2022 NSF REU Site: Materials Research with Data Science
PI: Aram Amassian, MSE Professor
REU Participant: Gabe Graves
Organic photovoltaic (OPV) materials have recently achieved a certified power conversion efficiency (PCE) of 18.5% and there are several promising demonstrations of scalable coating of OPV devices and modules using green solvents. However, the stability of these solar cell materials and devices continues to plague this technology and is widely seen as a primary bottleneck to its market adoption1,2,3. Addressing this problem with the help of data science and informatics tools, including machine learning, may yield significant new breakthroughs where traditional approaches have so far failed. For this purpose, the Amassian group has recently automated the formulation and printing of OPV materials with the aid of robotics, and automated characterization using optical, Raman, and chemical spectroscopies. This allows for high throughput assessment of material degradation and pinpoint some of the mechanisms of degradation following exposure to various external stressors. The workflow automation should enable data digitization efforts which synergize with data science and ML techniques implemented with the help of the Yingling group. REU students will be exposed to various aspects of fundamental organic photovoltaics materials and device-related research, as well as materials and device degradation concepts, and will help design robot-in-the-loop experimental campaigns that will be augmented with data-science methods to help assess various hypotheses of degradation as well as identify new and emerging trends in material and device degradation.
PI: Veronica Augustyn, MSE Associate Professor
REU Participant: Carter Wunch
The goal of this proposed research is to utilize materials informatics combined with synthesis and electrochemistry to synthesize new, metastable transition metal oxides via selective ion etching. The hypothesis driving this research is that there exists a large class of heretofore-undiscovered metastable transition metal oxides that can be obtained via selective etching of perovskite oxides and their derivative phases. For example, H2W2O7 is obtained via selective etching of Bi2W2O9 and exhibits fast electrochemical proton insertion and multi-color electrochromism4. To-date, such metastable oxides have been discovered via heuristic methods (trial-and-error, educated guess) but advancements in materials informatics should lead to more efficient screening of transition metal oxides suitable for selective ion etching, and new materials for electrochemical ion insertion technologies such as energy storage and electrochromics. Achieving this goal will lead to a new class of oxide materials with tunable optical and electronic properties. To attain the overarching goal, the research objectives are to: (1) Utilize materials informatics to identify and synthesize perovskite oxides and derivative phases suitable for selective ion etching in aqueous media; and (2) Characterize the structure and morphology of the obtained materials. These objectives will be supported by advanced materials characterization to determine the relationships between composition, structure, and synthesis.
PI: Nina Balke, MSE Associate Professor
REU Participant: Ian Lyons
The family of layered thio- and seleno-phosphates has gained attention as possible control dielectrics for the rapidly growing family of 2D and quasi-2D electronic materials5,6. Ferrielectric CuInP2S6 (CIPS) has been discovered in the 80’s but only recently it was revealed that this material exhibits a broad spectrum of unusual and even anomalous properties including negative electrostriction, a uniaxial quadruple potential well for Cu displacements enabled by the van-der-Waals (vdW) gap, and the existence of multiple polar states with dissimilar properties. This material opens the pathway towards ultra-thin few layer devices utilizing the polar material properties which cannot be realized with other materials which is the reason this material has seen a lot of research interest in the last few years. A property of CIPS which is of high interest is piezoelectricity which is linked to the bias-dependent strain response. One approach to strain the material is utilizing phase decomposition which is regulated by the Cu/In stoichiometry, resulting in a strained CIPS phase in a non piezoelectric matrix on length scales of hundreds of nanometers. A data-intensive approach is needed to link the local observables to macroscopic length scales as well as theoretical predictions. The REU student will use Piezoresponse Force Microscopy (PFM) to probe the local piezoelectric properties with a lateral resolution of tens of nanometer. This includes the proper calibration of PFM amplitude and phase response to extract the piezoelectric coefficient quantitatively. The technique will be employed on a variety of samples with locally and globally varying Cu/In ratios which can be determined locally in PFM through the phase separated piezoelectric and non-piezoelectric phases. The local Cu/In ratio will then be used to determine the strain of the material. The student will develop a data analysis flow to accurately capture the local material properties with high statistical significance in order to link them to macroscopic observations from complimentary techniques. Using this approach, observations across different length scales will be mapped onto the trends predicted by theory, which allows to explore CIPS as unique strain sensor material.
