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1. The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design

   https://doi.org/10.1038/s41524-020-00440-1  

   Choudhary, Kamal; Garrity, Kevin F.; Reid, Andrew C.; DeCost, Brian; Biacchi, Adam J.; Hight Walker, Angela R.; Trautt, Zachary; Hattrick-Simpers, Jason; Kusne, A. Gilad; Centrone, Andrea; et al (December 2020, npj Computational Materials)

   null (Ed.)

   Abstract The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an integrated infrastructure to accelerate materials discovery and design using density functional theory (DFT), classical force-fields (FF), and machine learning (ML) techniques. JARVIS is motivated by the Materials Genome Initiative (MGI) principles of developing open-access databases and tools to reduce the cost and development time of materials discovery, optimization, and deployment. The major features of JARVIS are: JARVIS-DFT, JARVIS-FF, JARVIS-ML, and JARVIS-tools. To date, JARVIS consists of ≈40,000 materials and ≈1 million calculated properties in JARVIS-DFT, ≈500 materials and ≈110 force-fields in JARVIS-FF, and ≈25 ML models for material-property predictions in JARVIS-ML, all of which are continuously expanding. JARVIS-tools provides scripts and workflows for running and analyzing various simulations. We compare our computational data to experiments or high-fidelity computational methods wherever applicable to evaluate error/uncertainty in predictions. In addition to the existing workflows, the infrastructure can support a wide variety of other technologically important applications as part of the data-driven materials design paradigm. The JARVIS datasets and tools are publicly available at the website: https://jarvis.nist.gov . 

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2. Prediction of crystallized phases of amorphous Ta2O5-based mixed oxide thin films using a density functional theory database

   https://doi.org/10.1063/5.0035573  

   Fazio, Mariana_A; Yang, Le; Menoni, Carmen_S (March 2021, APL Materials)

   The genomics approach to materials, heralded by increasingly accurate density functional theory (DFT) calculations conducted on thousands of crystalline compounds, has led to accelerated material discovery and property predictions. However, so far, amorphous materials have been largely excluded from this as these systems are notoriously difficult to simulate. Here, we study amorphous Ta2O5 thin films mixed with Al2O3, SiO2, Sc2O3, TiO2, ZnO, ZrO2, Nb2O5, and HfO2 to identify their crystalline structure upon post-deposition annealing in air both experimentally and with simulations. Using the Materials Project open database, phase diagrams based on DFT calculations are constructed for the mixed oxide systems and the annealing process is evaluated via grand potential diagrams with varying oxygen chemical potential. Despite employing calculations based on crystalline bulk materials, the predictions agree well with the experimentally observed crystallized phases of the amorphous thin films. In the absence of ternary phases, the dopant acts as an amorphizer agent increasing the thermal stability of Ta2O5. The least efficient amorphizer agent is found to be Nb2O5, for which the cation has similar chemical properties to those of Ta in Ta2O5. These results show that DFT calculations can be applied for the prediction of crystallized structures of annealed amorphous materials. This could pave the way for accelerated in silico material discovery and property predictions using the powerful genomic approach for amorphous oxide coatings employed in a wide range of applications such as optical coatings, energy storage, and electronic devices. 

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3. Additively manufactured Ni-20Cr to V functionally graded material: Computational predictions and experimental verification of phase formations

   https://doi.org/10.1016/j.jallcom.2024.174011  

   Tonyali, Beril; Sun, Hui; Bocklund, Brandon; Borgonia, John Paul; Otis, Richard A.; Shang, Shun-Li; Liu, Zi-Kui; Beese, Allison M. (February 2024, Journal of Alloys and Compounds)

   A database for the Cr-Ni-V system was constructed by modeling the binary Cr-V and ternary Cr-Ni-V systems using the CALPHAD approach aided by density functional theory (DFT)-based first-principles calculations and ab initio molecular dynamics (AIMD) simulations. To validate this new database, a functionally graded material (FGM) using Ni-20Cr and V was fabricated using directed energy deposition additive manufacturing (DED AM) and experimentally characterized. The deposited Ni-20Cr was pure fcc phase, while increasing V content across the gradient resulted in sigma phase formation, followed by bcc phase formation. The experimentally measured phases were compared with CALPHAD computations made using a Cr-Ni-V thermodynamic database from the literature and the database developed in the present work. The newly developed database was shown to better predict the experimentally observed phases due to its accurate modeling of binary systems within the database and the ternary liquid phase, which is critical for accurate Scheil calculations. 

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4. Explaining and Fixing DFT Failures for Torsional Barrier

   https://doi.org/10.doi.102110.1021/acs.jpclett.1c00426  

   Nam, Seungsoo; Cho, Eunbyol; Sim, Eunji; and Burke, Kieron (March 2021, The journal of physical chemistry letters)

   Most torsional barriers are predicted with high accuracies (about 1 kJ/mol) by standard semilocal functionals, but a small subset was found to have much larger errors. We created a database of almost 300 carbon–carbon torsional barriers, including 12 poorly behaved barriers, that stem from the Y═C—X group, where Y is O or S and X is a halide. Functionals with enhanced exchange mixing (about 50%) worked well for all barriers. We found that poor actors have delocalization errors caused by hyperconjugation. These problematic calculations are density-sensitive (i.e., DFT predictions change noticeably with the density), and using HF densities (HF-DFT) fixes these issues. For example, conventional B3LYP performs as accurately as exchange-enhanced functionals if the HF density is used. For long-chain conjugated molecules, HF-DFT can be much better than exchange-enhanced functionals. We suggest that HF-PBE0 has the best overall performance. 

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5. Physics guided deep learning for generative design of crystal materials with symmetry constraints

   https://doi.org/10.1038/s41524-023-00987-9  

   Zhao, Yong; Siriwardane, Edirisuriya M. Dilanga; Wu, Zhenyao; Fu, Nihang; Al-Fahdi, Mohammed; Hu, Ming; Hu, Jianjun (March 2023, npj Computational Materials)

   Abstract Discovering new materials is a challenging task in materials science crucial to the progress of human society. Conventional approaches based on experiments and simulations are labor-intensive or costly with success heavily depending on experts’ heuristic knowledge. Here, we propose a deep learning based Physics Guided Crystal Generative Model (PGCGM) for efficient crystal material design with high structural diversity and symmetry. Our model increases the generation validity by more than 700% compared to FTCP, one of the latest structure generators and by more than 45% compared to our previous CubicGAN model. Density Functional Theory (DFT) calculations are used to validate the generated structures with 1869 materials out of 2000 are successfully optimized and deposited into the Carolina Materials Databasewww.carolinamatdb.org, of which 39.6% have negative formation energy and 5.3% have energy-above-hull less than 0.25 eV/atom, indicating their thermodynamic stability and potential synthesizability. 

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