arXiv subjects

Basita Das

Publications and source records attributed to Basita Das.

5 recordsLinked to original sources

arXiv2025

Multimodal transformers with elemental priors for phase classification of X-ray diffraction spectra

Classifying a crystalline solid's phase using X-ray diffraction (XRD) is a challenging endeavor, first because this is a poorly constrained problem as there are nearly limitless candidate phases to compare against a given experimental spectrum, and second because experimental signals are confounded by overlapping peaks, preferred orientations, and phase mixtures. To address this challenge, we develop Chem-XRD, a multimodal framework based on vision transformer (ViT) architecture with two defining features: (i) crystallographic data is constrained by elemental priors and (ii) phase is classified according to probabilities (not absolutes). Elemental information can be extracted from pre-synthesis precursors or post-synthesis elemental analysis. By combining structural and elemental modalities, Chem-XRD simultaneously predicts both the number and identity of phases of lead-halide perovskite materials and their decomposition products. Through integrated gradient calculations, we show that the model can adeptly adjust the contributions of structural and elemental modalities toward the final prediction of phase identification, achieving a level of interpretability beyond what self-attention mechanisms can achieve.

physics.app-ph↗

arXiv2024

A Self-Supervised Robotic System for Autonomous Contact-Based Spatial Mapping of Semiconductor Properties

Integrating robotically driven contact-based material characterization techniques into self-driving laboratories can enhance measurement quality, reliability, and throughput. While deep learning models support robust autonomy, current methods lack reliable pixel-precision positioning and require extensive labeled data. To overcome these challenges, we propose an approach for building self-supervised autonomy into contact-based robotic systems that teach the robot to follow domain expert measurement principles at high-throughputs. Firstly, we design a vision-based, self-supervised convolutional neural network (CNN) architecture that uses differentiable image priors to optimize domain-specific objectives, refining the pixel precision of predicted robot contact poses by 20.0% relative to existing approaches. Secondly, we design a reliable graph-based planner for generating distance-minimizing paths to accelerate the robot measurement throughput and decrease planning variance by 6x. We demonstrate the performance of this approach by autonomously driving a 4-degree-of-freedom robotic probe for 24 hours to characterize semiconductor photoconductivity at 3,025 uniquely predicted poses across a gradient of drop-casted perovskite film compositions, achieving throughputs over 125 measurements per hour. Spatially mapping photoconductivity onto each drop-casted film reveals compositional trends and regions of inhomogeneity, valuable for identifying manufacturing process defects. With this self-supervised CNN-driven robotic system, we enable high-precision and reliable automation of contact-based characterization techniques at high throughputs, thereby allowing the measurement of previously inaccessible yet important semiconductor properties for self-driving laboratories.

cs.RO↗

arXiv2023

Using Scalable Computer Vision to Automate High-throughput Semiconductor Characterization

High-throughput materials synthesis methods have risen in popularity due to their potential to accelerate the design and discovery of novel functional materials, such as solution-processed semiconductors. After synthesis, key material properties must be measured and characterized to validate discovery and provide feedback to optimization cycles. However, with the boom in development of high-throughput synthesis tools that champion production rates up to $10^4$ samples per hour with flexible form factors, most sample characterization methods are either slow (conventional rates of $10^1$ samples per hour, approximately 1000x slower) or rigid (e.g., designed for standard-size microplates), resulting in a bottleneck that impedes the materials-design process. To overcome this challenge, we propose a set of automated material property characterization (autocharacterization) tools that leverage the adaptive, parallelizable, and scalable nature of computer vision to accelerate the throughput of characterization by 85x compared to the non-automated workflow. We demonstrate a generalizable composition mapping tool for high-throughput synthesized binary material systems as well as two scalable autocharacterization algorithms that (1) autonomously compute the band gap of 200 unique compositions in 6 minutes and (2) autonomously compute the degree of degradation in 200 unique compositions in 20 minutes, generating ultra-high compositional resolution trends of band gap and stability. We demonstrate that the developed band gap and degradation detection autocharacterization methods achieve 98.5% accuracy and 96.9% accuracy, respectively, on the FA$_{1-x}$MA$_{x}$PbI$_3$, $0\leq x \leq 1$ perovskite semiconductor system.

eess.IV↗

arXiv2021

Effect of doping, photodoping and bandgap variation on the performance of perovskite solar cells

Most traditional semiconductor materials are based on the control of doping densities to create junctions and thereby functional and efficient electronic and optoelectronic devices. The technology development for halide perovskites had initially only rarely made use of the concept of electronic doping of the perovskite layer and instead employed a variety of different contact materials to create functionality. Only recently, intentional, or unintentional doping of the perovskite layer is more frequently invoked as an important factor explaining differences in photovoltaic or optoelectronic performance in certain devices. Here we use numerical simulations to study the influence of doping and photodoping on photoluminescence quantum yield as well as other device relevant metrics. We find that doping can improve the photoluminescence quantum yield by making radiative recombination faster. This effect can benefit or harm photovoltaic performance given that the improvement of photoluminescence quantum efficiency and open-circuit voltage is accompanied by a reduction of the diffusion length. This reduction will eventually lead to inefficient carrier collection at high doping densities. The photovoltaic performance might improve at an optimum doping density which depends on a range of factors such as the mobilities of the different layers and the ratio of the capture cross sections for electrons and holes.

cond-mat.mtrl-sci↗

arXiv2020

Defect tolerant device geometries

The term defect tolerance is widely used in literature to describe materials such as lead-halides which exhibit long non-radiative lifetimes of carriers despite possessing a large concentration of point defects. Studies on defect tolerance of materials mostly look at the properties of the host material and/or the chemical nature of defects that affect the capture coefficients of defects. However, the recombination activity of a defect is not only a function of its capture coefficients alone but are also dependent on the electrostatics and the design of the layer stack of a photovoltaic device. Here we study the influence of device geometry on defect tolerance by combining calculations of capture coefficients with device simulations. We derive generic device design principles which can inhibit recombination inside a photovoltaic device for a given set of capture coefficients based on the idea of slowing down the slower of the two processes (electron and hole capture) even further by modifying electron and hole injection into the absorber layer. We use the material parameters and typical p-i-n device geometry representing methylammonium lead halide perovskites solar cells to illustrate the application of our generic design principles to improve specific devices .

cond-mat.mtrl-sci↗