Bulletin of the American Physical Society
APS March Meeting 2023
Volume 68, Number 3
Las Vegas, Nevada (March 5-10)
Virtual (March 20-22); Time Zone: Pacific Time
Session K28: Computational Design, Understanding and Discovery of Novel Materials IIIFocus
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Sponsoring Units: DMP Chair: Mauro Del Ben, LBNL; Rodrigo Freitas, MIT Room: Room 220 |
Tuesday, March 7, 2023 3:00PM - 3:36PM |
K28.00001: Machine Learning of Phase Diagrams Invited Speaker: Edwin García By starting from experimental- and ab initio-determined phase diagrams (PDs) of materials, a machine learning (ML) method is developed to infer the free energy function for each phase. The ML method samples the multidimensional space of Gibbs free energy parameters and user-defined physical constraints into a database of millions of PDs in order to identify the associated material properties. The method presented herein is 1000x to 100,000x faster than currently available approaches, and defines a new paradigm on the quantification of properties of materials and devices. The developed methodology is combined with the most widely used thermodynamic models – regular solution, Redlich-Kister, and sublattice formalisms– to infer the properties of materials for a few lithium-ion battery applications, reconstructing without human bias, well-established CALPHAD formulations while identifying previously missed stable and metastable phases and associated properties. |
Tuesday, March 7, 2023 3:36PM - 3:48PM |
K28.00002: Ferroelectricity in Sodalite-like MBxC6-x Clathrate Materials Ying Sun, Li Zhu The recent theory-orientated discovery of the first carbon-boron clathrate SrB3C3 [1] and the predicted ferroelectric ScB3C3 [2] are important advances toward developing novel ferroelectric materials. Here we propose 22 sodalite-like B-C frameworks with various B:C ratios based on the SrB3C3 structure, and perform high-throughput searches in the MBxC6-x system by introducing different metal (M) atoms into sodalite-like B-C frameworks. Several sodalite-like MBxC6-x clathrate materials have been found as ferroelectrics, exhibiting high polarization density and low mass density in comparison to commercially prevalent ferroelectrics. The tunable property upon substituting the guest metal atoms is also discussed. Our findings provide an example for the future discovery of novel clathrate ferroelectric materials and platforms for the experimental design of related functional devices. |
Tuesday, March 7, 2023 3:48PM - 4:00PM |
K28.00003: Study on the Ferroelectric Polarization of Polar Ordered Perovskites Under Epitaxial Strain]{Study on the Ferroelectric Polarization of Polar Ordered Perovskites under Epitaxial Strain Rishi Rao The study of perovskite oxynitrides (ABO2N) has quickly risen in importance since they represent a new class of materials with the promise for enhanced characteristics and applications in a wide range of fields. Recently, we predicted several stable ferroelectric phases of perovskite oxynitrides using swarm intelligence methods. We studied the polarization properties of the predicted oxynitrides under epitaxial strain using the Berry's phase approach and found high polarization values in excess of the typical polarization found in industrial ferroelectrics. The stability of these materials was also studied under epitaxial strain where it is found that the polarization can be tuned using 2 axis strains. These polar oxynitrides can provide a new avenue for replacing traditional lead based ferroelectric materials commonly found in industry. |
Tuesday, March 7, 2023 4:00PM - 4:12PM |
K28.00004: Theory-guided experimental optimization of high-efficiency ceramic capacitor Duane D Johnson, Xiaoli Tan To achieve high-efficiency (> 96%) and good energy-storage density (>3 J/cm3), we integrate density-functional theory (DFT) and geometric nonlinear transformation theory and estimate lattice mismatch to guide experiment synthesis to eliminate drawbacks in AFE dielectrics. Hysteresis loss (?E) is dominated by lattice mismatch between AFE-FE regions causing interfacial defects and distortions that hinder motion of AFE-FE interface during transition. So, using this combined effort, we successfully reduce electric-field-induced phase-transition hysteresis (<3 kV/cm), improve AFE-to-FE stability (fields >200 kV/cm), increase cyclic fatigue (108 cycles cycles), and optimize of composition in 7-dimensitonal perovskite (Pb,Sr,Ba,La,)(Zr,Sn,Ti)O3 to balance stability, hysteresis loss, and maximum critical fields for increased energy density. We demonstrated that further modification with Li+ and Bi3+ the efficiency can be improved to 96%. |
Tuesday, March 7, 2023 4:12PM - 4:24PM |
