Zobrazeno 1 - 10
of 20
pro vyhledávání: '"Karl Leswing"'
Autor:
Shaswat Mohanty, James Stevenson, Andrea R. Browning, Leif Jacobson, Karl Leswing, Mathew D. Halls, Mohammad Atif Faiz Afzal
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-15 (2023)
Abstract Understanding and predicting the properties of polymers is vital to developing tailored polymer molecules for desired applications. Classical force fields may fail to capture key properties, for example, the transport properties of certain p
Externí odkaz:
https://doaj.org/article/2dbe2c1c2a9b4039905d9d1df98e14c1
Autor:
H. Shaun Kwak, Yuling An, David J. Giesen, Thomas F. Hughes, Christopher T. Brown, Karl Leswing, Hadi Abroshan, Mathew D. Halls
Publikováno v:
Frontiers in Chemistry, Vol 9 (2022)
In recent years, generative machine learning approaches have attracted significant attention as an enabling approach for designing novel molecular materials with minimal design bias and thereby realizing more directed design for a specific materials
Externí odkaz:
https://doaj.org/article/9d052ffe22fc48adb56256115e599767
Autor:
Ryne C. Johnston, Kun Yao, Zachary Kaplan, Monica Chelliah, Karl Leswing, Sean Seekins, Shawn Watts, David Calkins, Jackson Chief Elk, Steven V. Jerome, Matthew P. Repasky, John C. Shelley
Publikováno v:
Journal of Chemical Theory and Computation. 19:2380-2388
Autor:
Joshua Staker, Kyle Marshall, Karl Leswing, Tim Robertson, Mathew D. Halls, Alexander Goldberg, Tsuguo Morisato, Hiroyuki Maeshima, Tatsuhito Ando, Hideyuki Arai, Masaru Sasago, Eiji Fujii, Nobuyuki N. Matsuzawa
Publikováno v:
The Journal of Physical Chemistry A. 126:5837-5852
Autor:
Steven Dajnowicz, Garvit Agarwal, James M. Stevenson, Leif D. Jacobson, Farhad Ramezanghorbani, Karl Leswing, Richard A. Friesner, Mathew D. Halls, Robert Abel
Publikováno v:
The Journal of Physical Chemistry B. 126:6271-6280
Liquid electrolytes are one of the most important components of Li-ion batteries, which are a critical technology of the modern world. However, we still lack the computational tools required to accurately calculate key properties of these materials (
Significant improvements have been made in the past decade to methods that rapidly and accurately predict binding affinity through free energy perturbation (FEP) calculations. This has been driven by recent advances in small molecule force fields and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0e5494f78ffc4069da81000b331e6583
https://doi.org/10.26434/chemrxiv-2023-vv5cq
https://doi.org/10.26434/chemrxiv-2023-vv5cq
Autor:
Steven V. Jerome, Robert Abel, Matthew P. Repasky, Kun Yao, Brian K. Shoichet, Ying Yang, Karl Leswing
Publikováno v:
Journal of Chemical Theory and Computation. 17:7106-7119
With the advent of make-on-demand commercial libraries, the number of purchasable compounds available for virtual screening and assay has grown explosively in recent years, with several libraries eclipsing one billion compounds. Today's screening lib
Autor:
Hiroyuki Maeshima, Masaru Sasago, Kyle Marshall, Tim Robertson, Joshua Staker, Gabriel Marques, Mathew D. Halls, David J. Giesen, Karl Leswing, Alexander Goldberg, Hideyuki Arai, Eiji Fujii, Nobuyuki N. Matsuzawa, Tsuguo Morisato
Publikováno v:
The Journal of Physical Chemistry A. 125:7331-7343
Materials exhibiting higher mobilities than conventional organic semiconducting materials such as fullerenes and fused thiophenes are in high demand for applications in printed electronics. To discover new molecules in the heteroacene family that mig
Autor:
Karl Leswing, Joshua Staker, Pieter H. Bos, Kyle Marshall, Sathesh Bhat, Phani Ghanakota, Kyle D. Konze, Gabriel Marques, Robert Abel
Publikováno v:
Journal of Chemical Information and Modeling. 60:4311-4325
The hit identification process usually involves the profiling of millions to more recently billions of compounds either via traditional experimental high-throughput screens (HTS) or computational virtual high-throughput screens (vHTS). We have previo
Autor:
Delaram Ghoreishi, Leif D. Jacobson, Robert Abel, Farhad Ramezanghorbani, Karl Leswing, Edward Harder, James Stevenson
Transferable high dimensional neural network potentials (HDNNP) have shown great promise as an avenue to increase the accuracy and domain of applicability of existing atomistic force fields for organic systems relevant to life science. We have previo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2fc31fac109e4f23981ec4030b379068
https://doi.org/10.26434/chemrxiv-2021-tmsdg-v3
https://doi.org/10.26434/chemrxiv-2021-tmsdg-v3