Deep Potential: A General Representation of a Many-Body Potential Energy Surface

被引：220

作者：

Han, Jiequn ^{[1
]}

Zhang, Linfeng ^{[1
]}

Car, Roberto ^{[1
,2
,3
]}

Weinan, E. ^{[1
,4
,5
,6
,7
]}

机构：

[1] Princeton Univ, Program Appl & Computat Math, Princeton, NJ 08544 USA

[2] Princeton Univ, Dept Phys, Dept Chem, Princeton, NJ 08544 USA

[3] Princeton Univ, Princeton Inst Sci & Technol Mat, Princeton, NJ 08544 USA

[4] Princeton Univ, Dept Math, Princeton, NJ 08544 USA

[5] Peking Univ, Ctr Data Sci, Beijing 100871, Peoples R China

[6] Peking Univ, Beijing Int Ctr Math Res, Beijing 100871, Peoples R China

[7] Beijing Inst Big Data Res, Beijing 100871, Peoples R China

来源：

COMMUNICATIONS IN COMPUTATIONAL PHYSICS | 2018年 / 23卷 / 03期

关键词：

Potential energy surface; deep learning; molecular simulation; EMBEDDED-ATOM-METHOD; MOLECULAR-DYNAMICS; FORCE-FIELD;

D O I：

10.4208/cicp.OA-2017-0213

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

We present a simple, yet general, deep neural network representation of the potential energy surface for atomic and molecular systems. It is "first-principle" based, in the sense that no ad hoc approximations or empirical fitting functions are required. When tested on a wide variety of examples, it reproduces the original model within chemical accuracy. This brings us one step closer to carrying outmolecular simulations with quantum mechanics accuracy at empirical potential computational cost.

引用

页码：629 / 639

页数：11

共 42 条

[1] Toward reliable density functional methods without adjustable parameters: The PBE0 model [J].

Adamo, C ;

Barone, V .

JOURNAL OF CHEMICAL PHYSICS, 1999, 110 (13) :6158-6170

[2]

[Anonymous], 1995, FAST PARALLEL ALGORI

[3] High-dimensional neural network potentials for metal surfaces: A prototype study for copper [J].

Artrith, Nongnuch ;

Behler, Joerg .

PHYSICAL REVIEW B, 2012, 85 (04)

[4] High-dimensional neural-network potentials for multicomponent systems: Applications to zinc oxide [J].

Artrith, Nongnuch ;

Morawietz, Tobias ;

Behler, Joerg .

PHYSICAL REVIEW B, 2011, 83 (15)

[5] On representing chemical environments [J].

Bartok, Albert P. ;

Kondor, Risi ;

Csanyi, Gabor .

PHYSICAL REVIEW B, 2013, 87 (18)

[6] Generalized neural-network representation of high-dimensional potential-energy surfaces [J].

Behler, Joerg ;

Parrinello, Michele .

PHYSICAL REVIEW LETTERS, 2007, 98 (14)

[7] Constructing high-dimensional neural network potentials: A tutorial review [J].

Behler, Joerg .

INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY, 2015, 115 (16) :1032-1050

[8] UNIFIED APPROACH FOR MOLECULAR-DYNAMICS AND DENSITY-FUNCTIONAL THEORY [J].

CAR, R ;

PARRINELLO, M .

PHYSICAL REVIEW LETTERS, 1985, 55 (22) :2471-2474

[9] QUANTUM MONTE-CARLO [J].

CEPERLEY, D ;

ALDER, B .

SCIENCE, 1986, 231 (4738) :555-560

[10] i-PI: A Python']Python interface for ab initio path integral molecular dynamics simulations [J].

Ceriotti, Michele ;

More, Joshua ;

Manolopoulos, David E. .

COMPUTER PHYSICS COMMUNICATIONS, 2014, 185 (03) :1019-1026

← 1 2 3 4 5 →