More Like this

1. JARVIS-Leaderboard: a large scale benchmark of materials design methods

   https://doi.org/10.1038/s41524-024-01259-w  

   Choudhary, Kamal; Wines, Daniel; Li, Kangming; Garrity, Kevin F.; Gupta, Vishu; Romero, Aldo H.; Krogel, Jaron T.; Saritas, Kayahan; Fuhr, Addis; Ganesh, Panchapakesan; et al (May 2024, npj Computational Materials)

   Abstract Lack of rigorous reproducibility and validation are significant hurdles for scientific development across many fields. Materials science, in particular, encompasses a variety of experimental and theoretical approaches that require careful benchmarking. Leaderboard efforts have been developed previously to mitigate these issues. However, a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with perfect and defect materials data is still lacking. This work introduces JARVIS-Leaderboard, an open-source and community-driven platform that facilitates benchmarking and enhances reproducibility. The platform allows users to set up benchmarks with custom tasks and enables contributions in the form of dataset, code, and meta-data submissions. We cover the following materials design categories: Artificial Intelligence (AI), Electronic Structure (ES), Force-fields (FF), Quantum Computation (QC), and Experiments (EXP). For AI, we cover several types of input data, including atomic structures, atomistic images, spectra, and text. For ES, we consider multiple ES approaches, software packages, pseudopotentials, materials, and properties, comparing results to experiment. For FF, we compare multiple approaches for material property predictions. For QC, we benchmark Hamiltonian simulations using various quantum algorithms and circuits. Finally, for experiments, we use the inter-laboratory approach to establish benchmarks. There are 1281 contributions to 274 benchmarks using 152 methods with more than 8 million data points, and the leaderboard is continuously expanding. The JARVIS-Leaderboard is available at the website:https://pages.nist.gov/jarvis_leaderboard/ 

   more » « less
2. What's Left for a Computational Chemist To Do in the Age of Machine Learning?

   https://doi.org/10.1002/ijch.202100016  

   Kulik, Heather_J (April 2021, Israel Journal of Chemistry)

   Abstract Machine learning (ML) has become a central focus of the computational chemistry community. I will first discuss my personal history in the field. Then I will provide a broader view of how this resurgence in ML interest echoes and advances upon earlier efforts. Although numerous changes have brought about this latest wave, one of the most significant is the increased accuracy and efficiency of low‐cost methods (e. g., density functional theory or DFT) that have made it possible to generate large data sets for ML models. ML has also been used to bypass, guide, or improve DFT. The field of computational chemistry thus finds itself at a crossroads as ML both augments and supersedes traditional efforts. I will present what I believe the role of the computational chemist will be in this evolving landscape, with specific focus on my experience in the development of autonomous workflows in computational materials discovery for open‐shell transition‐metal chemistry. 

   more » « less
3. An AI-driven microstructure optimization framework for elastic properties of titanium beyond cubic crystal systems

   https://doi.org/10.1038/s41524-023-01067-8  

   Mao, Yuwei; Hasan, Mahmudul; Paul, Arindam; Gupta, Vishu; Choudhary, Kamal; Tavazza, Francesca; Liao, Wei-keng; Choudhary, Alok; Acar, Pinar; Agrawal, Ankit (December 2023, npj Computational Materials)

   Abstract Materials design aims to identify the material features that provide optimal properties for various engineering applications, such as aerospace, automotive, and naval. One of the important but challenging problems for materials design is to discover multiple polycrystalline microstructures with optimal properties. This paper proposes an end-to-end artificial intelligence (AI)-driven microstructure optimization framework for elastic properties of materials. In this work, the microstructure is represented by the Orientation Distribution Function (ODF) that determines the volume densities of crystallographic orientations. The framework was evaluated on two crystal systems, cubic and hexagonal, for Titanium (Ti) in Joint Automated Repository for Various Integrated Simulations (JARVIS) database and is expected to be widely applicable for materials with multiple crystal systems. The proposed framework can discover multiple polycrystalline microstructures without compromising the optimal property values and saving significant computational time. 

   more » « less
4. Density functional theory-based electric field gradient database

   https://doi.org/10.1038/s41597-020-00707-8  

   Choudhary, Kamal; Ansari, Jaafar N.; Mazin, Igor I.; Sauer, Karen L. (December 2020, Scientific Data)

   Abstract The deviation of the electron density around the nuclei from spherical symmetry determines the electric field gradient (EFG), which can be measured by various types of spectroscopy. Nuclear Quadrupole Resonance (NQR) is particularly sensitive to the EFG. The EFGs, and by implication NQR frequencies, vary dramatically across materials. Consequently, searching for NQR spectral lines in previously uninvestigated materials represents a major challenge. Calculated EFGs can significantly aid at the search’s inception. To facilitate this task, we have applied high-throughput density functional theory calculations to predict EFGs for 15187 materials in the JARVIS-DFT database. This database, which will include EFG as a standard entry, is continuously increasing. Given the large scope of the database, it is impractical to verify each calculation. However, we assess accuracy by singling out cases for which reliable experimental information is readily available and compare them to the calculations. We further present a statistical analysis of the results. The database and tools associated with our work are made publicly available by JARVIS-DFT ( https://www.ctcms.nist.gov/~knc6/JVASP.html ) and NIST-JARVIS API ( http://jarvis.nist.gov/ ). 

   more » « less
5. Machine Learning for the Discovery, Design, and Engineering of Materials

   https://doi.org/10.1146/annurev-chembioeng-092320-120230  

   Duan, Chenru; Nandy, Aditya; Kulik, Heather J. (June 2022, Annual Review of Chemical and Biomolecular Engineering)

   Machine learning (ML) has become a part of the fabric of high-throughput screening and computational discovery of materials. Despite its increasingly central role, challenges remain in fully realizing the promise of ML. This is especially true for the practical acceleration of the engineering of robust materials and the development of design strategies that surpass trial and error or high-throughput screening alone. Depending on the quantity being predicted and the experimental data available, ML can either outperform physics-based models, be used to accelerate such models, or be integrated with them to improve their performance. We cover recent advances in algorithms and in their application that are starting to make inroads toward ( a) the discovery of new materials through large-scale enumerative screening, ( b) the design of materials through identification of rules and principles that govern materials properties, and ( c) the engineering of practical materials by satisfying multiple objectives. We conclude with opportunities for further advancement to realize ML as a widespread tool for practical computational materials design. 

   more » « less