AIMC Topic: Density Functional Theory

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Computational characterization and machine learning analysis of quantum optimized marine fungal metabolites targeting PD-L1 in cancer immunotherapy.

Journal of computer-aided molecular design
Cancer immune evasion is predominantly mediated through immune checkpoint pathways, such as the PD-1/PD-L1 axis. In this mechanism, PD-L1, which is often overexpressed on tumor cells, binds to PD-1 receptors on T cells, resulting in the inhibition of...

Adsorption Energy Prediction Model for CO Reduction on Electrocatalysts Containing Previously Unencountered Metal Elements.

Journal of chemical information and modeling
Electrochemical carbon dioxide reduction (CORR) using electrocatalysts has gained attention for its potential to convert atmospheric CO into value-added chemicals. Recently, machine learning (ML) has emerged as a promising approach for catalyst devel...

Toward Complete Molecular Structure Prediction from Infrared Spectroscopy Using Deep Learning.

Journal of chemical information and modeling
Infrared (IR) spectroscopy is a broadly used tool to solve the molecular structures of unknown compounds. Though the theory of generating IR spectra from molecules is well established, the inverse problem of solving molecular structures from given sp...

Accurate Simulations of Water and Aqueous Solutions through Fine-Tuned Dispersion-Corrected Density Functional Theory and Machine-Learning Interatomic Potentials.

Journal of chemical information and modeling
Dispersion-corrected density functional theory (DFT-D) is widely employed to model large molecular systems at an affordable computational cost and to develop machine-learning interatomic potentials (MLIPs), enabling reliable molecular dynamics (MD) s...

Low-Cost, High-Accuracy Reactivity Modeling: Integrating Genetic Algorithms and Machine Learning with Multilevel DFT Calculations.

Journal of chemical information and modeling
Accurate prediction of Gibbs activation energies (Δ) for Diels-Alder (DA) reactions remains a critical challenge in computational chemistry, as conventional density functional theory (DFT) methods often fail to consistently achieve chemical accuracy ...

Unraveling electronic structure modulation mechanism in cobalt spinel Fenton-like catalysis by integrating density functional theory and machine learning.

Water research
Heterogeneous Fenton-like catalysis holds significant promise for environmental remediation, yet conventional trial-and-error methodologies fail to capture the intrinsic structure-activity relationships in catalyst design. Herein, we present an integ...

3d electron cloud descriptors for enhanced QSAR modeling of anti-colorectal cancer compounds.

Journal of computer-aided molecular design
To address limitations of conventional Quantitative Structure-Activity Relationship (QSAR) descriptors in capturing molecular electronic and spatial complexity, we developed a high-dimensional framework using three-dimensional electron density featur...

γ-Graphyne as a Functional 2D Nanoarchitectonics for Room-Temperature Chemiresistive-Potentiometric Sensing Interfaces.

ACS sensors
The development of highly selective gas sensors operating at room temperature with detection capabilities in the parts-per-billion (ppb) range is one of fundamental and technological interest across diverse fields. Conventional sensor arrays often su...

Chemical Space Exploration with Artificial "Mindless" Molecules.

Journal of chemical information and modeling
We introduce MindlessGen, a Python-based generator for creating chemically diverse, "mindless" molecules through random atomic placement and subsequent geometry optimization. Using this framework, we constructed the benchmark set, containing 2061 mo...