Linear meta-learning surrogates trained across chemical objectives and auxiliary properties adapt rapidly to new multi-objective molecular searches and outperform baselines by 78% in Pareto performance on spin-crossover complexes.
Hierarchical modeling of molecular energies using a deep neural network
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Interpretable Meta-Learning for Multi-Objective Chemical Search
Linear meta-learning surrogates trained across chemical objectives and auxiliary properties adapt rapidly to new multi-objective molecular searches and outperform baselines by 78% in Pareto performance on spin-crossover complexes.