CASE STUDYDe Novo Design ofHole-Conducting
Molecules for
Organic Electronics
Panasonic and Schrödinger scientists designed over 50 novel
molecules with improved hole mobility by performing large-scaledensity functional theory (DFT) calculations and machine learning
inverse design.

Executive Summary
Tremendous Time Saved andCost Reduced•

3 de novo design methods developedand assessed•

14 million molecules enumerated
and screened•

9,000 DFT calculations performed•

Over 50 molecules identified withtarget performance profile

Performance Improved
Identified molecules with lower holereorganization energy (up to 22%reduction) than the lowest one intraining dataset

Highly Predictive Machine
Learning (ML) Models Developed
Leveraged data based on DFT calculationsof 250,000 molecules

New Insights Proposed
High quality de novo design
complements molecular enumerationand virtual screening

2Charge carrier mobility is one of themost important characteristics ofsemiconductor materials.
Applications in printed electronics demand molecules withhigh mobility. Despite rapid progress toward discovery of newmolecules with improved mobility, challenges persist. For
example, the impact of topological shape of the moleculeson the magnitude of hole mobility is not well understoodfor optimized molecular design, and it can be extremely
costly and time-consuming to synthesize and assess every
candidate molecule. Atomic simulations and machine learningtechnologies can reveal novel insights which are inaccessible toexperimental methods alone.

“With Schrödinger’s advanced simulation tools,
we were able to explore millions of molecules
and target tens of potential candidates
within a short period of time, which is simply
unfeasible with traditional approaches. Thislevel of computational power changes theway we innovate. Both the scientific expertiseand the excellence of technology Schrödinger
brings to the table give us confidence infuture collaborations.”— Nobuyuki N. Matsuzawa​General Manager of Panasonic Corporation3

Approach
Scientists at Panasonic are challenged to develop novel organic
semiconductor materials with higher efficiency. In order todrive innovation, Panasonic teamed up with Schrödinger forde novo design of new molecules leveraging the computational
power and expertise of Schrödinger of high-throughput DFT
calculations, machine learning/deep learning model building,and chemical enumeration.

Phase

1

Phase

2

Phase

3

Built highly predictive machine learningmodels using DFT calculations of 250,000
heteroacenes, and Schrödinger’s automatedquantitative structure−activity relationship tool
AutoQSAR/DeepChem.

Explored 3 de novo methods (Continuous,REINVENT, and JTNN). Generation of molecules
minimizing hole reorganization energy were
run with trained machine learning models foreach method. The top 3,000 molecules foreach method (9,000 in total) were selected fordownstream DFT analysis.

Hole reorganization energy andhole mobility of the top-ranked
molecules were calculated by DFT/
MD with Schrödinger Materials
Science tools, and over 50 novel
molecules were identified.

4Results
Scientists from Panasonic and Schrödinger
performed a thorough benchmark study of three denovo methods and identified molecular structuresin the heteroacene family, which may show
improved carrier transport properties.1 Schrödinger
demonstrated strong large-scale computing
capabilities and in-house expertise in machinelearning to develop de novo methods based on
knowledge and literature reports, building bayesian
optimizers and reward engineering.

ResultResultResultOver 50 molecules wereidentified to have lower
hole reorganizationenergy than lowest
data in the training set(up to 22% reduction).
We expect significant
enhancement in holemobility by the reductionof the reorganizationenergy in the newlydesigned molecules.
The best scoring
compound was foundby the JTNN method,
followed by REINVENT.
However, on the whole,
the REINVENT method
generated the best top
1,000 molecules.
Based on the findings,
the scientists proposethat high-quality de novo
methods should optimize
for compounds that “fillholes” in the space of theenumeration, generating
highly targeted molecules.

5Conclusion
Scientists from Panasonic and Schrödinger have applied three major classesof de novo molecular design (inverse design) methods to the challenging
problem of improving charge carrier mobility in materials science. Theyevaluated the performance of these methods via large-scale DFT calculationof hole reorganization energy. These methods present an attractive
complement to molecular enumeration and virtual screening, and recent
advances in deep learning for de novo design have yielded promising results
for the design of novel materials.
References
1. De Novo Design of Molecules with Low Hole Reorganization Energy
Based on a Quarter-Million Molecule DFT Screen
Gabriel Marques*, Karl Leswing, Tim Robertson, David Giesen, Mathew
D. Halls, Alexander Goldberg, Kyle Marshall, Joshua Staker, Tsuguo
Morisato, Hiroyuki Maeshima, Hideyuki Arai, Masaru Sasago, Eiji Fujii,
and Nobuyuki N. Matsuzawa*J. Phys. Chem. A 2021, 125, 33, 7331–7343

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