K28.00005: Temperature Dependence of Band Gap Renormalization in High-Temperature Sensor Materials via First-Principles and Experimental Corroboration Yuhua Duan, Jongwoo Park, Yu-Ning Wu, Tarak Nandi, Benjamin Chorpening, Wissam Saidi, Jeffrey K Wuenschell Understanding the temperature dependence of functional properties of high-T gas sensing materials is vital for their applications in combustion environments. The electron-phonon coupling that derives the electronic structure change with temperatures is a key property of interest as it affects other sensing responses. Herein, we assess the temperature dependence of band gap renormalization in metal oxides and perovskites by employing Allen-Heine-Cardona theory with first-principles simulations and corroborate with experimental observation. The calculated temperature-dependent band gap changes of these materials studied are in good agreement with in-house experimental data, proving that the theory can adequately predict renormalization on the band gap in the system of interest. The predicted and measured band gap variations are characterized using an analytical model, which can provide useful insights on the simulated zero-temperature band gaps. Based on the available data, a set of 53 metal oxides and perovskites were identified as potential high-T gas sensors. A machine learning model has been developed to predict the band-gap change by capturing the overall trend of the empirical parameters with respect to a reduced feature obtained by transforming the set of available physical features. |
Tuesday, March 7, 2023 4:24PM - 4:36PM |
K28.00006: Theoretical characterization of materials discovered at high-pressure: from fundamental research towards advanced applications Igor A. Abrikosov Combining theoretical simulations with experiment and broadly varying external parameters, pressure, temperature and composition allows one to discover phases which could not be synthesized otherwise [1]. Recent studies demonstrated that high-pressure (HP) high-temperature (HT) synthesis allowed for numerous discoveries of novel materials with exiting crystal chemistry [2]. However, the possibility to perform materials characterization at HP conditions is limited. Moreover, to use the novel materials in technological applications they must be quenchable to ambient conditions. In this talk we demonstrate the power of theoretical simulations for predicting the ambient pressure (meta-)stability of materials discovered in HP experiments and in exploration of their functionality, ranging from mechanical to electronic and topological properties. Of particular interest are non-intuitive theoretical results, like the one obtained in simulations of decompression of BeN4 from HP to ambient conditions, allowing for a discovery of a novel 2D Dirak material, the beryllonitrene [3]. We conclude that an exploration of metastable states of matter greatly enhances opportunities to design new advanced materials. |
Tuesday, March 7, 2023 4:36PM - 4:48PM Author not Attending |
K28.00007: CO2 capture by hybrid ultramicroporous TIFSIX-3-Ni under humid conditions using non-equilibrium cycling Timo Thonhauser, Saif Ullah Pyrazine-linked hybrid ultramicroporous materials (HUMs) have a pore size < 7 Å and are benchmark physisorbents for trace CO2 capture. However, their affinity for H2O decreases their carbon capture performance in humid environments. We present results of a study on the co-adsorption of H2O and CO2 in a high CO2-affinity HUM, i.e. TIFSIX-3-Ni. Through a combination of ab initio modeling with dynamic column breakthrough measurements and in situ infrared spectroscopy, we find that slow H2O sorption kinetics can enable CO2 uptake/release with retention of up to 90% of the dry CO2 uptake when using shortened adsorption cycles. The binding sites and sorption mechanisms of this co-adsorption environment reveal that the H2O and CO2 molecules cohabitate the same ultramicropore, made possible through favorable interactions between them at low water loading. At higher water loading, an energetically favored water network starts to displace CO2 molecules. Our results offer bottom-up design principles and insight into co-adsorption of CO2 and H2O that is relevant for carbon capture sorbents to address the challenges posed by gas capture and sequestration in humid environments. |
Tuesday, March 7, 2023 4:48PM - 5:00PM |
