The photochemical inheritance of Eduardo Lissi and Juan Grotewold and the intersystem crossings with other inheritances. [PDF]
Photochem PhotobiolEduardo Lissi, Dorita Adamo‐Lissi and Juan Grotewold in Aberystwyth, Wales, winter 1961. Upon their return to Buenos Aires in 1963, with their PhD degrees, Lissi and Grotewold started a Chemical Kinetics and Photochemistry group in the School of Sciences (University of Buenos Aires).
Braslavsky SE, Previtali CM.
europepmc +2 more sources
Proton-Transfer-Reaction Mass Spectrometry: Applications in Atmospheric Sciences
Chemical Reviews, 2017 Bin Yuan +2 more
exaly +2 more sources
Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems [PDF]
Chemical Reviews, 2021 Machine learning models are poised to make a transformative impact on chemical sciences by dramatically accelerating computational algorithms and amplifying insights available from computational chemistry methods.
J. Keith +6 more
semanticscholar +1 more source
Generative Models as an Emerging Paradigm in the Chemical Sciences
Journal of the American Chemical Society, 2023 Traditional computational approaches to design chemical species are limited by the need to compute properties for a vast number of candidates, e.g., by discriminative modeling.
Dylan M. Anstine, O. Isayev
semanticscholar +1 more source
Journal of Chemical Education, 2023 All learners have a contribution to make to the development of the Chemical Sciences, be that in novel ways to teach, and their perspectives and contexts, but also in research, both in chemical education and the wider Chemical Sciences. Through four case
M. Khan +11 more
semanticscholar +1 more source
NEXTorch: A Design and Bayesian Optimization Toolkit for Chemical Sciences and Engineering
Journal of Chemical Information and Modeling, 2021 Automation and optimization of chemical systems require well-informed decisions on what experiments to run to reduce time, materials, and/or computations. Data-driven active learning algorithms have emerged as valuable tools to solve such tasks. Bayesian
Yifan Wang, Tai-Ying Chen, D. Vlachos
semanticscholar +1 more source
Compressing physics with an autoencoder: Creating an atomic species representation to improve machine learning models in the chemical sciences. [PDF]
Journal of Chemical Physics, 2018 We define a vector quantity which corresponds to atomic species identity by compressing a set of physical properties with an autoencoder. This vector, referred to here as the elemental modes, provides many advantages in downstream machine learning tasks.
John E. Herr +3 more
semanticscholar +1 more source
ISOTOPE SEPARATION BY IONIC EXPANSION IN A MAGNETIC FIELD
Proceedings of the National Academy of Sciences of the United States of America, 1955 10 C. F. Garbers, C. H. Eugster, and P. Karrer, Helv. Chim. Acta, 35, 1850, 1952. 11 K. Lunde and L. Zechmeister, J. Am. Chem. Soc., 76, 2308,1954; L. Zechmeister and J. H. Pinckard, J. Am. Chem. Soc., 76, 4144, 1954. 12 W. Oroshnik and A. D.
D. A. +7 more
semanticscholar +1 more source
Tunable stacking fault energies by tailoring local chemical order in CrCoNi medium-entropy alloys [PDF]
Proceedings of the National Academy of Sciences of the United States of America, 2018 Significance Our work has revealed the nature of local chemical order and has established its significant relationship to the intrinsic and extrinsic stacking fault energy in CrCoNi medium-entropy solid-solution alloys, whose combination of strength ...
J. Ding, Qin Yu, M. Asta, R. Ritchie
semanticscholar +1 more source
Education sciences, 2021 The technological advancement and rapid development of artificial intelligence have led to a growing number of studies investigating pedagogical innovations incorporated with emerging technologies in this digital era.
Wang-Kin Chiu
semanticscholar +1 more source