Fine-tuning GPT-3 for machine learning electronic and functional properties of organic molecules

Z Xie, X Evangelopoulos, ÖH Omar, A Troisi… - Chemical …, 2024 - pubs.rsc.org
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 …

Multifidelity machine learning for molecular excitation energies

V Vinod, S Maity, P Zaspel… - Journal of Chemical …, 2023 - ACS Publications
The accurate but fast calculation of molecular excited states is still a very challenging topic.
For many applications, detailed knowledge of the energy funnel in larger molecular …

Resilience of Hund's rule in the chemical space of small organic molecules

A Majumdar, R Ramakrishnan - Physical Chemistry Chemical Physics, 2024 - pubs.rsc.org
We embark on a quest to identify small molecules in the chemical space that can potentially
violate Hund's rule. Utilizing twelve TDDFT approximations and the ADC (2) many-body …

Machine Learning for the Design of Novel OLED Materials

H Abroshan, P Winget, HS Kwak, Y An… - Machine Learning in …, 2022 - ACS Publications
One of the central paradigms in materials science is that data-driven methods will decrease
the time needed to develop optimal solutions. In other words, the time required to go from …

Optimized machine learning techniques enable prediction of organic dyes photophysical properties: Absorption wavelengths, emission wavelengths, and quantum …

KD Mahato, U Kumar - Spectrochimica Acta Part A: Molecular and …, 2024 - Elsevier
Applications of organic dyes, ranging from basic research to industry, are functions of their
photophysical properties. Two important aspects—(1) knowledge of the photophysical …

Structure prediction from spectra amidst dynamical heterogeneity in melanin

A Choudhury, R Ramakrishnan, D Ghosh - Chemical Communications, 2024 - pubs.rsc.org
Melanin is a biopolymer pigment that plays a central role in skin photoprotection. Its
extensive chemical and dynamical heterogeneity imparts this property through a broad …

Harnessing quantum power: Revolutionizing materials design through advanced quantum computation

Z Guo, R Li, X He, J Guo, S Ju - Materials Genome Engineering …, 2024 - Wiley Online Library
The design of advanced materials for applications in areas of photovoltaics, energy storage,
and structural engineering has made significant strides. However, the rapid proliferation of …

UV–visible absorption spectra of solvated molecules by quantum chemical machine learning

Z Chen, FC Bononi, CA Sievers, WY Kong… - Journal of Chemical …, 2022 - ACS Publications
Predicting UV–visible absorption spectra is essential to understand photochemical
processes and design energy materials. Quantum chemical methods can deliver accurate …

[HTML][HTML] Chemical design by artificial intelligence

DH Ess, KE Jelfs, HJ Kulik - The Journal of Chemical Physics, 2022 - pubs.aip.org
Empirical principles and structure–property relations derived from chemical intuition have for
centuries driven the design and synthesis of materials and molecules. In recent years …

[HTML][HTML] Machine learning based hybrid ensemble models for prediction of organic dyes photophysical properties: Absorption wavelengths, emission wavelengths …

KD Mahato, SS Kumar Das, C Azad, U Kumar - APL Machine Learning, 2024 - pubs.aip.org
Fluorescent organic dyes are extensively used in the design and discovery of new materials,
photovoltaic cells, light sensors, imaging applications, medicinal chemistry, drug design …