Amp: A modular approach to machine learning in atomistic simulations
| Main Author: | CPC, Mendeley |
|---|---|
| Other Authors: | Khorshidi, Alireza, Peterson, Andrew A. |
| Format: | Dataset |
| Terbitan: |
Mendeley
, 2016
|
| Subjects: | |
| Online Access: |
https:/data.mendeley.com/datasets/rhrbt5ddkk |
| ctrlnum |
0.17632-rhrbt5ddkk.1 |
|---|---|
| fullrecord |
<?xml version="1.0"?>
<dc><creator>CPC, Mendeley</creator><title>
Amp: A modular approach to machine learning in atomistic simulations </title><publisher>Mendeley</publisher><description>This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2018)
Abstract
Electronic structure calculations, such as those employing Kohn–Sham density functional theory or ab initio wavefunction theories, have allowed for atomistic-level understandings of a wide variety of phenomena and properties of matter at small scales. However, the computational cost of electronic structure methods drastically increases with length and time scales, which makes these methods difficult for long time-scale molecular dynamics simulations or large-sized systems. Machine-learning te...
Title of program: Amp
Catalogue Id: AFAK_v1_0
Nature of problem
Atomic interactions within many-body systems typically have complicated functional forms, difficult to represent in simple pre-decided closed-forms.
Versions of this program held in the CPC repository in Mendeley Data
AFAK_v1_0; Amp; 10.1016/j.cpc.2016.05.010
</description><subject>Atomic Physics</subject><subject>Physical Chemistry</subject><subject>Molecular Physics</subject><subject>Computational Physics</subject><contributor>Khorshidi, Alireza</contributor><contributor>Peterson, Andrew A.</contributor><type>Other:Dataset</type><identifier>10.17632/rhrbt5ddkk.1</identifier><rights>GNU Public License Version 3</rights><rights>http://www.gnu.org/licenses/gpl-3.0.en.html</rights><relation>https:/data.mendeley.com/datasets/rhrbt5ddkk</relation><date>2016-10-01T11:00:00Z</date><recordID>0.17632-rhrbt5ddkk.1</recordID></dc>
|
| format |
Other:Dataset Other |
| author |
CPC, Mendeley |
| author2 |
Khorshidi, Alireza Peterson, Andrew A. |
| title |
Amp: A modular approach to machine learning in atomistic simulations |
| publisher |
Mendeley |
| publishDate |
2016 |
| topic |
Atomic Physics Physical Chemistry Molecular Physics Computational Physics |
| url |
https:/data.mendeley.com/datasets/rhrbt5ddkk |
| contents |
This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2018)
Abstract
Electronic structure calculations, such as those employing Kohn–Sham density functional theory or ab initio wavefunction theories, have allowed for atomistic-level understandings of a wide variety of phenomena and properties of matter at small scales. However, the computational cost of electronic structure methods drastically increases with length and time scales, which makes these methods difficult for long time-scale molecular dynamics simulations or large-sized systems. Machine-learning te...
Title of program: Amp
Catalogue Id: AFAK_v1_0
Nature of problem
Atomic interactions within many-body systems typically have complicated functional forms, difficult to represent in simple pre-decided closed-forms.
Versions of this program held in the CPC repository in Mendeley Data
AFAK_v1_0; Amp; 10.1016/j.cpc.2016.05.010
|
| id |
IOS7969.0.17632-rhrbt5ddkk.1 |
| institution |
Universitas Islam Indragiri |
| affiliation |
onesearch.perpusnas.go.id |
| institution_id |
804 |
| institution_type |
library:university library |
| library |
Teknologi Pangan UNISI |
| library_id |
2816 |
| collection |
Artikel mulono |
| repository_id |
7969 |
| city |
INDRAGIRI HILIR |
| province |
RIAU |
| shared_to_ipusnas_str |
1 |
| repoId |
IOS7969 |
| first_indexed |
2020-04-08T08:28:09Z |
| last_indexed |
2020-04-08T08:28:09Z |
| recordtype |
dc |
| _version_ |
1686587743985991680 |
| score |
17.538404 |