuj1HzA