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Digital discovery

Showing results (41-50 of 131) with videos related to

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Digital Discovery|June 30, 2022
Limitations of machine learning models when predicting compounds with completely new chemistries: possible improvements applied to the discovery of new non-fullerene acceptorsZhi-Wen Zhao, Marcos Del Cueto, Alessandro Troisi
Digital Discovery|June 30, 2022
Deep generative models for peptide designFangping Wan, Daphne Kontogiorgos-Heintz, Cesar de la Fuente-Nunez
Digital Discovery|June 30, 2022
SPA<sup>H</sup>M: the spectrum of approximated Hamiltonian matrices representationsAlberto Fabrizio, Ksenia R Briling, Clemence Corminboeuf
Digital Discovery|November 3, 2022
Neural-network-backed evolutionary search for SrTiO<sub>3</sub>(110) surface reconstructionsRalf Wanzenböck, Marco Arrigoni, Sebastian Bichelmaier, et al.
Digital Discovery|July 12, 2024
A versatile optimization framework for porous electrode designMaxime van der Heijden, Gabor Szendrei, Victor de Haas, et al.
Digital Discovery|June 14, 2024
Mining patents with large language models elucidates the chemical function landscapeClayton W Kosonocky, Claus O Wilke, Edward M Marcotte, et al.
Digital Discovery|May 17, 2024
Learning peptide properties with positive examples onlyMehrad Ansari, Andrew D White
Digital Discovery|May 17, 2024
MLstructureMining: a machine learning tool for structure identification from X-ray pair distribution functionsEmil T S Kjær, Andy S Anker, Andrea Kirsch, et al.
Digital Discovery|November 26, 2025
Active learning meets metadynamics: automated workflow for reactive machine learning interatomic potentialsValdas Vitartas, Hanwen Zhang, Veronika Juraskova, et al.
Digital Discovery|November 5, 2025
GoFlow: efficient transition state geometry prediction with flow matching and E(3)-equivariant neural networksLeonard Galustian, Konstantin Mark, Johannes Karwounopoulos, et al.
Pageof 14

Showing results (41-50 of 131) with videos related to

Sort By:
Pageof 14
Digital Discovery|June 30, 2022
Limitations of machine learning models when predicting compounds with completely new chemistries: possible improvements applied to the discovery of new non-fullerene acceptorsZhi-Wen Zhao, Marcos Del Cueto, Alessandro Troisi
Digital Discovery|June 30, 2022
Deep generative models for peptide designFangping Wan, Daphne Kontogiorgos-Heintz, Cesar de la Fuente-Nunez
Digital Discovery|June 30, 2022
SPA<sup>H</sup>M: the spectrum of approximated Hamiltonian matrices representationsAlberto Fabrizio, Ksenia R Briling, Clemence Corminboeuf
Digital Discovery|November 3, 2022
Neural-network-backed evolutionary search for SrTiO<sub>3</sub>(110) surface reconstructionsRalf Wanzenböck, Marco Arrigoni, Sebastian Bichelmaier, et al.
Digital Discovery|July 12, 2024
A versatile optimization framework for porous electrode designMaxime van der Heijden, Gabor Szendrei, Victor de Haas, et al.
Digital Discovery|June 14, 2024
Mining patents with large language models elucidates the chemical function landscapeClayton W Kosonocky, Claus O Wilke, Edward M Marcotte, et al.
Digital Discovery|May 17, 2024
Learning peptide properties with positive examples onlyMehrad Ansari, Andrew D White
Digital Discovery|May 17, 2024
MLstructureMining: a machine learning tool for structure identification from X-ray pair distribution functionsEmil T S Kjær, Andy S Anker, Andrea Kirsch, et al.
Digital Discovery|November 26, 2025
Active learning meets metadynamics: automated workflow for reactive machine learning interatomic potentialsValdas Vitartas, Hanwen Zhang, Veronika Juraskova, et al.
Digital Discovery|November 5, 2025
GoFlow: efficient transition state geometry prediction with flow matching and E(3)-equivariant neural networksLeonard Galustian, Konstantin Mark, Johannes Karwounopoulos, et al.
Pageof 14