85 citations · 131 across the 12 of their papers we have counts for
12 papers
Quantum-centric Supercomputing for Materials Science: A Perspective on Challenges and Future Directions
Yuri Alexeev, Maximilian Amsler, Paul Baity +124
Computational models are an essential tool for the design, characterization, and discovery of novel materials. Hard computational tasks in materials science stretch the limits of e…
Formulation Graphs for Mapping Structure-Composition of Battery Electrolytes to Device Performance
Vidushi Sharma, Maxwell Giammona, Dmitry Zubarev +5
Advanced computational methods are being actively sought for addressing the challenges associated with discovery and development of new combinatorial material such as formulations.…
Beyond Chemical Language: A Multimodal Approach to Enhance Molecular Property Prediction
Eduardo Soares, Emilio Vital Brazil, Karen Fiorela Aquino Gutierrez +4
We present a novel multimodal language model approach for predicting molecular properties by combining chemical language representation with physicochemical features. Our approach,…
Human-AI Co-Creation Approach to Find Forever Chemicals Replacements
Juliana Jansen Ferreira, Vinícius Segura, Joana G. R. Souza +4
Generative models are a powerful tool in AI for material discovery. We are designing a software framework that supports a human-AI co-creation process to accelerate finding replace…
Domain-agnostic and Multi-level Evaluation of Generative Models
Girmaw Abebe Tadesse, Jannis Born, Celia Cintas +4
While the capabilities of generative models heavily improved in different domains (images, text, graphs, molecules, etc.), their evaluation metrics largely remain based on simplifi…
Toward Human-AI Co-creation to Accelerate Material Discovery
Dmitry Zubarev, Carlos Raoni Mendes, Emilio Vital Brazil +5
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 syste…
Topology-Driven Generative Completion of Lacunae in Molecular Data
Dmitry Yu. Zubarev, Petar Ristoski
We introduce an approach to the targeted completion of lacunae in molecular data sets which is driven by topological data analysis, such as Mapper algorithm. Lacunae are filled in…
Sample-Efficient Generation of Novel Photo-acid Generator Molecules using a Deep Generative Model
Samuel C. Hoffman, Vijil Chenthamarakshan, Dmitry Yu. Zubarev +2
Photo-acid generators (PAGs) are compounds that release acids ( ions) when exposed to light. These compounds are critical components of the photolithography processes that are…
Molecule Generation Experience: An Open Platform of Material Design for Public Users
Seiji Takeda, Toshiyuki Hama, Hsiang-Han Hsu +10
Artificial Intelligence (AI)-driven material design has been attracting great attentions as a groundbreaking technology across a wide spectrum of industries. Molecular design is pa…
Molecular Inverse-Design Platform for Material Industries
Seiji Takeda, Toshiyuki Hama, Hsiang-Han Hsu +17
The discovery of new materials has been the essential force which brings a discontinuous improvement to industrial products' performance. However, the extra-vast combinatorial desi…
AI-driven Inverse Design System for Organic Molecules
Seiji Takeda, Toshiyuki Hama, Hsiang-Han Hsu +7
Designing novel materials that possess desired properties is a central need across many manufacturing industries. Driven by that industrial need, a variety of algorithms and tools…
Data Infrastructure and Approaches for Ontology-Based Drug Repurposing
Stephen Boyer, Thomas Griffin, Sarath Swaminathan +2
We report development of a data infrastructure for drug repurposing that takes advantage of two currently available chemical ontologies. The data infrastructure includes a database…