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通往光明未来之路:为2050年的计算物理化学和生物物理学奠定基础。

Pathways to a Shiny Future: Building the Foundation for Computational Physical Chemistry and Biophysics in 2050.

作者信息

Biriukov Denys, Vácha Robert

机构信息

CEITEC - Central European Institute of Technology, Masaryk University, Kamenice 753/5, 625 00 Brno, Czech Republic.

National Centre for Biomolecular Research, Faculty of Science, Masaryk University, Kamenice 753/5, 625 00 Brno, Czech Republic.

出版信息

ACS Phys Chem Au. 2024 Apr 4;4(4):302-313. doi: 10.1021/acsphyschemau.4c00003. eCollection 2024 Jul 24.

DOI:10.1021/acsphyschemau.4c00003
PMID:39069976
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11274290/
Abstract

In the last quarter-century, the field of molecular dynamics (MD) has undergone a remarkable transformation, propelled by substantial enhancements in software, hardware, and underlying methodologies. In this Perspective, we contemplate the future trajectory of MD simulations and their possible look at the year 2050. We spotlight the pivotal role of artificial intelligence (AI) in shaping the future of MD and the broader field of computational physical chemistry. We outline critical strategies and initiatives that are essential for the seamless integration of such technologies. Our discussion delves into topics like multiscale modeling, adept management of ever-increasing data deluge, the establishment of centralized simulation databases, and the autonomous refinement, cross-validation, and self-expansion of these repositories. The successful implementation of these advancements requires scientific transparency, a cautiously optimistic approach to interpreting AI-driven simulations and their analysis, and a mindset that prioritizes knowledge-motivated research alongside AI-enhanced big data exploration. While history reminds us that the trajectory of technological progress can be unpredictable, this Perspective offers guidance on preparedness and proactive measures, aiming to steer future advancements in the most beneficial and successful direction.

摘要

在过去的二十五年里,分子动力学(MD)领域经历了显著的变革,这得益于软件、硬件及基础方法的大幅改进。在这篇展望文章中,我们思考了MD模拟的未来发展轨迹以及到2050年它们可能呈现的面貌。我们强调了人工智能(AI)在塑造MD及更广泛的计算物理化学领域未来方面的关键作用。我们概述了对这些技术进行无缝整合至关重要的关键策略和举措。我们的讨论深入探讨了多尺度建模、应对不断增加的数据洪流的有效管理、建立集中式模拟数据库以及这些知识库的自主优化、交叉验证和自我扩展等主题。这些进步的成功实施需要科学透明度、对解释AI驱动的模拟及其分析持谨慎乐观的态度,以及一种在AI增强的大数据探索的同时优先考虑知识驱动研究的思维方式。虽然历史提醒我们技术进步的轨迹可能不可预测,但这篇展望文章提供了关于准备工作和积极措施的指导,旨在将未来的进步导向最有益和成功的方向。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/746c/11274290/7494edb20905/pg4c00003_0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/746c/11274290/cedfedd9a025/pg4c00003_0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/746c/11274290/5a8072b7c5ed/pg4c00003_0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/746c/11274290/7494edb20905/pg4c00003_0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/746c/11274290/cedfedd9a025/pg4c00003_0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/746c/11274290/5a8072b7c5ed/pg4c00003_0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/746c/11274290/7494edb20905/pg4c00003_0003.jpg

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