K28.00008: Fully Automated Nanoscale to Atomistic Structure from Theory and Spectroscopy Experiments Davis G Unruh, V. S. Chaitanya Kolluru, Maria K Chan Computational investigation into the structural and electronic properties of a material begins with knowledge of the underlying atomistic structure. When investigating novel or non-stoichiometric materials, various experimental spectroscopic techniques can be used to probe the material. However, moving from the spectra to the oxidation state and atomic configuration requires searching a vast structural space, where it is critical to not only match the experimental data but to also minimize quantities such as the energy to ensure structures are physically plausible and realizable. To address this need, we have previously developed the FANTASTX code, a multi-objective evolutionary algorithm which performs structure search for a variety of spectroscopies using genetic algorithm and basin-hopping methods. While FANTASTX has demonstrated success with few-atom systems, a significant challenge in extrapolating to more complex large-scale systems is the presence of near-duplicates within the search space and the significant computational expense of first-principles calculations on large-scale systems. To address these issues, we have extended FANTASTX to automatically incorporate on-the-fly machine learning methods, including both structural fingerprinting and graph neural network methods, to both identify and eliminate structural duplicates prior to processing and replace the use of density functional theory as the geometric relaxation and energy prediction mechanism. |
Tuesday, March 7, 2023 5:00PM - 5:12PM |
K28.00009: Autonomous crystal structure search by crystal morphing Junpei Oba, Seiji Kajita Recently, the autonomous search of materials was studied to alleviate the costs of material developments. For the crystalline solid domain, the crystal invariances are essential for an efficient autonomous search. If the invariances are not promised in the search space, myriad redundant points that represent the same crystal structure exist, which obstructs a construction of an efficient crystal-search space. |
Tuesday, March 7, 2023 5:12PM - 5:24PM |
K28.00010: Li vs. Na-based Solid-State Batteries: a Multiscale Modeling methodology in link to experiments Mahmoud ATTIA, Jean-Paul CROCOMBETTE, Said Yagoubi, Thibault Charpentier Designing solid-state batteries (SSBs) requires the design of highly efficient solid electrolytes (SSEs) that exhibit high ionic conduction properties. Though they have numerous draw- |
Tuesday, March 7, 2023 5:24PM - 5:36PM |
K28.00011: Computationally Exploring Structure-Property Relationships of Thermal Transport in Metal-Organic Frameworks Meiirbek Islamov, Hasan Babaei, Jeffrey R Long, Alan J McGaughey, Diego A Gomez-Gualdron, Christopher E Wilmer Metal-Organic Frameworks (MOFs) are crystalline, highly porous materials that have been heralded as revolutionary materials for gas storage and separations applications. However, the practical utility of MOFs depends on how rapidly they can disperse the significant amount of thermal energy produced during the exothermic adsorption process. Despite its significance, there is a limited understanding of the structure-thermal transport relationships in MOFs. To tackle this problem, we conducted the first high-throughput computational screening of thermal conductivity in MOFs by performing classical molecular dynamics (MD) simulations on a diverse set of 10,194 hypothetical MOFs. We observed that high thermal conductivity in MOFs is favored by small pores, high densities, and four-connected metal nodes. Furthermore, we discovered six hypothetical MOF structures that displayed very high average thermal conductivity. Interestingly, these six MOFs share square planar metal nodes that are each connected to four perpendicular organic linkers, implying that topology may be particularly important for thermal transport in MOFs. |
Tuesday, March 7, 2023 5:36PM - 5:48PM |
K28.00012: First principles study of the electronic structure and optical properties of the metal-organic framework Zn-MFU-4l Alex Smith, Jeffrey B Neaton
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Tuesday, March 7, 2023 5:48PM - 6:00PM |
K28.00013: Systematic Modification of Functionality Through Free Energy Surface Tailoring Dan Mendels, Fabian Byléhn, Timothy Sirk, Juan J De Pablo Advances in manufacturing and characterization of complex molecular systems have created a need for new methods for design at molecular length scales. Emerging approaches are increasingly relying on the use of Artificial Intelligence (AI), and the training of AI models on large data libraries. This paradigm shift has led to successful applications, but shortcomings related to interpretability and generalizability continue to pose challenges. Here, we explore an alternative paradigm in which Machine Learning (ML) is combined with physics-based considerations for molecular and materials engineering. Specifically, collective variables, akin to those used in enhanced sampled simulations, are constructed using an ML model trained on data gathered from a single system. Through the ML-constructed collective variables, it becomes possible to identify critical interactions in the system of interest, the modulation of which enables a systematic tailoring of the system's free energy landscape. To explore the efficacy of the proposed approach we have applied it to numerous case studies, a few of which will be discussed and illustrated during this talk. |
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