Metal-organic frameworks (MOFs) exhibit promise as porous materials for carbon capture due to their design versatility and large pore sizes. The generic force fields (e.g., UFF and Dreiding) use one universal set of Lennard-Jones parameters for each element, while MOFs have a much richer local chemical environment than those chemical environments used to fit the UFF. When MOFs contain hard-Lewis acid metals, UFF systematically overestimates CO uptakes. To address this, we developed a workflow to affordably and efficiently generate reliable force fields to predict CO adsorption isotherms of MOFs containing metals from groups IIA (Mg, Ca, Sr, and Ba) and IIIA (Al, Ga, and In), connected to various carboxylate ligands. This method uses experimental isotherms as input. The optimal parameters are obtained by minimizing the loss function of the experimental and simulated isotherms, in which we use the Multistate Bennett Acceptance Ratio (MBAR) theory to derive the functionality relationship of loss functions in terms of force field parameters.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11635978 | PMC |
| http://dx.doi.org/10.1021/acs.jctc.4c01113 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Simulating large molecular systems over long timescales requires force fields that are both accurate and efficient. In recent years, E(3) equivariant neural networks have lifted the tension between computational efficiency and accuracy of force fields, but they are still several orders of magnitude more expensive than established molecular mechanics (MM) force fields. Here, we propose Grappa, a machine learning framework to predict MM parameters from the molecular graph, employing a graph attentional neural network and a transformer with symmetry-preserving positional encoding.
View Article and Find Full Text PDFThe Role of Machine Learning Approaches in Pediatric Oncology: A Systematic Review.
Cureus
January 2025
General Practice, Wad Medani Hospital, Wad Medani, SDN.
To enhance patient outcomes in pediatric cancer, a better understanding of the medical and biological risk variables is required. With the growing amount of data accessible to research in pediatric cancer, machine learning (ML) is a form of algorithmic inference from sophisticated statistical techniques. In addition to highlighting developments and prospects in the field, the objective of this systematic study was to methodically describe the state of ML in pediatric oncology.
View Article and Find Full Text PDFElemental Germanium Activation and Catalysis Enabled by Mechanical Force.
Angew Chem Int Ed Engl
January 2025
Sichuan University West China Hospital, State key laboratory of biotherapy, Renming South Road 17, 610041, Chengdu, CHINA.
In the realm of materials science and chemical industry, germanium emerges as a strategic resource with distinctive properties that extend its applicability beyond traditional electronics and optics into the promising field of chemical catalysis. Despite its significant role in advanced technological applications, the potential of elemental germanium as a catalyst remains unexplored. Leveraging recent developments in mechanochemistry, this study introduces a groundbreaking approach to activate elemental germanium via mechanical force, facilitating the Reformatsky reaction without the reliance on external reducing agents.
View Article and Find Full Text PDFContinuous Strain Modulation of Moiré Superlattice Symmetry From Triangle to Rectangle.
Small
January 2025
Department of Applied Physics, Nagoya University, Nagoya, 464-8603, Japan.
Moiré superlattices formed in van der Waals (vdW) bilayers of 2D materials provide an ideal platform for studying previously undescribed physics, including correlated electronic states and moiré excitons, owing to the wide-range tunability of their lattice constants. However, their crystal symmetry is fixed by the monolayer structure, and the lack of a straightforward technique for modulating the symmetry of moiré superlattices has impeded progress in this field. Herein, a simple, room-temperature, ambient method for controlling superlattice symmetry is reported.
View Article and Find Full Text PDFThe influence of temperature and pressure on the self-diffusion characteristics and mechanical sensitivity of DNTF: a molecular dynamics study.
J Mol Model
January 2025
School of Safety Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China.
Context: 3,4-Bis(3-nitrofurazan-4-yl) furoxan (DNTF) is a typical low-melting-point, high-energy-density compound that can serve as a cast carrier explosive. Therefore, understanding the safety of DNTF under different casting processes is of great significance for its efficient application. This study employed molecular dynamics simulations to investigate the effects of temperature and pressure on the self-diffusion characteristics and mechanical sensitivity of DNTF.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!