PI: Donald Brenner, MSE Professor
REU Participant: Skyler Kauffman
High entropy (HE) materials are typically defined by having five or more components in roughly equimolar proportions on a single lattice such that the configurational entropy contribution to the free energy becomes comparable to the enthalpic contribution.8 In this stability regime a given composition can have a stable crystal structure that is different from that of the constituents. It also leads to challenges in materials design. For example, HE carbides (HECs) are composed of two interpenetrating fcc lattices, one with carbon and one with five or more other cations arranged randomly. For a five species composition, a carbon vacancy has 210 possible near neighbor configurations; the number of possible configurations goes up to over 107 when also considering a second shell of cations. Clearly calculations of carbon vacancy concentrations, which are common defects that can affect a number of thermal-mechanical properties,9,10 are challenging with direct methods such as Density Functional Theory (DFT). REU students will be trained to run DFT calculations using VASP to find carbon vacancy formation energies for select configurations, and then use these calculations to help train a neural network (NN) from which energies can be calculated for other configurations. To start, we currently have a database of 40 carbon vacancy formation energies for nine different five-cation HEC compositions (a total 0f 360 energies), and a dedicated queue on the NC State High-Performance Computing Cluster to carry out additional calculations11. The goals of this research are for the students to (1) learn how to systematically carry out DFT calculations to expand this data base for NN fitting and model validation; (2) determine a database of not only energies but also properties such as valence electron concentrations and relative bond distances for species surrounding a carbon vacancy that optimize predictions of vacancy formation energies within a NN; and (3) understand how NNs are developed and used, as well as their limitations.
PI: Ramon Collazo, MSE Associate Professor
REU Participant: Bryan Wright
When exposed to ultraviolet (UV) light with wavelength around 265 nm, DNA or RNA of bacteria, viruses, and fungi can be denatured. For this purpose, UV irradiation is commonly used in water treatment plants and more recently for surface disinfection in various environments. Today, mercury lamps are used as the dominant technology to provide disinfecting UV light12. However, despite strong research efforts and interest, state-of-the-art UV-LEDs suffer from low power output and low overall efficiency. Commercially available LEDs have external quantum efficiencies (EQE) < 3%, and even lower wall-plug efficiency. These numbers are especially low when compared to III-nitride-based blue LEDs, which have evolved to achieve > 80% EQE. The main challenges towards the realization of UVC LEDs for disinfection applications include control of defects in the epitaxy process, optimization of p- and n-contacts, device structure design and low light extraction efficiency on the substrate side. Prospective REU students will be able to address any of these challenges in the Collazo group where the complete UVC LED process cycle, from simulation to testing, has been implemented. Based on this the following sub-research projects are available to the REU students: (a) implement point defect control and doping schemes for AlGaN layers and devices during the metal-organic chemical vapor deposition process, (b) simulate and test novel approaches towards better carrier injection in UVC LEDs, (c) improve the performance of electrical contacts, (d) support the fabrication process for LEDs with the goal to improve reliability and performance, (e) develop new light extraction schemes including nanopatterning and photonic crystals, or (f) characterize the optoelectronic properties of these UVC LEDs. Any of these efforts offer the student the opportunity to work with experienced scientists and exposure to state-of-the-art semiconductor science and technology, including SILVACO simulation, MOCVD growth, clean room technology, lithography, and etching methods. Automated probes stations and the lithographic ability to provide multiple LED designs within a wafer allows for the generation of relevant data to determine efficiency, reproducibility, and reliability among a variety of designs and dimensionality. The collected data will be used to train relevant machine learning schemes for the generalization of design rules that could be applied in future TCAD simulations of these state-of-the-art UV LEDs.
