13 research outputs found
Predicting Pair Correlation Functions of Glasses using Machine Learning
Glasses offer a broad range of tunable thermophysical properties that are
linked to their compositions. However, it is challenging to establish a
universal composition-property relation of glasses due to their enormous
composition and chemical space. Here, we address this problem and develop a
metamodel of composition-atomistic structure relation of a class of glassy
material via a machine learning (ML) approach. Within this ML framework, an
unsupervised deep learning technique, viz. convolutional neural network (CNN)
autoencoder, and a regression algorithm, viz. random forest (RF), are
integrated into a fully automated pipeline to predict the spatial distribution
of atoms in a glass. The RF regression model predicts the pair correlation
function of a glass in a latent space. Subsequently, the decoder of the CNN
converts the latent space representation to the actual pair correlation
function of the given glass. The atomistic structures of silicate (SiO2) and
sodium borosilicate (NBS) based glasses with varying compositions and dopants
are collected from molecular dynamics (MD) simulations to establish and
validate this ML pipeline. The model is found to predict the atom pair
correlation function for many unknown glasses very accurately. This method is
very generic and can accelerate the design, discovery, and fundamental
understanding of composition-atomistic structure relations of glasses and other
materials
Accelerated Design of Block Copolymers: An Unbiased Exploration Strategy via Fusion of Molecular Dynamics Simulations and Machine Learning
Star block copolymers (s-BCPs) have potential applications as novel
surfactants or amphiphiles for emulsification, compatbilization, chemical
transformations and separations. s-BCPs are star-shaped macromolecules
comprised of linear chains of different chemical blocks (e.g., solvophilic and
solvophobic blocks) that are covalently joined at one junction point. Various
parameters of these macromolecules can be tuned to obtain desired surface
properties, including the number of arms, composition of the arms, and the
degree-of-polymerization of the blocks (or the length of the arm). This makes
identification of the optimal s-BCP design highly non-trivial as the total
number of plausible s-BCPs architectures is experimentally or computationally
intractable. In this work, we use molecular dynamics (MD) simulations coupled
with reinforcement learning based Monte Carlo tree search (MCTS) to identify
s-BCPs designs that minimize the interfacial tension between polar and
non-polar solvents. We first validate the MCTS approach for design of small-
and medium-sized s-BCPs, and then use it to efficiently identify sequences of
copolymer blocks for large-sized s-BCPs. The structural origins of interfacial
tension in these systems are also identified using the configurations obtained
from MD simulations. Chemical insights on the arrangement of copolymer blocks
that promote lower interfacial tension were mined using machine learning (ML)
techniques. Overall, this work provides an efficient approach to solve design
problems via fusion of simulations and ML and provide important groundwork for
future experimental investigation of s-BCPs sequences for various applications
Neural-Network-Biased Genetic Algorithms for Materials Design: Evolutionary Algorithms That Learn
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