most citedQuantum Chemistry Driven Molecular Inverse Design with Data-free Reinforcement Learning

1 citations · 1 across the 2 of their papers we have counts for

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2 papers

cs.LG2025

Complexity-Regularized Proximal Policy Optimization

Luca Serfilippi, Giorgio Franceschelli, Antonio Corradi +1

Policy gradient methods usually rely on entropy regularization to prevent premature convergence. However, maximizing entropy indiscriminately pushes the policy towards a uniform di…

physics.chem-ph2025★ 1 cited

Quantum Chemistry Driven Molecular Inverse Design with Data-free Reinforcement Learning

Francesco Calcagno, Luca Serfilippi, Giorgio Franceschelli +3

The inverse design of molecules has challenged chemists for decades. In the past years, machine learning and artificial intelligence have emerged as new tools to generate molecules…