PI: Wenpei Gao, MSE Assistant Professor
REU Participant: Ishan Ghosh
Covering more than 71% of the Earth’s surface, the ocean not only regulates the global climate, but also holds diverse resources both in materials and biology. However, human activity has been constantly changing the ocean and coastal environments, among all the footprints, chemical pollution especially microplastics,13 poses great threats to the marine ecosystem, water quality, and human health. Identifying the materials in ocean, recognizing the influence from human activity, developing strategy to efficiently use the materials resources, and especially approaches to remediate the pollution, are key for a sustainable future. Although most current studies in ocean and coastal science have been actively using instruments developed for materials research, state-of-the-art methods in both synthesis and characterization are primarily employed only to study advanced functional materials. Learning the advanced techniques and data analysis in materials science and applying them to studying ocean and coastal environment represents a huge opportunity for MSE majored students as the new methods almost always bring new discovery. Specially, students will work on projects from experiments to data science including 1) collecting water from different water bodies, from ocean to those in rivers, lakes, etc. close to the coast, using the optical microscopes to capture micron scale process when the water dries and identifying the minerals and impurities, which may be related to the local natural environment, human activity, and industry, 2) studying the compositions of microplastics in ocean water and ways for plastic recycling and upcycling, and 3) learning and getting trained on state-of-the-art characterization tools, including SEM, XRD, and TEM with the help from graduate students, to understand the structure and processes of ocean materials across different length scales. Depending on the specific project, the students will focus on understanding the structures and formation processes of natural materials, summarizing the impact of human activity on environments from a materials science perspective, and potentially developing methods for collecting, recycling, or upcycling the microplastics in ocean. Especially, the data acquired with samples from different locations and the measurements done at the length scales across 7 orders of magnitude from the atomic scale to mm scale, are both results of this research and great training resources for the students to learn and help improve the methods for automated image and data analysis developed by the Gao group, which can help not only correlate different materials processes occurring in nature but also connect human activity with environmental science.
PI: Rajeev Gupta, MSE Associate Professor
REU Participant: Andrew Hollett
Materials beyond the conventional property limits are needed to meet current technological, socio-economic, and environmental challenges. The properties, including strength and corrosion resistance of commercial alloys, are limited by the conventional compositions and manufacturing technologies. Recent research on multi-principal element alloys (MPEAs) opened up a waste compositional space to be explored. Our ongoing research has shown that AlFeMnSi based MPEAs exhibit high corrosion resistance, high strength, and low density at lower material cost17. We have produced 15 different AlFeMnSi based alloys and investigated the microstructure and corrosion performance, which depend on the composition. Experimental optimization of the composition is a cumbersome process. We propose to utilize machine learning tools to determine the alloy composition resulting in the highest corrosion performance. Our experimental results will be used in training the ML models. Selected alloy compositions will be produced using arc melting and their corrosion performance will be tested.
PI: Doug Irving, MSE & Physics Professor
REU Participant: Jonathan Paul
Wide bandgap semiconductors and dielectrics are critical materials for next generation sustainable technologies. This application space includes solid state lighting, high power electronics, neuromorphic or quantum computing, and water purification. Reliable and controllable doping remains a significant challenge due to the complexity of the problem space, which encompasses the collective equilibrium of the ensemble of charged point defects, which is itself a massive and heavily interconnected combinatorial space, together with growth and processing history of the bulk material18. The Irving group has sought to address this problem with a recently developed point defect informatics framework and semi-automated computational setup that enables the design of electrical and optical properties of semiconductors and dielectrics from the bottom up. In addition, these informatics tools have been connected to the use of artificial intelligence (AI) based tools to select dopants and their concentrations to tailor the electrical response of dielectrics18-24. The point defects informatics framework has also been extended to provide essential information to multiscale device level simulations that have successfully predicted the electrical response of polycrystalline dielectrics as a function of grain size and have been used to identify device structures that might be essential for the realization of single photon emission from ultra-wide bandgap AlN. As part of this REU, participating students will utilize these tools and will gain experience in utilizing a point defects informatics framework for storage, curation, and querying structured data, utilizing semi-automated first principles hybrid functional simulation to add to point defect databases, guide doping of wide and ultrawide bandgap materials and suggest relevant growth conditions and realize solutions to the creation of sustainable materials for energy related applications, and use AI and ML based tools to find solutions in complex doping spaces.
