Aqueous solution chemistry in silico and the role of data-driven approaches

被引：2

作者：

Banerjee, Debarshi ^{[1
,2
]}

Azizi, Khatereh ^{[1
,3
]}

Egan, Colin K. ^{[1
]}

Donkor, Edward Danquah ^{[1
,2
]}

Malosso, Cesare ^{[2
]}

Di Pino, Solana ^{[4
]}

Miron, Gonzalo Diaz ^{[1
]}

Stella, Martina ^{[1
]}

Sormani, Giulia ^{[1
]}

Hozana, Germaine Neza ^{[1
,5
]}

Monti, Marta ^{[1
]}

Morzan, Uriel N. ^{[6
]}

Rodriguez, Alex ^{[1
,7
]}

Cassone, Giuseppe ^{[8
]}

Jelic, Asja ^{[1
]}

Scherlis, Damian ^{[4
]}

Hassanali, Ali ^{[1
]}

机构：

[1] Abdus Salaam Int Ctr Theoret Phys, Str Costiera 11, I-34151 Trieste, Italy

[2] Scuola Int Super Studi Avanzati SISSA, Via Bonomea 265, I-34136 Trieste, Italy

[3] Inst Res Fundamental Sci IPM, Sch Nano Sci, Tehran 193955531, Iran

[4] Univ Buenos Aires, Fac Ciencias Exactas & Nat, Dept Quim Inorgan Analit & Quim Fis, INQUIMAE, Buenos Aires 1428, DF, Argentina

[5] Univ Trieste, Dipartimento Fis, Via Alfonso Valerio 2, I-34127 Trieste, Italy

[6] Univ Buenos Aires, Fac Ciencias Exactas & Nat Pabellon, Inst Fis Buenos Aires, Ciudad Univ 1, RA-1428 Buenos Aires, Argentina

[7] Univ Trieste, Dipartimento Matemat & Geosci, Via Alfonso Valerio 12-1, I-34127 Trieste, Italy

[8] CNR, Natl Res Council, Inst Chem & Phys Proc, IPCF, Via S dAlcontres 37, I-98158 Pisa, Italy

来源：

CHEMICAL PHYSICS REVIEWS | 2024年 / 5卷 / 02期

关键词：

INITIO MOLECULAR-DYNAMICS; DENSITY-FUNCTIONAL THEORY; ENERGY DECOMPOSITION ANALYSIS; REACTIVE FORCE-FIELD; LIQUID-LIQUID TRANSITION; HYDRATED EXCESS PROTON; COARSE-GRAINED MODELS; AB-INITIO; POTENTIAL-ENERGY; 1ST PRINCIPLES;

D O I：

10.1063/5.0207567

中图分类号：

O64 [物理化学（理论化学）、化学物理学];

学科分类号：

070304 ; 081704 ;

摘要：

The use of computer simulations to study the properties of aqueous systems is, today more than ever, an active area of research. In this context, during the last decade there has been a tremendous growth in the use of data-driven approaches to develop more accurate potentials for water as well as to characterize its complexity in chemical and biological contexts. We highlight the progress, giving a historical context, on the path to the development of many-body and reactive potentials to model aqueous chemistry, including the role of machine learning strategies. We focus specifically on conceptual and methodological challenges along the way in performing simulations that seek to tackle problems in modeling the chemistry of aqueous solutions. In conclusion, we summarize our perspectives on the use and integration of advanced data-science techniques to provide chemical insights into physical chemistry and how this will influence computer simulations of aqueous systems in the future.

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页数：22

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