23 research outputs found
First Principles Investigation of Polymorphism in Halide Perovskites
Halide perovskites have been extensively studied as materials of interest for
optoelectronic applications. There is a major emphasis on ways to tailor the
stability, defect behavior, electronic band structure, and optical absorption
in halide perovskites, by changing the composition or structure. In this work,
we present our contribution to this field in the form of a comprehensive
computational investigation of properties as a function of the perovskite
phase, different degrees of lattice strains and octahedral distortion and
rotation, and the ordering of cations in perovskite alloys. We performed first
principles-based density functional theory computations using multiple
semi-local and non-local hybrid functionals to calculate optimized lattice
parameters, energies of decomposition, electronic band gaps, and theoretical
photovoltaic efficiencies. Trends and critical observations from the
high-throughput dataset are discussed, especially in terms of the range of
optoelectronic properties achievable while keeping the material in a
(meta)stable phase or distorted, strained, or differently ordered polymorph.
All data is made openly available to the community and is currently being
utilized to train state-of-the-art machine learning models for accelerated
prediction and discovery, as well as to guide rational experimental discovery
Accelerating Defect Predictions in Semiconductors Using Graph Neural Networks
Here, we develop a framework for the prediction and screening of native
defects and functional impurities in a chemical space of Group IV, III-V, and
II-VI zinc blende (ZB) semiconductors, powered by crystal Graph-based Neural
Networks (GNNs) trained on high-throughput density functional theory (DFT)
data. Using an innovative approach of sampling partially optimized defect
configurations from DFT calculations, we generate one of the largest
computational defect datasets to date, containing many types of vacancies,
self-interstitials, anti-site substitutions, impurity interstitials and
substitutions, as well as some defect complexes. We applied three types of
established GNN techniques, namely Crystal Graph Convolutional Neural Network
(CGCNN), Materials Graph Network (MEGNET), and Atomistic Line Graph Neural
Network (ALIGNN), to rigorously train models for predicting defect formation
energy (DFE) in multiple charge states and chemical potential conditions. We
find that ALIGNN yields the best DFE predictions with root mean square errors
around 0.3 eV, which represents a prediction accuracy of 98 % given the range
of values within the dataset, improving significantly on the state-of-the-art.
Models are tested for different defect types as well as for defect charge
transition levels. We further show that GNN-based defective structure
optimization can take us close to DFT-optimized geometries at a fraction of the
cost of full DFT. DFT-GNN models enable prediction and screening across
thousands of hypothetical defects based on both unoptimized and
partially-optimized defective structures, helping identify electronically
active defects in technologically-important semiconductors
Large Scale Benchmark of Materials Design Methods
Lack of rigorous reproducibility and validation are major 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 both 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_leaderboar
Rational Design of Polymer Dielectrics Using First Principles Computations and Machine Learning
While intuition-driven experiments and serendipity have guided traditional materials discovery, computational strategies have become increasingly prevalent and a powerful complement to experiments in modern day materials research. A novel approach for efficient materials design is ârational co-designâ, where high-throughput computational screening is used synergistically with experimental synthesis and testing. In this Thesis, the utility and promise of such an approach was demonstrated for the design of advanced polymer dielectrics for electrostatic energy storage applications. Density functional theory computations were applied to study the structural, electronic and dielectric properties of polymers, based on which targeted synthesis and property measurements were carried out for promising candidates. These co-design efforts led to the identification of potential replacements for present day âstandardâ dielectrics (such as biaxially oriented polypropylene) not only by new organic polymer candidates within known generic polymer subclasses (e.g., polyurea, polythiourea, polyimide), but also by organometallic polymers, a hitherto untapped but promising chemical subspace. Further, the prospects of significantly accelerating the materials design process using state-of-the-art machine learning techniques were explored. Vast computational data generated as part of this work was mined for the development of accurate âinstant predictionâ and âdesignâ models for the relevant properties of polymers. These models were converted into user-friendly polymer design tools, and along with the computational and experimental data, compiled in the form of a web-based application (http://khazana.uconn.edu/polymer_genome/) to facilitate the rapid design and discovery of polymer dielectrics