14 papers · ranked by Valyu relevance
Authors not listed
Artificial intelligence (AI) is reshaping scientific research by accelerating discovery and enabling the analysis of complex data that traditional methods struggle to handle. This review examines over 310,000 journal articles and patents from the CAS Content Collection (2015–2025), with a focus on, biomedical research…
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The global drive towards net-zero has accelerated the adoption of carbon fibre reinforced polymers (CFRP) for lightweight structures in various sectors such as aerospace, automotive, energy and biomedical. Mechanical machining of CFRP is often necessary to meet dimensional or assembly-related requirements. However…
Yuanqing Lu, Timur Fazletdinov, Zhiwen Pan, Katrin Wondraczek + 1 more
The synthesis of nanoscale particles and particle aggregates from liquid or gaseous precursors is affected by a variety of trade-off relations, for example, in terms of product composition, yield, or energy efficiency. Machine-supported process evaluation and learning (ML) of these relations enables optimization…
Authors not listed
Kinetic modeling is essential for predicting changes in food quality during processing and storage. This study evaluates the application of physics-informed neural networks (PINN) for food kinetic modeling, integrating kinetic insights into neural network frameworks. Based on three case studies, namely seed drying…
Nihal Dadheech
In our research journey, we undertook a comprehensive exploration of protein-protein interaction (PPI) prediction, with a primary focus on unraveling the intricate web of interactions involving the SARS-CoV-2 virus. Our research endeavor encompassed a multi-faceted approach that seamlessly integrated data…
Fuzhan Rahmanian, Robert M. Lee, Dominik Linzner, Kathrin Michel + 4 more
Predicting and monitoring battery life early and across chemistries is a significant challenge due to the plethora of degradation paths, form factors, and electrochemical testing protocols. Existing models typically translate poorly across different electrode, electrolyte, and additive materials, mostly require a fixed…
Tianfan Jin, Brett M Savoie
Contemporary machine learning algorithms have largely succeeded in automating the development of mathematical models from data. Although this is a striking accomplishment, it leaves unaddressed the multitude of scenarios, especially across the chemical sciences and engineering, where deductive, rather than inductive…
Prashanth Athri, Vidhya Murali, Pradyumna Y Muralidhar, Cassandra Königs + 4 more
- 1. Department of Computer Science and Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru, India - 2. PES Center for Pattern Recognition, Department of Computer Science and Engineering, PES University, Bengaluru, India - 3. Bioinformatics and Medical Informatics, Bielefeld University…
Authors not listed
Accurate prediction of melting points for pure molecules remains a significant challenge in predictive chemistry, with implications across various scientific fields, including materials science, drug discovery, and separations chemistry. Traditional methods, such as group contribution (GC) techniques, have shown…
William Borrelli, Joshua Schrier
Forward and retrosynthetic organic reaction prediction are challenging applications of artificial intelligence (AI) research in chemistry. IBM’s freely available RXN for Chemistry (https://rxn.res.ibm.com) treats reaction prediction as a translation problem, by using transformer-based machine learning models trained on…
Authors not listed
Background: Pharmaceutical batch scheduling in multi-reactor configurations presents complex optimization challenges under operational uncertainty, yet limited research addresses how parallel processing capacity affects heuristic performance and predictive modeling. Objectives: This study investigated scheduling…
Authors not listed
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and Middle East respiratory syndrome coronavirus (MERS-CoV) are two important targets in current drug discovery, mainly due to the COVID-19 pandemic and the MERS-CoV outbreaks in recent years. An important target of both SARS-CoV-2 and MERS-CoV is the main…
Martin Seifrid, Stanley Lo, Dylan Choi, Gary Tom + 12 more
Martin Seifrid 1 , Stanley Lo 2 , Dylan G. Choi 3 , Gary Tom 2 , My Linh Le 3 , Kunyu Li 3 , Rahul Sankar 3 , Hoai-Thanh Vuong 3 , Hiba Wakidi 3 , Ahra Yi 3 , Ziyue Zhu 3 , Nora Schopp 3 , Aaron Peng 3 , Benjamin Luginbuhl 3 , Thuc-Quyen Nguyen 3 , Alán Aspuru-Guzik 2
Authors not listed
Bioprocess engineering has incorporated effective AI applications in recent years that consist of traditional approaches to training models on relevant data to then analyze and predict new and unseen data. The missing component has been the ability to process mixed data from an assortment of dissimilar information…