Hybrid quantum mechanics/molecular mechanics (QM/MM) simulations have become an essential tool in computational chemistry, particularly for analyzing complex biological and condensed phase systems. Building on this foundation, our work presents a novel implementation of the Gaussian Electrostatic Model (GEM), a polarizable density-based force field, within the QM/MM framework. This advancement provides seamless integration, enabling efficient and optimized QM/GEM calculations in a single step using the LICHEM Code. We have successfully applied our implementation to water dimers and hexamers, demonstrating the ability to handle water systems with varying numbers of water molecules. Moreover, we have extended the application to describe the double proton transfer of the aspartic acid dimer in a box of water, which highlights the method's proficiency in investigating heterogeneous systems. Our implementation offers the flexibility to perform on-the-fly density fitting or to utilize pre-fitted coefficients to estimate exchange and Coulomb contributions. This flexibility enhances efficiency and accuracy in modeling molecular interactions, especially in systems where polarization effects are significant.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11223170 | PMC |
http://dx.doi.org/10.1063/5.0200722 | DOI Listing |
Mol Inform
January 2025
Department of Biosystems Science and Engineering, ETH Zurich, Klingelbergstrasse 48, 4056, Basel, Switzerland.
Utilizing the growing wealth of chemical reaction data can boost synthesis planning and increase success rates. Yet, the effectiveness of machine learning tools for retrosynthesis planning and forward reaction prediction relies on accessible, well-curated data presented in a structured format. Although some public and licensed reaction databases exist, they often lack essential information about reaction conditions.
View Article and Find Full Text PDFAdv Mater
January 2025
Jiangsu Key Laboratory for Science and Applications of Molecular Ferroelectrics, Southeast University, Nanjing, 211189, P. R. China.
Utilizing the correlation among diverse physical properties to facilitate multiplexing and multistate memory is anticipated to emerge as an efficient strategy to enhance memory capacity, achieve device miniaturization, and ensure information security. As an important functional material, ferroelectrics have long been considered as a potential candidate in multistate memory devices. Furthermore, the integration of optical response offers an alternative path to realizing multiplexing features, further enhancing the versatility and efficiency of these devices.
View Article and Find Full Text PDFJMIR Form Res
January 2025
ICMR-National Institute for Research in Digital Health and Data Science, Ansari Nagar, New Delhi, 110029, India, 91 7840870009.
Background: Verbal autopsy (VA) has been a crucial tool in ascertaining population-level cause of death (COD) estimates, specifically in countries where medical certification of COD is relatively limited. The World Health Organization has released an updated instrument (Verbal Autopsy Instrument 2022) that supports electronic data collection methods along with analytical software for assigning COD. This questionnaire encompasses the primary signs and symptoms associated with prevalent diseases across all age groups.
View Article and Find Full Text PDFSci Data
January 2025
Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
Immune checkpoint blockade (ICB) therapies have emerged as a promising avenue for the treatment of various cancers. Despite their success, the efficacy of these treatments is variable across patients and cancer types. Numerous single-cell RNA-sequencing (scRNA-seq) studies have been conducted to unravel cell-specific responses to ICB treatment.
View Article and Find Full Text PDFInt Dent J
January 2025
Department of Endodontics, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania.
Introduction: Artificial intelligence (AI), including its subfields of machine learning and deep learning, is a branch of computer science and engineering focused on creating machines capable of tasks requiring human-like intelligence, such as visual perception, decision-making, and natural language processing. AI applications have become increasingly prevalent in dental medicine, generating high expectations as well as raising ethical and practical concerns.
Methods: This critical review evaluates the current applications of AI in dentistry, identifying key perspectives, challenges, and limitations in ongoing AI research.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!