PI: Jacob Jones, MSE Professor
REU Participant: Zhane McCleod
Phosphorus (P) is a critical component of cellular structures like DNA and processes like energy transfer and underpins the productivity and sustainability of global food systems25. However, the way in which we currently use P is unsustainable and has a significantly negative impact on the environment including causing harmful algal blooms and fish kills. A key materials research challenge needing addressed is to design innovative solutions to recovering and reusing P, e.g. through sorption processes26. To that end, materials characterization tools are needed to develop fundamental insight into the processes involving interactions between aqueous P species and engineered materials, e.g. mechanisms, kinetics, and thermodynamics underpinning dissolution, precipitation, and intercalation. X-ray diffraction (XRD) is one of the most well-established techniques for determining and refining our models of the structures, which can inform the understanding of mechanisms of sorption. However, P has a relatively low X-ray scattering cross section, meaning that we are less sensitive to identifying its position, occupancy, and atomic displacement factors on specific crystallographic sites. Refinement of XRD data has historically been limited to least-squares-based refinements such as the Rietveld method. While computationally efficient, these refinements provide single point estimates for structure parameters (e.g., a single value for a lattice constant or an atomic position parameter) and result in unreliable estimates of uncertainty, a key deficiency when trying to quantify important structural parameters associated with P. Recent work led by the Jones group at NC State, with collaborators from the disciplines of mathematics and statistics, has developed a new structure refinement approach based on Bayesian statistics and using Markov chain Monte Carlo sampling algorithms27. In this approach, posterior probability distributions are determined on all material structure and instrument parameters, providing information about uncertainty that is important for formally testing for differences between samples, comparing properties of the new sample to historical values, and determining whether more data is required to obtain reliable results. Most recently, numerous sampling algorithms were evaluated for this approach28 and the code was released on Github as “QUAD: Quantitative Uncertainty Analysis for Diffraction”. The REU student will utilize this UQ analysis approach to determine methods for reducing uncertainties in the measurements such as the influence of longer counting times, use of monochromated or multi-wavelength radiation, etc. The student will have hands-on training on XRD, will measure XRD patterns from both standards and analytes, and will both utilize the code to determine structural parameters.
PI: Melissa Pasquinelli, Forest Biomaterials Professor
REU Participant: Mariel Gomez
As atmospheric carbon dioxide concentrations continue to rise, averting catastrophic climate change will increasingly rely not only on emissions reduction, but also on CO2 capture and storage. Developing efficient, scalable, sustainable materials for CO2 capture and storage is an engineering grand challenge that sits at the nexus of manufacturing, materials science, molecular-scale modeling, and thermodynamics. Membrane-based CO2 separation is the carbon capture and storage approach with the most promise for near-term manufacturability29. Membranes for CO2 separation have traditionally been made of porous polymers, supported ionic liquid membranes (SILMs), Metal–organic frameworks, or ceramics. The SILMs are prepared by impregnating an ionic liquid (IL) in the pores of a porous support such as cellulose and cellulose acetate. SILMs show a promising performance especially for separation of CO2 from flue gas by solution diffusion mechanism. In addition, a small amount of IL is required to make a SILM; therefore, this solves the cost concern for these membranes. CO2 removal with SILMs has the advantages of energy preservation and low operating cost. Permeability is a critical factor that should be taken into consideration to evaluate the performance of the SILMs. Two crucial parameters that affect the permeability are gas diffusivity and solubility. These properties mainly controlled by the pore size of support and IL type. We will leverage molecular simulation strategies coupled with experimental details to identify the correlation of pore size of cellulose as the IL support and IL type with gas permeance30. The use of machine learning techniques to facilitate the design of a green membranes with sustainable material components and optimized CO2 capture.
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(19) Bowes, P. C.; Baker, J. N.; Irving, D. L. Site Preference of Y and Mn in Nonstoichiometric BaTiO3 from First Principles. Phys. Rev. Mater. 2020, 4 (8). https://doi.org/10.1103/PhysRevMaterials.4.084601.
