mlsanityData to 14 Oct 2026

StriderNET: A Graph Reinforcement Learning Approach to Optimize Atomic Structures on Rough Energy Landscapes

arXiv 2301.12477 ↗First posted 29 Jan 2023cs.LG cond-mat.dis-nncited by 4

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Abstract (arXiv)

Optimization of atomic structures presents a challenging problem, due to their highly rough and non-convex energy landscape, with wide applications in the fields of drug design, materials discovery, and mechanics. Here, we present a graph reinforcement learning approach, StriderNET, that learns a policy to displace the atoms towards low energy configurations. We evaluate the performance of StriderNET on three complex atomic systems, namely, binary Lennard-Jones particles, calcium silicate hydrates gel, and disordered silicon. We show that StriderNET outperforms all classical optimization algorithms and enables the discovery of a lower energy minimum. In addition, StriderNET exhibits a higher rate of reaching minima with energies, as confirmed by the average over multiple realizations. Finally, we show that StriderNET exhibits inductivity to unseen system sizes that are an order of magnitude different from the training system.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 19 of its 58 references that are in the map. First posted on arXiv 29 Jan 2023.

Builds on

19 of 58 references.

References · 19

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7 more
  1. 2022Adaptive Cut Selection in Mixed-Integer Linear Programmingcited by 26
  2. 2022Learning Articulated Rigid Body Dynamics with Lagrangian Graph Neural Networkcited by 23
  3. 2022Unravelling the Performance of Physics-informed Graph Neural Networks for Dynamical Systemscited by 23
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 0; 2025: 1; 2026: 2.

4 papers.

  1. 2023GRAFENNE: Learning on Graphs with Heterogeneous and Dynamic Feature Setscited by 7
  2. 2026Position: Graph Condensation Needs a Reset -- Move Beyond Full-dataset Training and Model-Dependencecited by 1
  3. 2025MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structurescited by 0
  4. 2026GFFMERGE: Efficient Merging of Graph Neural Force Fields and Beyondcited by 0