Materials Genome, CALPHAD, and a Career over the Span of 20, 50, and 60 Years: An FMD/SMD Symposium in Honor of Zi-Kui Liu: Density Functional Theory
Sponsored by: TMS Functional Materials Division, TMS Structural Materials Division, TMS Materials Processing and Manufacturing Division, TMS: Alloy Phases Committee, TMS: Integrated Computational Materials Engineering Committee
Program Organizers: Yu Zhong, Worcester Polytechnic Institute; Richard Otis, Jet Propulsion Laboratory; Bi-Cheng Zhou, University of Virginia; Chelsey Hargather, New Mexico Institute of Mining and Technology; James Saal, Citrine Informatics; Carelyn Campbell, National Institute of Standards and Technology
Tuesday 2:30 PM
March 21, 2023
Room: Sapphire L
Location: Hilton
Session Chair: Bi-Cheng Zhou, University of Virginia
2:30 PM Invited
Ab Initio Descriptors to Guide Materials Design in High-dimensional Chemical and Structural Configuration Spaces: Fritz Koermann1; Tilmann Hickel1; Joerg Neugebauer1; 1MPI fuer Eisenforschung
Modern engineering materials have evolved from simple single phase materials to nano-composites that employ dynamic mechanisms down to the atomistic scale. The structural and thermodynamic complexity of this new generation of structural materials presents severe challenges to their design. Ab initio approaches provide perfect tools to new design routes but face serious challenges when having to systematically sample high-dimensional chemical and structural configuration spaces. Combining advanced sampling, machine learning as well as thermodynamic concepts with our python based framework pyiron allows us in a highly automated way to combine first principles calculations with big data analytics and to obtain accurate ab initio descriptors. The flexibility and the power of these approaches will be demonstrated for compositionally complex alloys, where they allowed us to derive and employ new design concepts.
3:00 PM Invited
A Solution to the Temperature Evolution of Multi-well Free-energy: Yi Wang1; Tiannan Yang1; Shun-Li Shang1; Long-Qing Chen1; Zi-Kui Liu1; 1Penn State
It has been a grand challenge to resolve the temperature evolution of multi-well free-energy landscape which is fundamentally relevant to phase transitions and associated critical phenomena as listed by Ginzburg [Rev. Mod. Phys. 76 (2004) 981]. Hereby we provide a simple solution, based on a priori concept of Boltzmann thermal mixing among multiple parabolic potentials. The approach is successfully demonstrated herein by studying the temperature dependences of the order parameters and heat capacities of various critical phenomena, including the superconductive phase transitions in Nb and YBa2Cu3O7, the metal-insulator and magnetic transitions in Ca3Ru2O7, the magnetic transition in Ni, the magnetic and ferroelectric transition in BiFeO3, and the ferroelectric transition in PbTiO3; indicating the capacity of the approach to address the exponential vanishing of the order parameter approaching the critical temperature and the exponential increasing of the heat capacity in a wide temperature range below the critical temperature.
3:30 PM Invited
Understanding Interstitial and Substitutional Alloying of Refractory Metals: Anton Van der Ven1; 1University of California, Santa Barbara
Refractory metals, including Ti, Zr, Hf, V, Nb and Ta are unique in that they are able to dissolve high concentrations of interstitial carbon, nitrogen and oxygen, leading to intriguing thermodynamic, kinetic and mechanical properties. In view of the increased interest in multi-principal element (MPE) alloys for high temperature structural applications, there is a need to understand the effects of non-dilute concentrations of interstitial alloying elements on refractory alloys. In this talk I will describe the results of first-principles statistical mechanics studies of phase stability of refractory metals alloyed with interstitial C, N and O. Interactions between interstitial and substitutional alloying elements have their origin in electronic structure properties that are specific to early transition metals. The strong interactions between dissolved interstitials and substitutional alloying elements manifest themselves at high temperatures, leading to short-range ordering phenomena that affect phase stability and diffusion
4:00 PM Break
4:20 PM Invited
Melting Temperature Prediction via Integrated First Principles and Deep Learning: Qijun Hong1; 1Arizona State University
We build a rapid and accurate tool for melting temperature prediction, by integrating methods we have developed based on density functional theory and deep learning. DFT generates highly accurate data points and iteratively improves the deep learning model, while the latter provides speed and efficiency. On the DFT side, we have built an accurate and cost-effective method for first principles melting temperature calculation. The method is implemented in the SLUSCHI package, an automated tool for DFT melting point calculation. On the deep learning side, we have built a melting temperature database that contains both experiment and computation. Based on the data, we then built a graph neural networks model that rapidly predicts melting temperature. We present examples of applications, such as the design and discovery of high-melting-point materials, and the evolution of mineral's melting temperature.
4:50 PM Invited
Stability of Transition Metal High Entropy Alloys: From First-principles and Machine Learning: Ying Chen1; Nguyen-Dung Tran1; Chang Liu2; Xinming Wang3; Jun Ni3; 1Tohoku University; 2Institute of Statistical Mathematics; 3Tsinghua University
Along the first-principles study on a specific quinary high entropy alloy FeNiCoCrPd which is synthesized by substituting Mn in Cantor alloy by Pd, to reveal the mechanism of inhomogeneous feature of Pd in enhancing the mechanical property, we have accumulated 1,000 DFT data of sub-systems of binary, ternary, quaternary for all equiatomic composition and typical non-equiatomic compositions using the special quasi-random structures (SQS), further extended to some senaries, for fcc. bcc and hcp structures. Based on this FeCoNiCrMnPd data set, systematic predictions are conducted using machine learning. The mesh searching for virtual systems of FeCoNiCrMnPd+x (x=all 3d-, 4d-elements, Mg, Al, Si, etc.) gave a general picture of solid solution stability of the transition metal ternaries, quaternaries. Furthermore, the elemental convolution graph neural networks (ECNet) combing transfer learning are attempted to explore the stability and properties of the higher compositional systems mainly based on the data of binaries and ternaries.
5:20 PM Invited
A Comprehensive First-principles and Machine Learning Study of Pure Elements and Alloys: From Pure Shear Deformation to Data-driven Insights into Mechanical Properties: Shun-Li Shang1; Yi Wang1; Jingjing Li1; Allison Beese1; Zi-Kui Liu1; 1Pennsylvania State University
Advance in machine learning (ML), especially in the cooperation between ML predictions, first-principles calculations, and experimental verification is emerging as a new paradigm to understand fundamentals, verify, analyze, and predict data, and design and discover materials. Here, first we perform high-throughput first-principles calculations for 60 pure elements and 25 dilute and concentrated alloys in the bcc, fcc, and hcp lattices, resulting in fundamental properties of ideal shear strength, stable and unstable stacking fault energies. Then, ML-based correlation analyses are performed to understand these fundamentals with respect to elemental attributes; and these fundamentals make it possible for data-driven insights into mechanical properties of pure elements and alloys, including hardness, fracture toughness, tensile strength, and elongation. The present study provides not only fundamental properties of pure elements and alloys, including high-entropy alloys, but also methodology regarding data-driven understanding and predictions as illustrated with mechanical properties.