Outsmarting Quantum Chemistry Through Transfer Learning
By Justin S. Smith, B. Nebgen, Roman I. Zubatyuk, Nicholas Lubbers, Christian Devereux, Kipton Barros, Sergei Tretiak, Olexandr Isayev, Adrian E. Roitberg. 2018
Computer simulations are foundational to theoretical chemistry. Quantum-mechanical (QM) methods provide the highest accuracy for simulating molecules but have difficulty scaling to large systems. Empirical interatomic potentials (classical force fields) are scalable, but lack transferability to new systems and are hard to systematically improve. Automated, data-driven machine learning is close to achieving the best of both approaches. Here we use transfer learning to retrain a general purpose neural network potential, ANI-1x, on a dataset of gold standard QM calculations (CCSD(T)/CBS level) that is relatively small but designed to optimally span chemical space. The resulting potential, ANI-1ccx, approaches CCSD(T)/CBS accuracy on benchmarks for reaction thermochemistry, isomerization, and drug-like molecular torsions. ANI-1ccx is broadly applicable to materials science, biology and chemistry, and billions of times faster than the parent CCSD(T)/CBS calculations.
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Citations in the corpus
- Machine learning for molecular simulation (2019, methodology): “While Behler-Parrinello networks have mainly been used to make predictions of the same molecular system in order to run MD simulations unaffordable by direct ab-initio QM MD [52], ANI has been trained on DFT and coupled-cluster data across a large chemical space [11, 104, 12].”