Accelerating Copolymer Inverse Design using AI Gaming algorithm

TL;DR

This paper introduces an AI gaming algorithm-based approach using Monte Carlo tree search combined with molecular dynamics to efficiently solve vast inverse sequencing problems in polymer design, significantly reducing computational evaluations.

Contribution

The study develops a novel MCTS-MD framework for inverse polymer sequencing, demonstrating its scalability and efficiency in identifying optimal sequences in extremely large search spaces.

Findings

Successfully identified copolymer sequences with zero interfacial energy within a few hundred evaluations.

Scalability demonstrated for chain lengths from 10-mer to 30-mer, with search spaces up to 1 billion.

Framework adaptable to other polymer and protein inverse design problems with limited data.

Abstract

There exists a broad class of sequencing problems, for example, in proteins and polymers that can be formulated as a heuristic search algorithm that involve decision making akin to a computer game. AI gaming algorithms such as Monte Carlo tree search (MCTS) gained prominence after their exemplary performance in the computer Go game and are decision trees aimed at identifying the path (moves) that should be taken by the policy to reach the final winning or optimal solution. Major challenges in inverse sequencing problems are that the materials search space is extremely vast and property evaluation for each sequence is computationally demanding. Reaching an optimal solution by minimizing the total number of evaluations in a given design cycle is therefore highly desirable. We demonstrate that one can adopt this approach for solving the sequencing problem by developing and growing a decision tree, where each node in the tree is a candidate sequence whose fitness is directly evaluated by molecular simulations. We interface MCTS with MD simulations and use a representative example of designing a copolymer compatibilizer, where the goal is to identify sequence specific copolymers that lead to zero interfacial energy between two immiscible homopolymers. We apply the MCTS algorithm to polymer chain lengths varying from 10-mer to 30-mer, wherein the overall search space varies from 210 (1024) to 230 (~1 billion). In each case, we identify a target sequence that leads to zero interfacial energy within a few hundred evaluations demonstrating the scalability and efficiency of MCTS in exploring practical materials design problems with exceedingly vast chemical/material search space. Our MCTS-MD framework can be easily extended to several other polymer and protein inverse design problems, in particular, for cases where sequence-property data is either unavailable and/or is resource intensive.