Accelerating material design with the generative toolkit for scientific discovery

Autor: Matteo Manica, Jannis Born, Joris Cadow, Dimitrios Christofidellis, Ashish Dave, Dean Clarke, Yves Gaetan Nana Teukam, Giorgio Giannone, Samuel C. Hoffman, Matthew Buchan, Vijil Chenthamarakshan, Timothy Donovan, Hsiang Han Hsu, Federico Zipoli, Oliver Schilter, Akihiro Kishimoto, Lisa Hamada, Inkit Padhi, Karl Wehden, Lauren McHugh, Alexy Khrabrov, Payel Das, Seiji Takeda, John R. Smith

Publikováno v: npj Computational Materials, Vol 9, Iss 1, Pp 1-6 (2023)

Abstract With the growing availability of data within various scientific domains, generative models hold enormous potential to accelerate scientific discovery. They harness powerful representations learned from datasets to speed up the formulation of

Externí odkaz: https://doaj.org/article/d563c21247234f54b23d37c88d05e3d1

Explainable Cross-Topic Stance Detection for Search Results

Autor: Tim Draws, Karthikeyan Natesan Ramamurthy, Ioana Baldini, Amit Dhurandhar, Inkit Padhi, Benjamin Timmermans, Nava Tintarev

Publikováno v: CHIIR'23: Proceedings of the 2023 Conference on Human Information Interaction and Retrieval, 221-235
STARTPAGE=221;ENDPAGE=235;TITLE=CHIIR'23: Proceedings of the 2023 Conference on Human Information Interaction and Retrieval
CHIIR 2023-Proceedings of the 2023 Conference on Human Information Interaction and Retrieval

One way to help users navigate debated topics online is to apply stance detection in web search. Automatically identifying whether search results are against, neutral, or in favor could facilitate diversification efforts and support interventions tha

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e236e82bd0c65f035b313d9d2fa968aa
https://doi.org/10.1145/3576840.3578296

Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations

Autor: Aleksandra Mojsilovic, Cicero Nogueira dos Santos, James L. Hedrick, Jason Crain, Sebastian Gehrmann, Kahini Wadhawan, Flaviu Cipcigan, Inkit Padhi, Hendrik Strobelt, Yi Yan Yang, Pin-Yu Chen, Payel Das, Jeremy P. K. Tan, Vijil Chenthamarakshan, Tom Sercu

Publikováno v: Nature Biomedical Engineering. 5:613-623

The de novo design of antimicrobial therapeutics involves the exploration of a vast chemical repertoire to find compounds with broad-spectrum potency and low toxicity. Here, we report an efficient computational method for the generation of antimicrob

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6c2ef9d795ea0ca59343137c94178a55
https://doi.org/10.1038/s41551-021-00689-x

Accelerating Material Design with the Generative Toolkit for Scientific Discovery

Autor: Matteo Manica, Jannis Born, Joris Cadow, Dimitrios Christofidellis, Ashish Dave, Dean Clarke, Yves Gaetan Nana Teukam, Giorgio Giannone, Samuel C. Hoffman, Matthew Buchan, Vijil Chenthamarakshan, Timothy Donovan, Hsiang Han Hsu, Federico Zipoli, Oliver Schilter, Akihiro Kishimoto, Lisa Hamada, Inkit Padhi, Karl Wehden, Lauren McHugh, Alexy Khrabrov, Payel Das, Seiji Takeda, John R. Smith

With the growing availability of data within various scientific domains, generative models hold enormous potential to accelerate scientific discovery. They harness powerful representations learned from datasets to speed up the formulation of novel hy

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::145b068669f6e0ef69f5e03844536585

DualTKB: A Dual Learning Bridge between Text and Knowledge Base

Autor: Pierre L. Dognin, Payel Das, Igor Melnyk, Inkit Padhi, Cicero Nogueira dos Santos

Publikováno v: EMNLP (1)

In this work, we present a dual learning approach for unsupervised text to path and path to text transfers in Commonsense Knowledge Bases (KBs). We investigate the impact of weak supervision by creating a weakly supervised dataset and show that even

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6a702ccf88e30ab4c41763da36f36c16