不断扩大的神经科学世界

IF 5.7 1区 化学 Q2 CHEMISTRY, PHYSICAL Journal of Chemical Theory and Computation Pub Date : 2024-10-17 DOI:10.1016/j.cell.2024.09.015
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引用次数: 0

摘要

了解大脑令全世界许多人着迷和好奇。在这期 50 周年 "聚焦神经科学 "特刊中,我们将介绍前沿内容,反映该领域的进展,重点介绍新出现的主题,并为未来更多年激动人心的神经科学研究铺平道路。
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The expanding world of neuroscience
Understanding the brain fascinates and intrigues many across the world. In this 50th Anniversary “Focus on Neuroscience” issue, we present Leading Edge content reflecting on the progress of the field, highlighting emerging topics, and paving the way toward many more years of exciting neuroscience research.
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来源期刊
Journal of Chemical Theory and Computation
Journal of Chemical Theory and Computation 化学-物理:原子、分子和化学物理
CiteScore
9.90
自引率
16.40%
发文量
568
审稿时长
1 months
期刊介绍: The Journal of Chemical Theory and Computation invites new and original contributions with the understanding that, if accepted, they will not be published elsewhere. Papers reporting new theories, methodology, and/or important applications in quantum electronic structure, molecular dynamics, and statistical mechanics are appropriate for submission to this Journal. Specific topics include advances in or applications of ab initio quantum mechanics, density functional theory, design and properties of new materials, surface science, Monte Carlo simulations, solvation models, QM/MM calculations, biomolecular structure prediction, and molecular dynamics in the broadest sense including gas-phase dynamics, ab initio dynamics, biomolecular dynamics, and protein folding. The Journal does not consider papers that are straightforward applications of known methods including DFT and molecular dynamics. The Journal favors submissions that include advances in theory or methodology with applications to compelling problems.
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Bayesian Approach for Computing Free Energy on Perturbation Graphs with Cycles. Deterministic and Faster GW Calculations with a Reduced Number of Valence States: O(N2 ln N) Scaling in the Plane-Waves Formalism. The Dynamic Diversity and Invariance of Ab Initio Water. Automatic Feature Selection for Atom-Centered Neural Network Potentials Using a Gradient Boosting Decision Algorithm. Data Quality in the Fitting of Approximate Models: A Computational Chemistry Perspective.
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