Computational Discovery and Design of Materials : Session V
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 8:30 AM
March 22, 2023
Room: Cobalt 502A
Location: Hilton
Session Chair: Sara Kadkhodaei, University of Illinois Chicago; Houlong Zhuang, Arizona State University
8:30 AM
Designing High-Tc Superconductors with BCS-inspired Screening, Density Functional Theory and Deep-learning: Kamal Choudhary1; 1National Institute of Standards and Technology
Recent advances in first principles calculations and machine learning techniques allow for a systematic search for phonon-mediated superconductors. We develop a multi-step workflow for the discovery of conventional superconductors, starting with a Bardeen-Cooper-Schrieffer inspired pre-screening of 1736 materials with high Debye temperature and electronic density of states at the Fermi-level. Next, we perform electron-phonon coupling calculations for 1058 of them to establish a large and systematic database. Using the McMillan-Allen-Dynes formula, we identify 105 dynamically stable materials with transition temperatures, Tc > 5 K. In addition, we analyze trends in our dataset and individual materials including MoN, VC, VTe, KB6, Ru3NbC, V3Pt, ScN, LaN2, RuO2, and TaC. Finally, we demonstrate that deep-learning models can predict superconductor properties, including the Eliashberg function, thousands of times faster than direct first principles computations. We apply the trained model on the crystallographic open database to pre-screen candidates for further DFT calculations.
9:00 AM Invited
Bridging First-principles Calculations with Experiment: Insights from Case Studies on (Photo)Electrochemical Systems: Wennie Wang1; 1University of Texas at Austin
Having a synergistic feedback between ab initio methods and experiment can enable new physical insights into complex, heterogeneous materials systems that are challenging to achieve with either experiment or theory alone. The use of quantum mechanical methods based on density functional theory to simulate or interpret measured spectra or microscopy images provides an effective way to connect atomistic insights with macroscopic materials performance. This presentation will highlight several case studies that demonstrate this framework and reflect on strategies that helped to strengthen each collaboration. In particular, we will discuss how a combined theory-experiment approach led to 1) revealing the impact of surface composition on photoelectrochemical performance in BiVO4 photoanodes, 2) identifying the hydroxylation species formed on BiVO4 (010) surfaces with water dosing, and 3) identifying species formed in the solid-electrolyte interphase of sodium-ion battery electrodes.
9:30 AM
Machine Learning Assisted Discovery of Composite Solid-state Electrolytes in Context of Li-ion Batteries: Hasan Muhammad Sayeed1; Taylor D. Sparks1; 1University Of Utah
Solid-state lithium-ion batteries (SSLB) are considered next-generation energy storage devices for superior energy density and safety compared to their counterparts. Solid-state electrolytes (SSE) are the critical component of SSLBs. There are different types of SSEs, among which composite solid-state electrolytes (CSSEs) combines organic polymers and inorganic ceramics and can provide the advantages of all the single-phase electrolytes while solving their shortcomings. To use CSSEs in SSLBs, electrolyte materials must satisfy multiple requirements such as high ionic but low electronic conductivity, structural and electrochemical stability of interfaces and so on at once. We used Bayesian Optimization to search through vast potential combination space of CSSEs while optimizing for desirable properties of SSLBs. We generated random compositions at each iteration and predicted ionic and electronic conductivity. High performing samples were synthesized and characterized for validation. This data was then fed back into the model as training data and the process was repeated.
9:50 AM
Design of Bistable Metamaterials for Desired Dynamic Behavior: Hesaneh Kazemi1; Brianna MacNider1; Jaeyub Hyun1; Nicholas Boechler1; H. Alicia Kim1; 1University of California San Diego
This work presents a method for the design of architected materials made of bistable building blocks for desired dynamic behavior. With the advances of additive manufacturing, which has facilitated the fabrication of microstructures of materials, there has been an increasing interest in design of architected materials to obtain desired properties. Among these materials, bistable architected materials are of interest since they can exploit microstructural instabilities to exhibit extraordinary mechanical properties. These materials can be used for controlled trapping of elastic energy by reconfiguration into higher energy and stable deformed states. The instabilities in these metamaterials are very sensitive to the geometry of the structure. Therefore, in this work, we propose a method to design the microstructures of bistable metamaterials to obtain specific behavior. We demonstrate the effectiveness of the method via numerical examples.
10:10 AM Break
10:30 AM Invited
Closed Loop Computational Materials Discovery: Raymundo Arroyave1; Brent Vela1; Danial Khatamsaz1; Prashant Singh2; Duane Johnson2; Douglas Allaire1; 1Texas A&M University; 2Ames Lab
In this talk, I will present some recent efforts toward developing and deploying closed-loop materials discovery frameworks within materials simulation workflows. Specifically, it will be shown how Bayesian Optimization (BO) methods can be used to efficiently explore vast materials spaces. These frameworks incorporate the ability to optimize for multiple objectives at once, and are capable of dealing with multiple constraints. Moreover, the framework is capable of incorporating multiple information sources at different levels of fidelity. While the frameworks have been deployed on structural materials, their application in the discovery of novel functional materials is straightforward.
11:00 AM
Elucidating the Mechanisms for Fast Diffusion in Doped LLZO: Juan Verduzco1; Alejandro Strachan1; 1Purdue University
Discovery of novel electrolyte materials is key for the realization of solid-state batteries. Despite this interest, experimentally explored materials still underperform liquid electrolytes by over an order of magnitude. Mechanisms that drive improved Li transport in these materials are still in need of further research. To address this challenge, we analyze density functional theory – molecular dynamics (DFT-MD) simulations to explore the effects of doping on cubic lithium lanthanum zirconium oxide (LLZO). This analysis explores the effects of dopant inclusion on site-wise occupancy, residence time, and diffusion coefficients. We discuss a model based on trends derived from quantification of correlated motions of lithium atoms to propose potential compositions to accelerate design of such materials.