APPLICATION NOTE

Accelerating
the design andoptimization of
OLED materials
using active
learning
OLEDs (Organic Light-Emitting Diodes) are extensively used in digital
displays such as those in smart phones, television screens, computer
monitors, and game consoles. They contain organic molecules withunique electronic structures that create light in the visible part of thespectrum through a process called electroluminescence. For an OLED
to be commercially valuable, it should satisfy several constraints,
including high efficiency, low-cost fabrication, and good electrochemical
stability for long-term operation, making discovery of novel OLED
materials a challenging problem.

Challenges
Molecular modeling and simulation tools have
proven effective in materials discovery and areincreasingly deployed in industrial R&D. Although
digital simulations have offered tremendous timesavings for R&D workflows compared to traditional
experimental approaches, several challenges persist:•The potential chemical space for materials
design and discovery is massive, even for highly
constrained problems•

Predictions based on density functional
theory (DFT) calculations can be laborious andcomputationally expensive, limiting the numberof candidates evaluated within fixed timescales
and resources•

It is challenging to maintain a high levelof accuracy to properly assess the complexityof materials

Solution: Active
Learning Workflows
A new approach is required to guide scientists tothe best performing or useful candidates. Thanks
to the application of a machine learning (ML)paradigm called “active learning” (AL), Schrödinger
has made this problem readily tractable. Recently,
Schrödinger has developed active learning
workflows which leverage the synergy between
physics-based simulations and machine learningfor optoelectronic properties predictions. The active
learning workflow enables scientists to zoom in on
the “best-performing” portion of a given sample
space in a more efficient and cost-effective manner.
The workflow allows:•

Automated active learning calculations withminimum users input•

Accounting for multiple optoelectronicparameters simultaneously formaterials discovery•

Combining active learning with DFT to efficiently
identify materials with optimal properties•

Employing built-in descriptors and fingerprints
to featurize chemical structures•

Building high-performance ML models using
adaptive design procedures•

Minimizing the number of time-consuming
physics-based calculations“The AL workflow enables accelerated design and discoveryof optoelectronic materials. The workflow is fully automated,significantly reducing the number of physics-based simulations
to predict materials properties in an extensive library. The rapid
screening of datasets allows a better understanding of structurefunction relationships for systematic design and application ofoptoelectronic materials with higher efficiency.”­—Hadi Abroshan, Senior Scientist, Schrödinger

2

Case Study
Recent studies by Schrödinger, published in Frontiers in Chemistry and presented atSID-Display Week 2022, have demonstrated the active learning paradigm for OLED
materials discovery.1,2
To explore the best performing hole-transporting molecules for OLED, scientists
screened a large pool of 9,000 molecules using the automated active learning workflow.
The initial training set included 50 molecules for which a machine learning model wasdeveloped supporting efficient multi-parameter optimization (MPO). The ML model wasthen used to predict the rest of 8,950 molecules in the pool, each of which only costs
a fraction of second. Top 50 molecules with higher MPO score and uncertainty wereselected for DFT calculations and the calculated data were input for the next iterationof ML model training. The size of the training set for active learning was increased from50 to 550 molecules in 10 iterations.
Active learning is the iteration of these steps until the DFT calculated data and themachine learning predicted data converge with sufficient accuracy. By using the AL
approach, the scientific team was able to screen the chemical space of the materials
library 18 times faster than the traditional approach of expensive quantum mechanicalcalculations, leading to considerable time and resource savings.

The Active Learning Workflow

Diagram of theautomated active
learning workflow
for the design anddiscovery of novel
OLED materials
3References
1. Active Learning Accelerates Design and Optimization of HoleTransporting Materials for Organic Electronics
Hadi Abroshan, H. Shaun Kwak, Yuling An, Christopher Brown,
Anand Chandrasekaran, Paul Winget and Mathew D. Halls, Front.
Chem., 2022, 9, 800371
2. Active Learning for the Design of Novel OLED Materials
Hadi Abroshan, Anand Chandrasekaran, Paul Winget, Yuling An,Shaun Kwak, Christopher T. Brown, Tsuguo Morisato, and Mathew
D. Halls, SID Symposium Digest of Technical Papers, 2022, 53, 885-888

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