no code implementations • 24 Apr 2020 • Seiji Takeda, Toshiyuki Hama, Hsiang-Han Hsu, Victoria A. Piunova, Dmitry Zubarev, Daniel P. Sanders, Jed W. Pitera, Makoto Kogoh, Takumi Hongo, Yenwei Cheng, Wolf Bocanett, Hideaki Nakashika, Akihiro Fujita, Yuta Tsuchiya, Katsuhiko Hino, Kentaro Yano, Shuichi Hirose, Hiroki Toda, Yasumitsu Orii, Daiju Nakano
The discovery of new materials has been the essential force which brings a discontinuous improvement to industrial products' performance.
no code implementations • 5 Nov 2022 • Dmitry Zubarev, Carlos Raoni Mendes, Emilio Vital Brazil, Renato Cerqueira, Kristin Schmidt, Vinicius Segura, Juliana Jansen Ferreira, Dan Sanders
There is an increasing need in our society to achieve faster advances in Science to tackle urgent problems, such as climate changes, environmental hazards, sustainable energy systems, pandemics, among others.
no code implementations • 20 Jan 2023 • Girmaw Abebe Tadesse, Jannis Born, Celia Cintas, William Ogallo, Dmitry Zubarev, Matteo Manica, Komminist Weldemariam
To this end, we propose a framework for Multi-level Performance Evaluation of Generative mOdels (MPEGO), which could be employed across different domains.
no code implementations • 11 Apr 2023 • Juliana Jansen Ferreira, Vinícius Segura, Joana G. R. Souza, Gabriel D. J. Barbosa, João Gallas, Renato Cerqueira, Dmitry Zubarev
Generative models are a powerful tool in AI for material discovery.
no code implementations • 22 Jun 2023 • Eduardo Soares, Emilio Vital Brazil, Karen Fiorela Aquino Gutierrez, Renato Cerqueira, Dan Sanders, Kristin Schmidt, Dmitry Zubarev
Our approach opens up promising avenues for future research in molecular property prediction by harnessing the synergistic potential of both chemical language and physicochemical features, leading to enhanced performance and advancements in the field.
no code implementations • 7 Jul 2023 • Vidushi Sharma, Maxwell Giammona, Dmitry Zubarev, Andy Tek, Khanh Nugyuen, Linda Sundberg, Daniele Congiu, Young-Hye La
A widely adopted approach involves domain informed high-throughput screening of individual components that can be combined into a formulation.