Dr. Anand Chandrasekaran Profile

Dr. Anand Chandrasekaran

at Schrodinger Inc

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Publications (2)

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Proceedings Article | 1 October 2023 Poster + Paper

Hole transport materials for QLEDs: a combined approach of machine learning and atomistic simulation

Hadi Abroshan, H. Shaun Kwak, Anand Chandrasekaran, Alex Chew, Alexandr Fonari, Mathew Halls

Proceedings Volume 12659, 1265906 (2023) https://doi.org/10.1117/12.2675778

KEYWORDS: Machine learning, Active learning, Optoelectronics, Materials properties, Design and modelling, Quantum simulation, Quantum dot light emitters

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QLEDs have emerged as an alternative for optoelectronic applications. However, for widespread application of QLEDs, the device efficiency is required to be improved. There is a significant energy level mismatch between the valence band of commonly used quantum dots (QDs) and the HOMO level of traditional hole transport materials (HTMs). Given the small energy level mismatch between the conduction bands of the QDs and commercial electron transport materials, charge carriers in the light-emitting layer are imbalanced. Such a charge imbalance decreases the efficiency of QLED devices, and thus it is of great importance to design novel HTL materials with small energy mismatch with the QDs. Given the numerous potential molecules in the organic space, employing expensive and time-consuming approaches based on chemical intuition and trial-and-error experimentation is practically ineffective. Thus, realizing next-generation QLEDs technologies requires a paradigm change in materials design and development. Here, we combine active learning (AL) and high-throughput quantum mechanical calculations as a novel strategy to efficiently navigate the search space in a large materials library. The AL enables a systematic material screening by accounting multiple optoelectronic properties while minimizing the number of calculations. We further evaluated the top candidates using atomistic simulations and machine learning to investigate charge mobility and thermal stability in their amorphous films. This work offers guidelines for efficient computational screening of materials for QLEDs, reducing laborious, time-consuming, and expensive computer simulations, materials synthesis, and device fabrication.

Proceedings Article | 1 August 2021 Presentation + Paper

Accelerated design and optimization of novel OLED materials via active learning

Hadi Abroshan, Anand Chandrasekaran, Paul Winget, Yuling An, Shaun Kwak, Christopher Brown, Mathew Halls

Proceedings Volume 11808, 118080S (2021) https://doi.org/10.1117/12.2598140

KEYWORDS: Organic light emitting diodes, Optoelectronics, Molecules, Data modeling, Oxidation, Machine learning, Organic electronics, Optoelectronic devices

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To date, the development of organic light-emitting diode (OLED) materials has been primarily based on a combination of chemical intuition and trial-and-error experimentation. The approach is often expensive and time-consuming, let alone in most instances fails to offer new materials leading to higher efficiencies. Data-driven approaches have emerged as a powerful tool to accelerate the design and discovery of novel materials with multifunctional properties for next generation OLED technologies. Virtual high-throughput methods assisted by machine learning (ML) enable a broad screening of chemical space to predict material properties and suggest new candidates for OLEDs. In order to build reliable predictive ML models for OLED materials, it is required to create and manage a high volume of data which not only maintain high accuracy but also properly assess the complexity of materials chemistry in the OLED space. Active learning (AL) is among several strategies developed to face the challenge in both materials science and life science applications, where the data management in large-scale becomes a main bottleneck. Here, we present a workflow that efficiently combines AL with atomic-scale simulations to reliably predict optoelectronic properties of OLED materials. This study provides a robust and validated framework to account for multiple parameters that simultaneously influence OLED performance. Results of this work pave the way for a fundamental understanding of optoelectronic performance of emergent layers from a molecular perspective, and further screen candidate materials with superior efficiencies before laborious simulations, synthesis, and device fabrication.

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