首页|University of California Reports Findings in Machine Learning (Highly Accurate Prediction of NMR Chemical Shifts from Low- Level Quantum Mechanics Calculations Using Machine Learning)
University of California Reports Findings in Machine Learning (Highly Accurate Prediction of NMR Chemical Shifts from Low- Level Quantum Mechanics Calculations Using Machine Learning)
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New research on Machine Learning is the subject of a report. According to news reporting out of Berkeley, California, by NewsRx editors, research stated, “Theoretical predictions of NMR chemical shifts from first-principles can greatly facilitate experimental interpretation and structure identification of molecules in gas, solution, and solid-state phases. However, accurate prediction of chemical shifts using the gold-standard coupled cluster with singles, doubles, and perturbative triple excitations [CCSD(T)] method with a complete basis set (CBS) can be prohibitively expensive.” Our news journalists obtained a quote from the research from the University of California, “By contrast, machine learning (ML) methods offer inexpensive alternatives for chemical shift predictions but are hampered by generalization to molecules outside the original training set. Here, we propose several new ideas in ML of the chemical shift prediction for H, C, N, and O that first introduce a novel feature representation, based on the atomic chemical shielding tensors within a molecular environment using an inexpensive quantum mechanics (QM) method, and train it to predict NMR chemical shieldings of a high-level composite theory that approaches the accuracy of CCSD(T)/CBS. In addition, we train the ML model through a new progressive active learning workflow that reduces the total number of expensive high-level composite calculations required while allowing the model to continuously improve on unseen data. Furthermore, the algorithm provides an error estimation, signaling potential unreliability in predictions if the error is large. Finally, we introduce a novel approach to keep the rotational invariance of the features using tensor environment vectors (TEVs) that yields a ML model with the highest accuracy compared to a similar model using data augmentation.”
BerkeleyCaliforniaUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningPhysicsQuantum MechanicsQuantum Physics
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