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.
The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules
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Adaptive MFML algorithm saturates accuracy at low fidelities before escalating, cutting data costs up to 30x vs single-fidelity and 5x vs standard MFML on coupled cluster and excitation energies.
Proposes generative pseudo-force fields trained on quadratic pseudo-potentials from noisy equilibria as a time-step-agnostic diffusion variant for efficient molecular conformation generation with high validity on QM9.
A neural network LDA functional overfit to water data achieves 1 kcal/mol errors on ionization and atomization energies and matches PBE/B3LYP on WATER27 binding energies after transfer learning from one datum.
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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.