title: Neural-Network-Biased Genetic Algorithms for Materials Design: Evolutionary Algorithms That Learn.
abstract: Machine learning has the potential to dramatically accelerate high-throughput approaches to materials design, as demonstrated by successes in biomolecular design and hard materials design. However, in the search for new soft materials exhibiting properties and performance beyond those previously achieved, machine learning approaches are frequently limited by two shortcomings. First, because they are intrinsically interpolative, they are better suited to the optimization of properties within the known range of accessible behavior than to the discovery of new materials with extremal behavior. Second, they require large pre-existing data sets, which are frequently unavailable and prohibitively expensive to produce. Here we describe a new strategy, the neural-network-biased genetic algorithm (NBGA), for combining genetic algorithms, machine learning, and high-throughput computation or experiment to discover materials with extremal properties in the absence of pre-existing data. Within this strategy, predictions from a progressively constructed artificial neural network are employed to bias the evolution of a genetic algorithm, with fitness evaluations performed via direct simulation or experiment. In effect, this strategy gives the evolutionary algorithm the ability to "learn" and draw inferences from its experience to accelerate the evolutionary process. We test this algorithm against several standard optimization problems and polymer design problems and demonstrate that it matches and typically exceeds the efficiency and reproducibility of standard approaches including a direct-evaluation genetic algorithm and a neural-network-evaluated genetic algorithm. The success of this algorithm in a range of test problems indicates that the NBGA provides a robust strategy for employing informatics-accelerated high-throughput methods to accelerate materials design in the absence of pre-existing data.
authors.affiliation: Department of Polymer Engineering, The University of Akron , 250 South Forge Street, Akron, Ohio 44325, United States.
authors.forename: Tarak K
authors.initials: TK
authors.lastname: Patra
authors.affiliation: Department of Polymer Engineering, The University of Akron , 250 South Forge Street, Akron, Ohio 44325, United States.
authors.forename: Venkatesh
authors.initials: V
authors.lastname: Meenakshisundaram
authors.affiliation: Department of Polymer Engineering, The University of Akron , 250 South Forge Street, Akron, Ohio 44325, United States.
authors.forename: Jui-Hsiang
authors.initials: JH
authors.lastname: Hung
authors.affiliation: Department of Polymer Engineering, The University of Akron , 250 South Forge Street, Akron, Ohio 44325, United States.
authors.forename: David S
authors.identifier: 0000-0002-1436-9269
authors.initials: DS
authors.lastname: Simmons
mesh_terms: D000465:Algorithms; D006801:Humans; D000069550:Machine Learning; D056004:Molecular Dynamics Simulation; D016571:Neural Networks, Computer; D011108:Polymers / Q000737:chemistry*
chemical_list: D011108:Polymers
keywords: Ising model; compatibilizer; genetic algorithm; machine learning; materials design; molecular dynamics simulation; neural network; optimization; polymers; soft matter