We introduce a Deep Reinforcement Learning(DRL)model for crystal structure relaxation and compare different types of neural network architectures and reinforcement learning algorithms for this purpose.Numerical experi...We introduce a Deep Reinforcement Learning(DRL)model for crystal structure relaxation and compare different types of neural network architectures and reinforcement learning algorithms for this purpose.Numerical experiments are conducted on Al-Fe structures,with potential energy surfaces generated using EAM potentials.We examine the influence of parameter settings on model performance and benchmark the best-performing models against classical optimization algorithms.Additionally,the model’s capacity to generalize learned interaction patterns from smaller atomic systems to more complex systems is assessed.The results demonstrate the potential of DRL models to enhance the efficiency of structure relaxation compared to classical optimizers.展开更多
1 Introduction Constrained Reinforcement Learning(CRL),modeled as a Constrained Markov Decision Process(CMDP)[1,2],is commonly used to address applications with security restrictions.Previous works[3]primarily focused...1 Introduction Constrained Reinforcement Learning(CRL),modeled as a Constrained Markov Decision Process(CMDP)[1,2],is commonly used to address applications with security restrictions.Previous works[3]primarily focused on the single-constraint issue,overlooking the more common multi-constraint setting which involves extensive computations and combinatorial optimization of multiple Lagrange multipliers.展开更多
基金supported by the Russian Science Foundation(grant#19-72-30043).The calculations were performed on the Zhores cluster at the Skolkovo Institute of Science and Technology.
文摘We introduce a Deep Reinforcement Learning(DRL)model for crystal structure relaxation and compare different types of neural network architectures and reinforcement learning algorithms for this purpose.Numerical experiments are conducted on Al-Fe structures,with potential energy surfaces generated using EAM potentials.We examine the influence of parameter settings on model performance and benchmark the best-performing models against classical optimization algorithms.Additionally,the model’s capacity to generalize learned interaction patterns from smaller atomic systems to more complex systems is assessed.The results demonstrate the potential of DRL models to enhance the efficiency of structure relaxation compared to classical optimizers.
基金supported by the Fundamental Research Funds for the Central Universities(No.2023JBZX011)the Aeronautical Science Foundation of China(No.202300010M5001).
文摘1 Introduction Constrained Reinforcement Learning(CRL),modeled as a Constrained Markov Decision Process(CMDP)[1,2],is commonly used to address applications with security restrictions.Previous works[3]primarily focused on the single-constraint issue,overlooking the more common multi-constraint setting which involves extensive computations and combinatorial optimization of multiple Lagrange multipliers.
