Computational Discovery and Design of Materials : Session VI
Sponsored by: TMS Materials Processing and Manufacturing Division, TMS: Computational Materials Science and Engineering Committee, TMS: Integrated Computational Materials Engineering Committee
Program Organizers: Houlong Zhuang, Arizona State University; Duyu Chen, University of California, Santa Barbara; Ismaila Dabo, Pennsylvania State University; Yang Jiao, Arizona State University; Sara Kadkhodaei, University Of Illinois Chicago; Mahesh Neupane, Army Research Laboratory; Xiaofeng Qian, Texas A&M University; Arunima Singh, Arizona State University; Natasha Vermaak, Lehigh University
Wednesday 2:00 PM
March 22, 2023
Room: Cobalt 502A
Location: Hilton
Session Chair: Xiaofeng Qian, Texas A&M University
2:00 PM Invited
Band Gap Renormalization in 2D Materials from First-principles: Sahar Sharifzadeh1; 1Boston University
Incorporation of electron-phonon interactions in the modeling of materials is important for an accurate comparison of observables to experimental measurements. Optical and transport properties of materials are often strongly impacted by the quantum-mechanical nature of lattice vibrations, which are computationally expensive to incorporate explicitly in a first-principles approach. Here, we study bandgap renormalization due to electron-phonon coupling in 2D materials in a high-throughput fashion. For ~100 monolayer materials, we compute the gap with and without the presence of phonons. The connection between different physical descriptors with band gap renormalization are explored and highlighted using a data-driven approach, demonstrating that the strength of electron-phonon interactions is highly dependent on the bonding structure. Overall, this framework allows for a systematic theoretical exploration of the influence of phonons on optical properties.
2:30 PM
What is a Minimal Working Example for a Self-driving Laboratory?: Sterling Baird1; Taylor Sparks1; 1University of Utah
Self-driving labs are the future; however, the capital and expertise required can be daunting. We introduce an optimization task for less than $100, a square foot of desk space, and an hour of total setup time from the shopping cart to the first "autonomous drive." We use optics rather than chemistry for our demo. After all, light is easier to move than matter. While not materials-based, several core principles of a self-driving materials discovery lab are retained in this cross-domain example: sending commands to hardware to adjust physical parameters, receiving measured objective properties, decision-making via active learning, and utilizing cloud-based simulations. The demo is accessible, extensible, modular, and repeatable, making it an ideal candidate for low-cost prototyping of self-driving laboratory concepts and learning the principles of self-driving laboratories in a low-risk setting. Extensions to liquid- and solid-mixing demos will be discussed. Come find out how to set up your own miniature self-driving lab!
2:50 PM
Exploiting First-principles Based Interpretation of X-ray Absorption Spectra of Ni, Cr, Fe Elements in Molten-salt System: Mehmet Topsakal1; Kaifeng Zheng1; Nirmalendu Patra1; Michael Woods2; Ruchi Gakhar2; Phillip Halstenberg3; Shannon Mahurin3; Anatoly Frenkel4; Simerjeet Gill1; 1BNL; 2Idaho National Laboratory; 3Oak Ridge National Laboratory; 4Stony Brook University
First-principles simulations of XAS can play a critical role in interpreting the abstract spectral features as observed in XAS spectra and draw physical insights into local structure. For example, understanding the effect of solvent chemistry and radiation on the speciation and local structure of metals is crucial for predicting the stability and reactivity of molten salts for successful deployment of molten salt reactor (MSR) systems. Due to its element-specific feature, XAS can allow studying metal species' local coordination environment and chemical structure in molten salt systems. In this work, we demonstrate the interpretation of experimentally observed XAS via first-principles simulations on model molten salt systems containing Ni, Cr, and Fe. Multiple approaches will be benchmarked and compared to elucidate benefits and limitations. This work was supported as part of the Molten Salts in Extreme Environments, Energy Frontier Research Center, funded by the U.S. Department of Energy Office of Science.
3:10 PM
Graph Mining in Materials Science for the Prediction of Material Properties: Mehrdad Jalali1; Christof Wöll1; 1Institute of Functional Interfaces (IFG), Karlsruhe Institute of Technology (KIT
There has been a rapid growth in the number and application of new materials. Today, increased computational power and the established use of automated machine learning approaches make the data science tools available, which provide an overview of the chemical space, support the choice of appropriate materials, and predict specific properties of materials for the desired application. Among the different data science tools, graph theory approaches, where data generated from numerous real-world applications are represented as a graph (network) of connected objects, have been widely used in various scientific fields. In this work, we describe the application of a particular graph theory approach known as graph mining to predict material properties based on a constructed graph in the metal-organic framework (MOFs) and high-entropy alloys (HEAs) materials. The results show a significant improvement in predicting properties in both fields.