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Generative Discovery of New Semiconductors: Design without Understanding

Discovery of novel materials with exceptional properties is fundamental to the technology progress to more efficient solar panels, longer-life batteries, and room-temperature superconductors. Traditionally, the discovery process is a highly ad hoc, empirical, serendipitous process based on inventors’ ingenuity and knowledge. To address this issue, materials scientists now mainly resort to the rational design process, in which they aim to obtain a mechanistic understanding of how materials composition and structures determine their function properties. However, the complexity of these relationships and the difficulty to enumerate all the delicate and intricate design rules makes it challenging to explore the vast chemical design space using such rational design approaches.

Now a team of researchers from the Machine Learning and Evolution Laboratory at the University of South Carolina applied a paradigm-shifting deep learning based generative design approach for computational discovery of novel semiconductors. By combining a generative adversarial networks (GAN) model, a metal classifier, and high-throughput first-principles calculations, they have discovered 12 novel stable AA’′MH6 semiconductors in the F-43m space group. They showed that AA′MnH6 and NaYRuH6 semiconductors have considerably different properties compared to the rest of the AA′MH6 semiconductors, which are all wide-bandgap materials and may be ideal for certain applications. 

The findings are reported today in the journal Nature npj Computational Materials, in a paper by UofSC postdoc Edirisuriya M. Dilanga Siriwardane, graduate student Yong Zhao, and UofSC Professor of Computer Science Dr. Jianjun Hu (the corresponding author); Indika Perera at the University of Moratuwa. 

In their generative design approach, they first train a deep neural network based crystal structure generator model using all known crystal materials deposited in the databases, and then they run these generators to create a large number of hypothetical materials, which are then fed to the metal classifier to filter out potential semiconductors. Then the top candidate structures are fed to the  first principle simulation software Vasp for structure relaxation and stability check, and property calculation, which are very slow processes that used the High Performance Clusters (HPC) at the UofSc Research Computing facility. 

This work is another success case for the emerging AI-for-Science paradigm conducted by the interdisciplinary team with the computational materials expertise from Dr. Siriwardane and deep learning and computing expertise from Dr. Zhao, Prof. Hu, and Prof. Perera. “Compared to rational design approaches, the deep learning based generative design approach has the special advantage in terms of its capability to efficiently navigate the almost infinite chemical design space of materials, assisted by the implicit, latent, dark knowledge learned by the neural network”, says Prof. Hu. Their new material design approach does not require explicit specification of design knowledge and rules (so, design without understanding), which is in sharp contrast to the traditional rational design methods. 

“The deep learning generative models are very good at learning implicit chemical and materials composition knowledge”, says Dilanga, the paper’s lead author. “But the current algorithm can be further improved in terms of their success rates of generating stable structures”. Since the DFT quantum simulation is very slow, it is desirable to have a high hit-rate when they are used for validating the generated hypothetical materials. He adds, “our capability to discover more interesting materials is currently also limited by the computing power at UofSC”. 

“Generating thermodynamically and structurally stable crystals structures is a non-trivial problem”, says Zhao, who led the development of the deep generative algorithm. He has recently introduced physical rules and geometric constraints to further improve the quality of the generated crystal structures. 

For the discovered materials to become practical on the market or be adopted commercially, Hu says, “our hypothetical materials can guide experimental materials scientists to synthesize and characterize their properties”. 

The research was supported by the National Science Foundation and used the HPC computing facility of UofSC.

CSE Faculty Research Awards

We report that six of our faculty members have received research awards. They are:

  • Dr. Qi Zhang received a grant from the National Science Foundation (NSF) for "Cooperative Planning and Learning via Scalable and Learnable Multi-Agent Commitments"
  • Dr. Mike Huhns and Vignesh Narayanan received award from the University of Maryland/ARLIS/DOD for their project "Information Competition Simulator"
  • Dr. Ramtin Zand received an award from the Findora Foundation for the project "Design and Implementation of Hardware Accelerator for Gemini Zero-Knowledge Protocol"
  • Dr. Jason Bakos received an award from the National Science Foundation (NSF) for the project "Machine Learning on the Edge for Real-Time Microsecond State Estimation of High-Rate Dynamic Events”
  • Dr. Biplav Srivastava received a grant from the South Carolina Research Authority (SCRA) for the project "AI-assisted Data-driven team building for individual and institutional success"
  • Dr. Yan Tong received an award from the SC Space Grant Consortium/College of Charleston/NASA for the project "Computational Algorithms and Speed: An Education Game for High School Students (Andrew Bernhardt)”

