SciTech News Volume 70, No. 2 (2016)

Table of Contents: Columns and Reports From the Editor 3 Division News Science-Technology Division 4 New Members 6 Chemistry Division 7 New Members11 Engineering Division 12 Aerospace Section of the Engineering Division 17 Reviews Sci-Tech Book News Reviews 1

Studying polymer physics by machine learning.

Recently, machine learning becomes a computational method that burst in popularity. Many disciplines, such as condensed matter physics, quantum chemistry, chemical engineering as well as polymer physics have incorporate machine learning into their studies. This thesis mainly focuses on applying machine learning methods into the study of polymer physics. More specifically, two computational methods are studied: 1. how to classify polymer states by supervised or unsupervised learning methods, 2. how to use FNNs to search for structures of diblock copolymer under self-consistent field theory scheme. In the first topic, polymer samples that consist of both vastly different structures, such as gas-like random coil, liquid-like globular and subtly different structures, such as crystalline anti-Mackay, Mackay, are generated by Monte Carlo method. We then explored the capability of a FNN on the classification of different polymer configurations systematically. Base on a series of numerical experiments, we find that a FNN, after appropriate training, is able to not only identify all these structures, but also accurately locate the transition points between multiple states. The location given by the FNN has a good agreement with that provided by specific-heat calculations from the traditional method, which shows that the FNN offers a new tool for further studies of the polymeric phase transitions. We also studied these states with principal component analysis (PCA). When polymer samples only contain coil and globular states, PCA can distinguish these states, and offer insights to understand the relation between features and order parameters of these states. However, PCA itself is not powerful enough to distinguish globular, anti-Mackay, Mackay states. Then, a hybrid scheme combining PCA and supervised learning is utilized to identify and precisely detect the critical point of phase transitions between these polymer configurations. Compared with traditional methods, our studies demonstrate machine learning based methods have some distinct advantages. Firstly, these methods directly and only use molecular coordinates, which indicates its high compatibility with multiple sampling methods. In addition, the trained FNN has high transferability. In terms of identify transition points, our approaches requires much fewer samples, which indicates they are computationally faster than the traditional methods. In the second topic, we start from using the universal approximation theorem of FNN to build a machine learning based PDE solver. Our work mainly focuses on diffusion equations. This algorithm utilizes the function generated by the FNN as a trial function and adjusts the weights and biases of the FNN to search for the solution of a given PDE. The trial function will have a good match with the solution, when the weights and biases are optimal. Our approach is important to high dimensional diffusion equations. We discovered that the growth of the computational time obeys a power law with respect to the dimensionality, which indicates that the machine learning based solver offers a candidate algorithm that may not suffer from the ``curse of dimensionality''. We then demonstrated that this machine learning PDE solver can be conveniently adopted to deal with multi-variable, coupled integrodifferential equations in the self-consistent field theory for predicting polymer self-assembly structures. We observed all known three-dimensional classical structures, and our solutions have an excellent agreement with traditional solutions

Multi-Fidelity Bayesian Optimization for Efficient Materials Design

Materials design is a process of identifying compositions and structures to achieve desirable properties. Usually, costly experiments or simulations are required to evaluate the objective function for a design solution. Therefore, one of the major challenges is how to reduce the cost associated with sampling and evaluating the objective. Bayesian optimization is a new global optimization method which can increase the sampling efficiency with the guidance of the surrogate of the objective. In this work, a new acquisition function, called consequential improvement, is proposed for simultaneous selection of the solution and fidelity level of sampling. With the new acquisition function, the subsequent iteration is considered for potential selections at low-fidelity levels, because evaluations at the highest fidelity level are usually required to provide reliable objective values. To reduce the number of samples required to train the surrogate for molecular design, a new recursive hierarchical similarity metric is proposed. The new similarity metric quantifies the differences between molecules at multiple levels of hierarchy simultaneously based on the connections between multiscale descriptions of the structures. The new methodologies are demonstrated with simulation-based design of materials and structures based on fully atomistic and coarse-grained molecular dynamics simulations, and finite-element analysis. The new similarity metric is demonstrated in the design of tactile sensors and biodegradable oligomers. The multi-fidelity Bayesian optimization method is also illustrated with the multiscale design of a piezoelectric transducer by concurrently optimizing the atomic composition of the aluminum titanium nitride ceramic and the device’s porous microstructure at the micrometer scale.Ph.D

Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications

This article presents a state-of-the-art review of the applications of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) in building and construction industry 4.0 in the facets of architectural design and visualization; material design and optimization; structural design and analysis; offsite manufacturing and automation; construction management, progress monitoring, and safety; smart operation, building management and health monitoring; and durability, life cycle analysis, and circular economy. This paper presents a unique perspective on applications of AI/DL/ML in these domains for the complete building lifecycle, from conceptual stage, design stage, construction stage, operational and maintenance stage until the end of life. Furthermore, data collection strategies using smart vision and sensors, data cleaning methods (post-processing), data storage for developing these models are discussed, and the challenges in model development and strategies to overcome these challenges are elaborated. Future trends in these domains and possible research avenues are also presented

Advances in Molecular Simulation

Molecular simulations are commonly used in physics, chemistry, biology, material science, engineering, and even medicine. This book provides a wide range of molecular simulation methods and their applications in various fields. It reflects the power of molecular simulation as an effective research tool. We hope that the presented results can provide an impetus for further fruitful studies

Modelling tools and methodologies for rapid protocell prototyping

The field of unconventional computing considers the possibility of implementing computational devices using novel paradigms and materials to produce computers which may be more efficient, adaptable and robust than their silicon based counterparts. The integration of computation into the realms of chemistry and biology will allow the embedding of engineered logic into living systems and could produce truly ubiquitous computing devices. Recently, advances in synthetic biology have resulted in the modification of microorganism genomes to create computational behaviour in living cells, so called “cellular computing”. The cellular computing paradigm offers the possibility of intelligent bacterial agents which may respond and communicate with one another according to chemical signals received from the environment. However, the high levels of complexity when altering an organism which has been well adapted to certain environments over millions of years of evolution suggests an alternative approach in which chemical computational devices can be constructed completely from the bottom up, allowing the designer exquisite control and knowledge about the system being created. This thesis presents the development of a simulation and modelling framework to aid the study and design of bottom-up chemical computers, involving the encapsulation of computational re-actions within vesicles. The new “vesicle computing” paradigm is investigated using a sophisticated multi-scale simulation framework, developed from mesoscale, macroscale and executable biology techniques

Scientific Advances in STEM

Following a previous topic (Scientific advances in STEM: from professors to students; https://www.mdpi.com/topics/advances_stem), this new topic aims to highlight the importance of establishing collaborations among research groups from different disciplines, combining the scientific knowledge from basic to applied research as well as taking advantage of different research facilities. Fundamental science helps us to understand phenomenological basics, while applied science focuses on products and technology developments, highlighting the need to perform a transference of knowledge to society and the industrial sector

Experimental investigation and modelling of the heating value and elemental composition of biomass through artificial intelligence

Abstract: Knowledge advancement in artificial intelligence and blockchain technologies provides new potential predictive reliability for biomass energy value chain. However, for the prediction approach against experimental methodology, the prediction accuracy is expected to be high in order to develop a high fidelity and robust software which can serve as a tool in the decision making process. The global standards related to classification methods and energetic properties of biomass are still evolving given different observation and results which have been reported in the literature. Apart from these, there is a need for a holistic understanding of the effect of particle sizes and geospatial factors on the physicochemical properties of biomass to increase the uptake of bioenergy. Therefore, this research carried out an experimental investigation of some selected bioresources and also develops high-fidelity models built on artificial intelligence capability to accurately classify the biomass feedstocks, predict the main elemental composition (Carbon, Hydrogen, and Oxygen) on dry basis and the Heating value in (MJ/kg) of biomass...Ph.D. (Mechanical Engineering Science

2023 IMSAloquium

Welcome to IMSAloquium 2023. This is IMSA’s 36 th year of leading in educationalinnovation, and the 35th year of the IMSA Student Inquiry and Research (SIR) Program.https://digitalcommons.imsa.edu/archives_sir/1033/thumbnail.jp