Computational Discovery and Design of Functional Materials
Our research focuses on the discovery and design of novel materials for energy conversion and storage, and next-generation computing. We identify new composition–structure–property relationships to enable materials by design. Historically, materials have been discovered by Edisonian trial-and-error approaches and even, serendipitous “happy accidents.” Designing materials with tailored properties is challenging because of the astronomical number of possible compounds, and structures. We utilize computational tools, including first-principles methods and machine learning to discover and design functional materials.
Materials Discovery via High-Throughput Searches
The concept of high-throughput (HT) computational search is simple yet powerful — compute the properties of a large number of materials using first-principles, and then interrogate this database to search for materials with desired properties. Clearly, the predictions should be validated by reality. The HT experimental approach was pioneered over a hundred years ago by Edison; with the advent of accurate computational methods and availability of inexpensive computing resources, its computational counterpart is now a viable path for materials discovery. We are using HT search to find novel materials for applications that require contraindicated combination of materials properties. For example, most known materials with high electrical conductivity also exhibit high thermal conductivity; thermoelectrics require materials with high electrical but low thermal conductivity.
- Material Descriptors to Predict Thermoelectric Performance of Narrow-gap Semiconductors and Semimetals, Materials Horizons 10, 4256 (2023)
- Emerging Materials and Design Principles for Wurtzite-Type Ferroelectrics, ChemRxiv (2023)
- A Search for New Back Contacts for CdTe Solar Cells, Science Advances 9, eade376 (2023).
- A Computational Survey of Semiconductors for Power Electronics, Energy & Environmental Science, 12, 3338 (2019).
- Computationally Guided Discovery of Thermoelectric Materials, Nature Reviews Materials 2, 17503 (2017).
Materials Design Through Doping and Alloying
Alloying and doping are common ways to design materials with optimized functional and structural properties. From alloying copper with tin to improve mechanical strength of tools and weaponry in the Bronze Age to alloying iron to produce steel during the Industrial Revolution, alloys have become the cornerstone of human advacement. Similarly, doping enabled silicon microelectronics during the Digital Revolution. Like materials discovery (Theme 1), alloying and doping are often driven by chemical intuition and laborious experimental trial-and-error. First-principles computations can accelerate alloying and doping decisions by identifying alloy combinations, optimal compositions, and suitable dopants and their effect on the functional properties. We are using computational approaches to design materials for energy storage and conversion, and next-generation microelectronics.
- Synthesis and Calculations of Wurtzite Al1-xGdxN Heterostructural Alloys, Chemistry of Materials (2022).
- Controlling Defects to Achieve Reproducibly High Ionic Conductivity in Na3SbS4 Solid Electrolyte, Chemistry of Materials 34, 5634 (2022).
- Computational Design of Thermoelectric Alloys Through Optimization of Transport and Dopability, Materials Horizons 9, 720 (2022).
- Devil is in the Defects: Electronic Conductivity in Solid Electrolytes, Chemistry of Materials 18, 7484 (2021).
- Doping by Design: Finding New n-type Dopable ABX4 Zintl Phases for Thermoelectrics, Journal of Materials Chemistry A 8, 25306 (2020).
- Investigation of n-type Doping Strategies for Mg3Sb2, Journal of Materials Chemistry A 6, 13806 (2018).
Materials Discovery and Design in Large Chemical Spaces
Computational design of materials has become viable with the expansion of methods and databases. However, it remains challenging in practice to explore large chemical spaces with ab initio methods alone. It is estimated that there are more than a trillion plausible valence-balanced compositions when considering the elements in the periodic table and up to quarternary stoichiometry. Even more challenging is “inverse design” i.e., building a stable material that possess a specific set of properties. We are using a combination of ab initio methods and machine learning to search for new functional materials in vast chemical spaces.
- Upper-Bound Energy Minimization to Search for Stable Functional Materials with Graph Neural Networks, JACS Au 3, 113 (2023).
- Predicting Energy and Stability of Known and Hypothetical Crystals using Graph Neural Network, Patterns 2, 100361 (2021).
- Charting Lattice Thermal Conductivity for Inorganic Crystals and Discovering Rare Earth Chalcogenides for Thermoelectrics, Energy and Environmental Science, 14, 3559 (2021).
Open Science – Databases, Codes, Public Datasets
Thermoelectrics Design Lab: An open-access database of DFT-calculated thermoelectric properties of ~2700 inorganic materials
TEDesignLab: A Virtual Laboratory for Thermoelectric Material Design, Computational Materials Science 112A, 368 (2016)
www.prashungorai.org/tedesignlab
Pylada-defects: An open-source Python package for automation of first-principles point defect calculations
A Computational Framework for Automation of Point Defect Calculations, Computational Materials Science 130, 1 (2017)
github.com/pylada/pylada-defects
Anisotropy Atlas: A dataset of calculated lattice thermal conductivity tensors of ~2500 layered (quasi-2D) structures from the Inorganic Crystal Structure Database. The calculation methodology is documented in this paper.