Computational approaches for organic semiconductors: from chemical and physical understanding to predicting new materials
While a complete understanding of organic semiconductor (OSC) design principles remains
elusive, computational methods─ ranging from techniques based in classical and quantum …
elusive, computational methods─ ranging from techniques based in classical and quantum …
Modeling excited states of molecular organic aggregates for optoelectronics
FJ Hernández, R Crespo-Otero - Annual Review of Physical …, 2023 - annualreviews.org
Light-driven phenomena in organic molecular aggregates underpin several mechanisms
relevant to optoelectronic applications. Modeling these processes is essential for aiding the …
relevant to optoelectronic applications. Modeling these processes is essential for aiding the …
Identification of Unknown Inverted Singlet–Triplet Cores by High-Throughput Virtual Screening
Molecules where the energy of the lowest excited singlet state is found below the energy of
the lowest triplet state (inverted singlet–triplet molecules) are extremely rare. It is particularly …
the lowest triplet state (inverted singlet–triplet molecules) are extremely rare. It is particularly …
Fine-tuning GPT-3 for machine learning electronic and functional properties of organic molecules
We evaluate the effectiveness of fine-tuning GPT-3 for the prediction of electronic and
functional properties of organic molecules. Our findings show that fine-tuned GPT-3 can …
functional properties of organic molecules. Our findings show that fine-tuned GPT-3 can …
Electronic, redox, and optical property prediction of organic π-conjugated molecules through a hierarchy of machine learning approaches
Accelerating the development of π-conjugated molecules for applications such as energy
generation and storage, catalysis, sensing, pharmaceuticals, and (semi) conducting …
generation and storage, catalysis, sensing, pharmaceuticals, and (semi) conducting …
The application of chemical similarity measures in an unconventional modeling framework c-RASAR along with dimensionality reduction techniques to a …
A Banerjee, K Roy - Scientific Reports, 2024 - nature.com
With the exponential progress in the field of cheminformatics, the conventional modeling
approaches have so far been to employ supervised and unsupervised machine learning …
approaches have so far been to employ supervised and unsupervised machine learning …
Towards a comprehensive data infrastructure for redox-active organic molecules targeting non-aqueous redox flow batteries
The shift of energy production towards renewable, yet at times inconsistent, resources like
solar and wind have increased the need for better energy storage solutions. An emerging …
solar and wind have increased the need for better energy storage solutions. An emerging …
Molecular Geometry Impact on Deep Learning Predictions of Inverted Singlet–Triplet Gaps
L Barneschi, L Rotondi, D Padula - The Journal of Physical …, 2024 - ACS Publications
We present a deep learning model able to predict excited singlet–triplet gaps with a mean
absolute error (MAE) of≈ 20 meV to obtain potential inverted singlet–triplet (IST) …
absolute error (MAE) of≈ 20 meV to obtain potential inverted singlet–triplet (IST) …
Towards a fast machine-learning-assisted prediction of the mechanoelectric response in organic crystals
Organic semiconductors can improve the performance of wearable electronics, e-skins, and
pressure sensors by exploiting their mechanoelectric response. However, identifying new …
pressure sensors by exploiting their mechanoelectric response. However, identifying new …
Virtual screening for organic solar cells and light emitting diodes
The field of organic semiconductors is multifaceted and the potentially suitable molecular
compounds are very diverse. Representative examples include discotic liquid crystals, dye …
compounds are very diverse. Representative examples include discotic liquid crystals, dye …