Zobrazeno 1 - 10
of 178
pro vyhledávání: '"Thompson, Aidan P"'
Autor:
Cangi, Attila, Fiedler, Lenz, Brzoza, Bartosz, Shah, Karan, Callow, Timothy J., Kotik, Daniel, Schmerler, Steve, Barry, Matthew C., Goff, James M., Rohskopf, Andrew, Vogel, Dayton J., Modine, Normand, Thompson, Aidan P., Rajamanickam, Sivasankaran
We present the Materials Learning Algorithms (MALA) package, a scalable machine learning framework designed to accelerate density functional theory (DFT) calculations suitable for large-scale atomistic simulations. Using local descriptors of the atom
Externí odkaz:
http://arxiv.org/abs/2411.19617
Breaking the mold: overcoming the time constraints of molecular dynamics on general-purpose hardware
Autor:
Perez, Danny, Thompson, Aidan, Moore, Stan, Oppelstrup, Tomas, Sharapov, Ilya, Santos, Kylee, Sharifian, Amirali, Kalchev, Delyan Z., Schreiber, Robert, Pakin, Scott, Leon, Edgar A., Laros III, James H., James, Michael, Rajamanickam, Sivasankaran
The evolution of molecular dynamics (MD) simulations has been intimately linked to that of computing hardware. For decades following the creation of MD, simulations have improved with computing power along the three principal dimensions of accuracy,
Externí odkaz:
http://arxiv.org/abs/2411.10532
Autor:
Santos, Kylee, Moore, Stan, Oppelstrup, Tomas, Sharifian, Amirali, Sharapov, Ilya, Thompson, Aidan, Kalchev, Delyan Z, Perez, Danny, Schreiber, Robert, Pakin, Scott, Leon, Edgar A, Laros III, James H, James, Michael, Rajamanickam, Sivasankaran
Publikováno v:
SC '24: Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis, 2024, Article No. 8
Molecular dynamics (MD) simulations have transformed our understanding of the nanoscale, driving breakthroughs in materials science, computational chemistry, and several other fields, including biophysics and drug design. Even on exascale supercomput
Externí odkaz:
http://arxiv.org/abs/2405.07898
Autor:
Nguyen-Cong, Kien, Willman, Jonathan T., Gonzalez, Joseph M., Williams, Ashley S., Belonoshko, Anatoly B., Moore, Stan G., Thompson, Aidan P., Wood, Mitchell A., Eggert, Jon H., Millot, Marius, Zepeda-Ruiz, Luis A., Oleynik, Ivan I.
Diamond possesses exceptional physical properties due to its remarkably strong carbon-carbon bonding, leading to significant resilience to structural transformations at very high pressures and temperatures. Despite several experimental attempts, synt
Externí odkaz:
http://arxiv.org/abs/2401.08884
Autor:
Rohskopf, Andrew, Goff, James, Sema, Dionysios, Gordiz, Kiarash, Nguyen, Ngoc Cuong, Henry, Asegun, Thompson, Aidan P., Wood, Mitchell A.
Machine learning (ML) enables the development of interatomic potentials that promise the accuracy of first principles methods while retaining the low cost and parallel efficiency of empirical potentials. While ML potentials traditionally use atom-cen
Externí odkaz:
http://arxiv.org/abs/2306.02255
The exceptional properties observed in complex concentrated alloys (CCAs) arise from the interplay between crystalline order and chemical disorder at the atomic scale, complicating a unique determination of properties. In contrast to conventional all
Externí odkaz:
http://arxiv.org/abs/2303.04311
Autor:
Fiedler, Lenz, Modine, Normand A., Schmerler, Steve, Vogel, Dayton J., Popoola, Gabriel A., Thompson, Aidan P., Rajamanickam, Sivasankaran, Cangi, Attila
The properties of electrons in matter are of fundamental importance. They give rise to virtually all molecular and material properties and determine the physics at play in objects ranging from semiconductor devices to the interior of giant gas planet
Externí odkaz:
http://arxiv.org/abs/2210.11343
Atomic cluster expansion (ACE) methods provide a systematic way to describe particle local environments of arbitrary body order. For practical applications it is often required that the basis of cluster functions be symmetrized with respect to rotati
Externí odkaz:
http://arxiv.org/abs/2208.01756
Autor:
Willman, Jonathan T., Nguyen-Cong, Kien, Williams, Ashley S., Belonoshko, Anatoly B., Moore, Stan G., Thompson, Aidan P., Wood, Mitchell A., Oleynik, Ivan I.
A Spectral Neighbor Analysis (SNAP) machine learning interatomic potential (MLIP) has been developed for simulations of carbon at extreme pressures (up to 5 TPa) and temperatures (up to 20,000 K). This was achieved using a large database of experimen
Externí odkaz:
http://arxiv.org/abs/2205.01209
Autor:
Zapiain, David Montes de Oca, Wood, Mitchell A., Lubbers, Nicholas, Pereyra, Carlos Z., Thompson, Aidan P., Perez, Danny
Advances in machine learning (ML) techniques have enabled the development of interatomic potentials that promise both the accuracy of first principles methods and the low-cost, linear scaling, and parallel efficiency of empirical potentials. Despite
Externí odkaz:
http://arxiv.org/abs/2201.09829