Hosted by Gabriel Hesch and Autumn Phaneuf, who have advanced degrees in electrical engineering and industrial engineering/operations research respectively, come together to discuss mathematics as a pure field all in its own as well as how it describes the language of science, engineering, and even creativity.
Breaking Math brings you the absolute best in interdisciplinary science discussions - bringing together experts in varying fields including artificial intelligence, neuroscience, evolutionary biology, physics, chemistry and materials-science, and more - to discuss where humanity is headed.
website: breakingmath.io
linktree: linktree.com/breakingmathmedia
email: breakingmathpodcast@gmail.com
All content for Breaking Math Podcast is the property of Gabriel Hesch and Autumn Phaneuf and is served directly from their servers
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Hosted by Gabriel Hesch and Autumn Phaneuf, who have advanced degrees in electrical engineering and industrial engineering/operations research respectively, come together to discuss mathematics as a pure field all in its own as well as how it describes the language of science, engineering, and even creativity.
Breaking Math brings you the absolute best in interdisciplinary science discussions - bringing together experts in varying fields including artificial intelligence, neuroscience, evolutionary biology, physics, chemistry and materials-science, and more - to discuss where humanity is headed.
website: breakingmath.io
linktree: linktree.com/breakingmathmedia
email: breakingmathpodcast@gmail.com
Molecular dynamics simulation with GFlowNets: machine learning the importance of energy estimators in computational chemistry and drug discovery
Breaking Math Podcast
28 minutes
6 months ago
Molecular dynamics simulation with GFlowNets: machine learning the importance of energy estimators in computational chemistry and drug discovery
In this episode of Breaking Math, hosts Autumn and Gabriel take a deep dive into the paper "Towards Equilibrium Molecular Conformation Generation with GFlowNets" by Volokova et al., published in the Digital Discovery Journal by the Royal Society of Chemistry. They explore the cutting-edge intersection of molecular conformations and machine learning, comparing traditional methods like molecular dynamics and cheminformatics with the innovative approach of Generative Flow Networks (GFlowNets) for molecular conformation generation.
The episode covers empirical results that showcase the effectiveness of GFlowNets in computational chemistry, their scalability, and the role of energy estimators in advancing fields like drug discovery. Tune in to learn how machine learning is transforming the way we understand molecular structures and driving breakthroughs in chemistry and pharmaceuticals.
Keywords: molecular conformations, machine learning, GFlowNets, computational chemistry, drug discovery, molecular dynamics, cheminformatics, energy estimators, empirical results, scalability, math, mathematics, physics, AI
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You can find the paper "Towards equilibrium molecular conformation generation with GFlowNets" by Volokova et al [https://pubs.rsc.org/en/content/articlepdf/2024/dd/d4dd00023d]in Digital Discovery Journal [https://x.com/digital_rsc] by the Royal Society of Chemistry [https://x.com/RoySocChem].
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email: breakingmathpodcast@gmail.com
Breaking Math Podcast
Hosted by Gabriel Hesch and Autumn Phaneuf, who have advanced degrees in electrical engineering and industrial engineering/operations research respectively, come together to discuss mathematics as a pure field all in its own as well as how it describes the language of science, engineering, and even creativity.
Breaking Math brings you the absolute best in interdisciplinary science discussions - bringing together experts in varying fields including artificial intelligence, neuroscience, evolutionary biology, physics, chemistry and materials-science, and more - to discuss where humanity is headed.
website: breakingmath.io
linktree: linktree.com/breakingmathmedia
email: breakingmathpodcast@gmail.com
