Computational methods for asymmetric catalysis DOI
Sharon Pinus, Jérôme Genzling, Mihai Burai Patrascu

et al.

Nature Catalysis, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 3, 2024

Language: Английский

Beyond molecular structure: critically assessing machine learning for designing organic photovoltaic materials and devices DOI Creative Commons
Martin Seifrid, Stanley Lo, Dylan G. Choi

et al.

Journal of Materials Chemistry A, Journal Year: 2024, Volume and Issue: 12(24), P. 14540 - 14558

Published: Jan. 1, 2024

We assess state of machine learning for organic photovoltaic devices and data availability within the field, discuss best practices in representations model selection, release a comprehensive dataset fabrication conditions.

Language: Английский

Citations

3
Autonomous millimeter scale high throughput battery research system DOI Creative Commons
Fuzhan Rahmanian, Stefan Fuchs, Bojing Zhang

et al.

Digital Discovery, Journal Year: 2024, Volume and Issue: 3(5), P. 883 - 895

Published: Jan. 1, 2024

The high-throughput Auto-MISCHBARES platform streamlines reliable autonomous experimentation across laboratory devices through scheduling, quality control, live feedback, and real-time data management, including measurement, validation analysis.

Language: Английский

Citations

2
High-throughput experimentation and machine learning-promoted synthesis of α-phosphoryloxy ketones via Ru-catalyzed P(O)O-H insertion reactions of sulfoxonium ylides DOI
Lin An, Jingyuan Liu,

Yougen Xu

et al.

Science China Chemistry, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 10, 2024

Language: Английский

Citations

2
Rapid Prediction of Conformationally-Dependent DFT-Level Descriptors using Graph Neural Networks for Carboxylic Acids and Alkyl Amines DOI Creative Commons
Brittany C. Haas, Melissa A. Hardy, Shree Sowndarya S. V.

et al.

Digital Discovery, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 28, 2024

Data-driven reaction discovery and development is a growing field that relies on the use of molecular descriptors to capture key information about substrates, ligands, targets. Broad adaptation this strategy hindered by associated computational cost descriptor calculation, especially when considering conformational flexibility. Descriptor libraries can be precomputed agnostic application reduce burden data-driven development. However, as one often applies these models evaluate novel hypothetical structures, it would ideal predict compounds on-the-fly. Herein, we report DFT-level for ensembles 8528 carboxylic acids 8172 alkyl amines towards goal. Employing 2D 3D graph neural network architectures trained culminated in predictive molecule-level descriptors, well bond- atom-level conserved reactive site (carboxylic acid or amine). The predictions were confirmed robust an external validation set medicinally-relevant amines. Additionally, retrospective study correlating rate amide coupling reactions demonstrated suitability predicted downstream applications. Ultimately, enable high-fidelity vast number potential greatly increasing accessibility

Language: Английский

Citations

2
Computational methods for asymmetric catalysis DOI
Sharon Pinus, Jérôme Genzling, Mihai Burai Patrascu

et al.

Nature Catalysis, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 3, 2024

Language: Английский

Citations

2