Výsledky vyhledávání - "Dmitry Zubarev"

Integrating Chemical Language and Physicochemical Features for Enhanced Molecular Property Prediction with Multimodal Language Models

Autor: Soares, Eduardo Almeida, Brazil, Emilio Vital, KAREN F. A. GUTIERREZ, RENATO CERQUEIRA, DANIEL P. SANDERS, KRISTIN SCHMIDT, DMITRY ZUBAREV

Here we present a novel multimodal language model (MultiModal-MoLFormer) approach for predicting molecular properties, which combines chemical language representation embeddings derived from the recently introduced MoLFormer chemical language model a

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::03863109ad4a614c4d59ba8773e9a0db

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An extensible platform for enabling artificial intelligence guided design of catalysts and materials

Autor: Nathaniel Park, Matteo Manica, Jannis Born, James Hedrick, Tim Erdmann, Dmitry Zubarev, Nil Mill, Pedro Arrechea

Advances in artificial intelligence (AI), machine learning (ML), and automated experimentation are poised to vastly accelerate and reshape polymer science research. The difficulties of experimental data and polymer structure representation form a sig

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::dd870a67ae4881df40ee68cf781af7e2
https://doi.org/10.26434/chemrxiv-2022-811rl-v2

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An extensible software platform for accelerating polymer discovery through informatics and artificial intelligence development

Autor: Nathaniel Park, Matteo Manica, Jannis Born, Dmitry Zubarev, Nil Mill, James Hedrick, Pedro Arrechea, Tim Erdmann

The inherent architectural complexity and stochasticity of polymeric materials combined with their myriad application domains present significant challenges for accurate data modeling and representation—hindering development of new artificial intel

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::579ac153d51d3994c20ac0dcb25cc0a9
https://doi.org/10.26434/chemrxiv-2022-811rl

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Transfer Learning Enabled Deep Learning Model for the Prediction of Battery Performance from Electrolyte Formulations

Autor: Vidushi Sharma, Maxwell Giammona, Dmitry Zubarev, Tim Erdmann, Andy Tek, Khanh Nugyuen, Young-Hye Na

Publikováno v: ECS Meeting Abstracts. :166-166

Next generation batteries utilizing lithium (Li) metal anode could represent a 10x improvement in anode specific capacity compared to currently used graphite anodes however they still face major challenges associated with their evenness of plating an

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::06a43b065d28c5fc5be02ebd3894897e
https://doi.org/10.1149/ma2022-022166mtgabs

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