Aqueous Solution Chemistry In Silico and the Role of Data Driven Approaches
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| author | Banerjee, Debarshi Azizi, Khatereh Egan, Colin K. Donkor, Edward Danquah Malosso, Cesare Di Pino, Solana Miron, Gonzalo Diaz Stella, Martina Sormani, Giulia Hozana, Germaine Neza Monti, Marta Morzan, Uriel N. Rodriguez, Alex Cassone, Giuseppe Jelic, Asja Scherlis, Damian Hassanali, Ali |
| author_facet | Banerjee, Debarshi Azizi, Khatereh Egan, Colin K. Donkor, Edward Danquah Malosso, Cesare Di Pino, Solana Miron, Gonzalo Diaz Stella, Martina Sormani, Giulia Hozana, Germaine Neza Monti, Marta Morzan, Uriel N. Rodriguez, Alex Cassone, Giuseppe Jelic, Asja Scherlis, Damian Hassanali, Ali |
| contents | 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 in physical chemistry and how this will influence computer simulations of aqueous systems in the future. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_06236 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Aqueous Solution Chemistry In Silico and the Role of Data Driven Approaches Banerjee, Debarshi Azizi, Khatereh Egan, Colin K. Donkor, Edward Danquah Malosso, Cesare Di Pino, Solana Miron, Gonzalo Diaz Stella, Martina Sormani, Giulia Hozana, Germaine Neza Monti, Marta Morzan, Uriel N. Rodriguez, Alex Cassone, Giuseppe Jelic, Asja Scherlis, Damian Hassanali, Ali Chemical Physics 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 in physical chemistry and how this will influence computer simulations of aqueous systems in the future. |
| title | Aqueous Solution Chemistry In Silico and the Role of Data Driven Approaches |
| topic | Chemical Physics |
| url | https://arxiv.org/abs/2403.06236 |