Autonomous platforms for data-driven organic synthesis DOI Creative Commons
Wenhao Gao, Priyanka Raghavan,

Connor W. Coley

и другие.

Nature Communications, Год журнала: 2022, Номер 13(1)

Опубликована: Фев. 28, 2022

Achieving autonomous multi-step synthesis of novel molecular structures in chemical discovery processes is a goal shared by many researchers. In this Comment, we discuss key considerations what an ideal platform may look like and the apparent state art. While most hardware challenges can be overcome with clever engineering, other will require advances both algorithms data curation.

Язык: Английский

The case for data science in experimental chemistry: examples and recommendations DOI
Junko Yano, Kelly J. Gaffney, John M. Gregoire

и другие.

Nature Reviews Chemistry, Год журнала: 2022, Номер 6(5), С. 357 - 370

Опубликована: Апрель 21, 2022

Язык: Английский

Процитировано

62

Machine Learning Yield Prediction from NiCOlit, a Small-Size Literature Data Set of Nickel Catalyzed C–O Couplings DOI
Jules Schleinitz, Maxime Langevin,

Yanis Smail

и другие.

Journal of the American Chemical Society, Год журнала: 2022, Номер 144(32), С. 14722 - 14730

Опубликована: Авг. 8, 2022

Synthetic yield prediction using machine learning is intensively studied. Previous work has focused on two categories of data sets: high-throughput experimentation data, as an ideal case study, and sets extracted from proprietary databases, which are known to have a strong reporting bias toward high yields. However, predicting yields published reaction remains elusive. To fill the gap, we built set nickel-catalyzed cross-couplings organic publications, including scope optimization information. We demonstrate importance source failed experiments emphasize how publication constraints shape exploration chemical space by synthetic community. While models still fail perform out-of-sample predictions, this shows that adding knowledge enables fair predictions in low-data regime. Eventually, hope unique public database will foster further improvements methods for more realistic context.

Язык: Английский

Процитировано

59

Comparative Evaluation of Light‐Driven Catalysis: A Framework for Standardized Reporting of Data** DOI Creative Commons
Dirk Ziegenbalg, Andrea Pannwitz, Sven Rau

и другие.

Angewandte Chemie International Edition, Год журнала: 2022, Номер 61(28)

Опубликована: Июнь 13, 2022

Light-driven homogeneous and heterogeneous catalysis require a complex interplay between light absorption, charge separation, transfer, catalytic turnover. Optical irradiation parameters as well reaction engineering aspects play major roles in controlling performance. This multitude of factors makes it difficult to objectively compare light-driven catalysts provide an unbiased performance assessment. Scientific Perspective highlights the importance collecting reporting experimental data catalysis. A critical analysis benefits limitations commonly used indicators is provided. Data collection according FAIR principles discussed context future automated analysis. The authors propose minimum dataset basis for unified community encouraged support development this parameter list through open online repository.

Язык: Английский

Процитировано

53

Single-atom heterogeneous catalysts for sustainable organic synthesis DOI
Georgios Giannakakis, Sharon Mitchell, Javier Pérez-Ramı́rez

и другие.

Trends in Chemistry, Год журнала: 2022, Номер 4(4), С. 264 - 276

Опубликована: Фев. 21, 2022

Язык: Английский

Процитировано

52

Autonomous platforms for data-driven organic synthesis DOI Creative Commons
Wenhao Gao, Priyanka Raghavan,

Connor W. Coley

и другие.

Nature Communications, Год журнала: 2022, Номер 13(1)

Опубликована: Фев. 28, 2022

Achieving autonomous multi-step synthesis of novel molecular structures in chemical discovery processes is a goal shared by many researchers. In this Comment, we discuss key considerations what an ideal platform may look like and the apparent state art. While most hardware challenges can be overcome with clever engineering, other will require advances both algorithms data curation.

Язык: Английский

Процитировано

49