Machine learning models are increasingly being utilized to predict outcomes of organic chemical reactions. A large amount of reaction data is used to train these models, which is in …
A closed-loop, autonomous molecular discovery platform driven by integrated machine learning tools was developed to accelerate the design of molecules with desired properties …
Electrochemical research often requires stringent combinations of experimental parameters that are demanding to manually locate. Recent advances in automated instrumentation and …
Out-of-equilibrium electrochemical reaction mechanisms are notoriously difficult to characterize. However, such reactions are critical for a range of technological applications …
H Shimakawa, A Kumada, M Sato - npj Computational Materials, 2024 - nature.com
Data-driven materials science has realized a new paradigm by integrating materials domain knowledge and machine-learning (ML) techniques. However, ML-based research has often …
F Coppola, L Frigau, J Markelj… - Journal of the …, 2023 - ACS Publications
Non-destructive, fast, and accurate methods of dating are highly desirable for many heritage objects. Here, we present and critically evaluate the use of near-infrared (NIR) spectroscopic …
In a recent article in this journal, van Gerwen et al (2022 Mach. Learn.: Sci. Technol. 3 045005) presented a kernel ridge regression model to predict reaction barrier heights. Here …
H Yang, J Wang, R Mo, P Hu, X Liu, Y Liu, J Cui… - Separation and …, 2025 - Elsevier
Immobilization of cadmium (Cd) in soil is a complex systematic process in which biochar materials and experimental conditions need to be selected from an infinite space of …
H Guo, Y Lu, Z Lei, H Bao, M Zhang, Z Wang… - Nature …, 2024 - nature.com
Carbon quantum dots (CQDs) have versatile applications in luminescence, whereas identifying optimal synthesis conditions has been challenging due to numerous synthesis …