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Quantum Computing at the Frontiers of Biological Sciences
Prashant S. Emani, Jonathan Warrell, Alan Anticevic +15
The search for meaningful structure in biological data has relied on cutting-edge advances in computational technology and data science methods. However, challenges arise as we pus…
Machine Learning for Scent: Learning Generalizable Perceptual Representations of Small Molecules
Benjamin Sanchez-Lengeling, Jennifer N. Wei, Brian K. Lee +3
Predicting the relationship between a molecule's structure and its odor remains a difficult, decades-old task. This problem, termed quantitative structure-odor relationship (QSOR)…
From absorption spectra to charge transfer in PEDOT nanoaggregates with machine learning
Loïc M. Roch, Semion K. Saikin, Florian Häse +4
Fast and inexpensive characterization of materials properties is a key element to discover novel functional materials. In this work, we suggest an approach employing three classes…
Beyond Ternary OPV: High-Throughput Experimentation and Self-Driving Laboratories Optimize Multi-Component Systems
Stefan Langner, Florian Häse, José Darío Perea +6
Fundamental advances to increase the efficiency as well as stability of organic photovoltaics (OPVs) are achieved by designing ternary blends which represents a clear trend towards…
Resource-efficient digital quantum simulation of -level systems for photonic, vibrational, and spin- Hamiltonians
Nicolas P. D. Sawaya, Tim Menke, Thi Ha Kyaw +3
Simulation of quantum systems is expected to be one of the most important applications of quantum computing, with much of the theoretical work so far having focused on fermionic an…
Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space
AkshatKumar Nigam, Pascal Friederich, Mario Krenn +1
Challenges in natural sciences can often be phrased as optimization problems. Machine learning techniques have recently been applied to solve such problems. One example in chemistr…