(20) Wu, Y.; Bowes, P. C.; Baker, J. N.; Irving, D. L. Influence of Space Charge on the Conductivity of Nanocrystalline SrTiO 3. J. Appl. Phys. 2020, 128 (1). https://doi.org/10.1063/5.0008020.
(21) Baker, J. N.; Bowes, P. C.; Harris, J. S.; Collazo, R.; Sitar, Z.; Irving, D. L. Complexes and Compensation in Degenerately Donor Doped GaN. Appl. Phys. Lett. 2020, 117 (10). https://doi.org/10.1063/5.0013988.
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What Participants are Saying
Ishan Ghosh majors in Materials Science and Engineering at Clemson University. Learning about the MAT-DAT REU through a friend also looking at REU programs, Ishan calls the experience “enlightening.” The program expanded his horizons, giving him an overall unique experience. Ishan’s “aha moment” was getting his Python code to compile without any errors. Sharing kudos to MSE student coordinator Hillary Stone, his favorite part of the program was making connections with others from different backgrounds who share similar interests. To break up his time researching, Ishan recommends a visit to the State Farmer’s Market near NC State’s campus. His parting quote: “Go in with an open mind ready for whatever may come your way.”
Drew Hollett majors in Civil Engineering at Tufts University. Drew was recommended to the MAT-DAT REU by a professor at school. He says the experience was very rewarding and gave him the opportunity to do theoretical and experimental work. He picked up skills applicable to other fields and got to picture life as a graduate student. Giving praise to MSE Professor Yingling, student coordinator Hillary Stone, and Professor Gupta’s research group, his favorite part of the program was choosing how to solve a research problem due to the open-ended curriculum. Drew loved the openness of the NC State campus and surrounding areas like Lake Raleigh. Drew strongly recommends the REU. His parting quote: “Sometimes you just need to take a break to get the answer to a hard question.”
“It’s a great program for students interested in learning about how computer science and machine learning can be applied to engineering.”
– Drew Hollett
Ian Lyons majors in Chemistry at the University of Washington. Ian learned about the MAT-DAT REU through the NSF website. He was excited to work directly with his PI, Professor Balke, and he enjoyed learning how to code and apply machine learning to real-world problems. His favorite part of the program was working with actual real-world data. “The satisfaction of seeing your hard work pay off felt very nice,” says Ian. He recommends the REU to anyone interested in doing research in the future. He thinks it is a good way to quickly find out if research is for you and what your strengths and weakness are. It also helps to build many skills that are necessary to succeed in the academic world with much less pressure. For recreation, Ian was drawn to the NC State rock climbing wall in the Carmichael Gym, as well as the rock climbing community. His parting thoughts: “Take this opportunity to try new things and spend time with your roommates! We went skydiving and I am glad to have had the group experiences we had. There are not a lot of people on Centennial Campus during the summer, so get to know the people that are here. Introduce yourself and exchange socials at all of the welcome REU events because there are not many chances afterward to do so because people are most open to making friends when they first arrive somewhere new.”
Zhane McCleod majors in Aerospace Engineering at the University of Florida. Zhane learned about the MAT-DAT REU through her job. She feels grateful for the exposure to Python, machine learning, as well as graduate school. Zhane loved how free this program was: “Housing and travel are paid for. You are paid generously, even if you don’t have a summer internship or summer classes, an experience like this allows one to travel. I was able to work on my project in my own space and on my own time.” Zhane’s “aha” moment was discovering the machine learning methods linear regression, decision trees, and random forests will not train datasets with missing values unless rows with missing values are deleted. She had nothing but amazing notes for her mentor, Josh Harris, and her PI, Jacob Jones, and his research group. “I’ve grown more as a bold and confident woman with the help of our regular group meetings,” says Zhane. “I was surprised to learn that NC State has three campuses and that Centennial Campus is as big as it is. I was also surprised how NC State has a campus devoted to engineering and textiles.” Zhane shares her parting thought “The REU program is a mixture of fun, learning, and summer.”
Questions about the MAT-DAT REU? Email firstname.lastname@example.orgProgram Details Application Requirements Current Projects