Recent progress in generative adversarial networks applied to inversely designing inorganic materials: A brief review

R Jabbar, R Jabbar, S Kamoun - Computational Materials Science, 2022 - Elsevier
Generative adversarial networks (GANs) are deep generative models (GMs) that have
recently attracted attention owing to their impressive performance in generating completely …

Identification of Unknown Inverted Singlet–Triplet Cores by High-Throughput Virtual Screening

OH Omar, X Xie, A Troisi, D Padula - Journal of the American …, 2023 - ACS Publications
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 …

Accurate ionization potentials, electron affinities, and band gaps from the ωLH22t range-separated local hybrid functional: No tuning required

S Fürst, M Kaupp - Journal of Chemical Theory and …, 2023 - ACS Publications
The optimal tuning (OT) of range-separated hybrid (RSH) functionals has been proposed as
the currently most accurate DFT-based way to compute the relevant quantities required for …

Roles and opportunities for machine learning in organic molecular crystal structure prediction and its applications

RJ Clements, J Dickman, J Johal, J Martin, J Glover… - MRS Bulletin, 2022 - Springer
The field of crystal structure prediction (CSP) has changed dramatically over the past
decade and methods now exist that will strongly influence the way that new materials are …

Accelerated organic crystal structure prediction with genetic algorithms and machine learning

A Kadan, K Ryczko, A Wildman, R Wang… - Journal of Chemical …, 2023 - ACS Publications
We present a high-throughput, end-to-end pipeline for organic crystal structure prediction
(CSP)─ the problem of identifying the stable crystal structures that will form from a given …

Organic materials repurposing, a data set for theoretical predictions of new applications for existing compounds

ÖH Omar, T Nematiaram, A Troisi, D Padula - Scientific Data, 2022 - nature.com
We present a data set of 48182 organic semiconductors, constituted of molecules that were
prepared with a documented synthetic pathway and are stable in solid state. We based our …

Towards a comprehensive data infrastructure for redox-active organic molecules targeting non-aqueous redox flow batteries

R Duke, V Bhat, P Sornberger, SA Odom, C Risko - Digital Discovery, 2023 - pubs.rsc.org
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 …

Ground‐State Orbital Descriptors for Accelerated Development of Organic Room‐Temperature Phosphorescent Materials

Y Mao, X Yao, Z Yu, Z An, H Ma - … Chemie International Edition, 2024 - Wiley Online Library
Organic materials with room‐temperature phosphorescence (RTP) are in high demand for
optoelectronics and bioelectronics. Developing RTP materials highly relies on expert …

From Concept to Synthesis: Developing Heat-Resistant High Explosives through Automated High-Throughput Virtual Screening

ZJ Lu, Y Hu, WS Dong, WL Cao, TW Wang… - The Journal of …, 2023 - ACS Publications
In this paper, we investigate the utilization of high-throughput virtual screening (HTVS) to
identify and develop novel heat-resistant high explosives (HRHEs) that possess a …

Reorganization energies of flexible organic molecules as a challenging target for machine learning enhanced virtual screening

K Chen, C Kunkel, K Reuter, JT Margraf - Digital Discovery, 2022 - pubs.rsc.org
The molecular reorganization energy λ strongly influences the charge carrier mobility of
organic semiconductors and is therefore an important target for molecular design. Machine …