CSE Faculty Research Awards

We report that three of our faculty members have received research awards. They are:

  • Dr. Biplav Srivastava: from Tantiv4, Inc for "Task Order 6: Analysis of Power IoT data for management of infrastructure".  
  • Dr. Amit Sheth: from UK Research and Innovation/Queen Mary University London for "Alan Turing Institute’s Turing AI Fellowships".
  • Dr. Srihari Nelakuditi from then National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)/NIH for "Validity and Utility of Consumer-based Wearable Fitness Trackers to Monitor Free-Living Physical Activity Energy Expenditure and Sleep in Children 5-12 Years Old".
  • Dr. Homay Valafar and Forest Agostinelli from the UofSC Office of Research for “ASPIRE II: Automatic and personalized identification of smoking using smartwatches".

Magellan Scholars Summer and Fall 2022

We congratulate the following Computer Science and Engineering undergraduate students for receiving a Magellan Scholar Award for Summer and Fall 2022. Their names and projects are:

  • Ahad Chattha, "Development of Mitral Valve Disease induced by TGFB1 Signaling"
  • Thomas Geiger, "Mapping Per- and Polyfluoroalkyl Substances (PFAS) in South Carolina Rivers: Are There Hotspots?"
  • Dylan Johnson, "Graph Constrained Crystal Structure Prediction Using Neural Dynamics"
  • Lilian Lamb, "A self-positioning platform for stationary measurements in the aquatic environment"
  • Jacqueline Schellberg, "Millimeter-Wave Imaging on 5G Handheld Smart Devices"
  • Katelyn Wyandt, "AI-Driven User Interface Design for Optimal User Engagement: A Use Case for Personalized Learning"
  • Zachary Young, "Generating Global Indoor Point Clouds Using 5G Millimeter-Wave Technology"

Discover UofSC Day: CSE Honorees

 The following CSE undergraduates were recognized for their posters on research, education, and public service at Discover UofSC 2022:

  • James Seekings, Computer Science (major), Mathematic (major) - Junior
  • Sara Hendrix, Computer Science (major) – Freshman
  • Xeerak Muhammad, Computer Engineering (major) - Senior
  • Antonio Turner, Computer Engineering (major) – Sophomore
  • Lilian Lamb, Computer Engineering (major) – Sophomore

Capstone Projects Showcase 2022

This year the students in the Senior Capstone course developed 40 apps. There were

  • 12 web applications using technologies such as Django, node, express, Angular, react, Vue, firebase, AWS.
  • 21 Android or iOS mobile apps using technologies such as Android Studio, Flutter, React Native Android, XCode, Firebase.
  • 7 desktop apps using technologies such as Unreal Engine, Unity, C#, Pygame.

You can view all the video demos online. If you are interested in proposing an idea for the team's next year, then let us know.

Instructor: UofSC CSE Department

Instructors will be required to teach courses in computer science and engineering, which may include computer programming (Python, Java, C++), web programming (HTML, CSS, JavaScript), computer engineering (architecture, embedded systems, advanced digital logic), data structures and algorithms, databases, operating systems, networks, computer security, software engineering, and capstone courses. Duties could also include teaching computer science and computer engineering courses to high ability students in the South Carolina Honors College. Additional duties outside of the classroom will include participation in student engagement activities such as student advising and recruitment.

Required Education and Experience    
Minimum requirements are a Master’s degree in computer science or a closely related field by the beginning date of employment and a commitment to excellence in teaching.

Preferred Qualifications    
Master’s degree in computer science or a closely related field and a commitment to excellence in teaching.

Apply here.

Jacqueline Schellberg Awarded the 2022 IEEE MTT-S Undergraduate Scholarship

Jacqueline Schellberg, an undergraduate student in the CSE department, has received a competitive 2022 undergraduate scholarship from the IEEE Microwave Theory and Techniques Society (MTT-S). She will be honored by MTT-S in June 2022 in Denver, CO.

