Tuning the Molecular Weight Distribution from Atom Transfer Radical Polymerization Using Deep Reinforcement Learning

@article{Li2017TuningTM,
  title={Tuning the Molecular Weight Distribution from Atom Transfer Radical Polymerization Using Deep Reinforcement Learning},
  author={Haicheng Li and Christopher R. Collins and Thomas G Ribelli and K. Matyjaszewski and Geoffrey J. Gordon and T. Kowalewski and D. Yaron},
  journal={arXiv: Chemical Physics},
  year={2017}
}
We devise a novel technique to control the shape of polymer molecular weight distributions (MWDs) in atom transfer radical polymerization (ATRP). This technique makes use of recent advances in both simulation-based, model-free reinforcement learning (RL) and the numerical simulation of ATRP. A simulation of ATRP is built that allows an RL controller to add chemical reagents throughout the course of the reaction. The RL controller incorporates fully-connected and convolutional neural network… Expand

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References

SHOWING 1-10 OF 158 REFERENCES
Deep reinforcement learning for de novo drug design
TLDR
The ReLeaSE method is used to design chemical libraries with a bias toward structural complexity or toward compounds with maximal, minimal, or specific range of physical properties, such as melting point or hydrophobicity. Expand
Optimizing Chemical Reactions with Deep Reinforcement Learning
TLDR
This model outperformed a state-of-the-art blackbox optimization algorithm by using 71% fewer steps on both simulations and real reactions, and showed a better performance after training on reactions with similar or even dissimilar underlying mechanisms, which demonstrates its learning ability. Expand
Molecular de-novo design through deep reinforcement learning
TLDR
A method to tune a sequence-based generative model for molecular de novo design that through augmented episodic likelihood can learn to generate structures with certain specified desirable properties is introduced. Expand
Automatic Control of Polymer Molecular Weight during Synthesis
The continuous record of monomer and polymer concentrations, Cm and Cp, and cumulative weight-average mass, Mw, furnished by automatic continuous online monitoring of polymerization reactions (ACOMP)Expand
Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction
TLDR
This work presents two gradient procedures that can learn neural network policies for several problems, including a sequential prediction task and several high-dimensional robotics control problems and provides a comprehensive theoretical study of IL. Expand
Benchmarking Deep Reinforcement Learning for Continuous Control
TLDR
This work presents a benchmark suite of continuous control tasks, including classic tasks like cart-pole swing-up, tasks with very high state and action dimensionality such as 3D humanoid locomotion, task with partial observations, and tasks with hierarchical structure. Expand
A Brief Survey of Deep Reinforcement Learning
TLDR
This survey will cover central algorithms in deep reinforcement learning, including the deep Q-network, trust region policy optimisation, and asynchronous advantage actor-critic, and highlight the unique advantages of deep neural networks, focusing on visual understanding via reinforcement learning. Expand
Human-level control through deep reinforcement learning
TLDR
This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks. Expand
Evolving deep unsupervised convolutional networks for vision-based reinforcement learning
TLDR
Both the MPCNN preprocessor and the RNN controller are evolved successfully to control a car in the TORCS racing simulator using only visual input, the first use of deep learning in the context evolutionary RL. Expand
Continuous control with deep reinforcement learning
TLDR
This work presents an actor-critic, model-free algorithm based on the deterministic policy gradient that can operate over continuous action spaces, and demonstrates that for many of the tasks the algorithm can learn policies end-to-end: directly from raw pixel inputs. Expand
...
1
2
3
4
5
...