Only 10 to 11 MTT-S undergraduate scholarships are presented worldwide each year. Past years' fellows came from Australia, Canada, China, Malaysia, Russia, and the USA. For the USA, the scholars are from top institutions, such as the University of California Los Angeles, Georgia Institute of Technology, University of Michigan, Ann Arbor, University of Washington, University of Maryland - College Park, Princeton University, etc.

This undergraduate scholarship will support Schellberg's project related to handheld, through-obstruction millimeter-wave imaging on 5G smart devices. The project could enable beyond-traditional-vision applications, such as non-intrusive package inspection and mobile physical security. She has already published a conference article related to the project at the ACM UbiComp 2021.

Schellberg is a rising junior student at USC in Computer Engineering. Her research interests include millimeter-wave imaging and RF motion tracking. She is also the recipient of many other honors and awards, including the ACM UbiComp 2021 Best Poster Honorable Mention, Spring 2022 Magellan Scholar Award, and was invited to participate in the 2021 Grace Hopper Celebration Conference from the CSE department.

Schellberg works under the supervision of Dr. Sanjib Sur, Assistant Professor of Computer Science and Engineering.

The IEEE Microwave Theory and Techniques Society (MTT-S) is a transnational society with more than 10,500 members and 190 chapters worldwide. This society promotes the advancement of microwave theory and its applications, including RF, microwave, millimeter-wave, and terahertz technologies.

CSE Faculty Research Awards

We report that six of our faculty members have received research awards. They are:

  • Yan Tong from Office of Naval Research (ONR)/DOD for "Reliable Perception for Unmanned Maritime Systems"
  • Jason Bakos from Savannah River National Lab/DOE for "A Unified Machine Learning Framework for Corrosion Surveillance"
  • Amit Sheth 
    • from BMW for "Proactive and Automated Material Control -- BMW" Co-PI Forest Agostinelli
    • from SC Department of Commerce for "Proactive and Automated Material Control -- SC Dept of Commerce" Co-PI Forest Agostinelli
    • from College of Charleston/NASA EPSCoR for "Augmenting Physics-Based Design and Multi-Physics Based Manufacturing with Data Driven Models to Manufacture Advanced Composites Structures with Automated Fiber Placement"
  • Srihari Nelakuditi from NSF for "REU Supplement for MRI: Acquisition of Omnipercipient Chamber for Gathering Ground Truth and Enabling Research on Smart and Connected Things"
  • Homay Valafar from Pennington Biomedical Research Center/Louisiana State University for "The Smartwatch Pilot Project"

Dr Sheth Ranked in Top Computer Scientists in the World

We would like to congratulate Dr. Amit Sheth. He was recently ranked by as one of the Top Computer Scientists in the world. More specifically, he was ranked:

  • #80 in World
  • #50 in the U.S.

with a 111 H-Index, 47,049 citations, and 581 publications.

For context, Amit was ranked close to notable Computer Scientists such as Turing Award winner Yan Lecun (#76, #46) and Co-Director of Stanford’s Human AI Institute Li Fei-Fei (#74, #45)

Congratulations to Amit for this well-deserved recognition. We are proud to have one of the world’s Top Computer Scientists in the CSE Department and leading the UofSC AI Institute.

Dr. Sur Receives NSF Career Award

We are proud to announce that Dr. Sanjib Sur has received an NSF Career Award for his project titled "Vision and Learning Augmented D-Band Networking and Imaging".

This project aims to enable the practical adoption of D-band mmWave networks and applications by solving the fundamental challenges in deployment, link adaptation, coordination, and unified networking-imaging. Specifically, the project explores an optical vision and deep learning augmented paradigm by thoroughly understanding the physical properties of the D-band channel, building measurement-driven empirical and learning models, and designing practical, real-time systems. Successful execution of this project would enable the following. (1) A framework for optimal deployment and a “what-if” analysis tool to help optimize the cost and benefits of D-band deployment in both indoor and outdoor environments. (2) Link adaptation and coordination protocols that significantly minimize latency and maximize throughput and efficiency for scalable D-band networking. (3) A unified networking-imaging protocol that reduces disruptions to the throughput and latency and overcomes challenges with the channel specularity to enable high-resolution D-band images. The project will design, build, and empirically validate the proposed systems in a D-band testbed, and th