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Artificial Intelligence Chemistry
Scopus
ISSN 2949-7477 · Elsevier · 122 artikel di indeks
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Artikel Jurnal
Accelerating accurate multi-property prediction of perovskites via meta-heuristic algorithm tuned machine learning
The vast compositional space of ABX3 perovskites presents a dual bottleneck for materials discovery: prohibitive screening costs and inefficient hyperparameter optimization (HPO) required to develop accurate predictive models. To address this computational challenge, we introduce a systematic…
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Artikel Jurnal
MEP-CNN: A visual and explainable machine learning framework for explosive molecule classification based on molecular electrostatic potentials
Identifying explosive molecules is crucial for safety and materials research. In this work, we propose a machine learning framework for classifying 119 molecules using images of their electrostatic potential maps (MEPs), computed from quantum-chemical methods and depicting the molecules' positive…
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Physics-informed neural networks for automated extraction of scalar coupling constants and chemical shifts from strongly coupled NMR spectra: ABC and ABCD spin systems
We present an automated pipeline for extracting scalar coupling constants J and chemical shifts from strongly coupled 1H NMR spectra of ABC and ABCD spin systems. It operates on an isolated peak list of the target spin system without requiring initial parameters or manual transition assignment. It…
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Actinium–DOTA coordination in water from hybrid ML/MM: Structure, free energies, and water-exchange pathways
Quantitative simulation of trivalent f-block chelates in water remains challenging because bonded and non-bonded force-field models make different approximations for coordination structure, exchange dynamics, and ion–ligand interactions in highly charged systems. Here, we develop a hybrid…
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Maximizing machine learning interatomic potential transferability for the discovery of the novel stellated octadecagon Bi18-Pt24 cage structure
Achieving true transferability remains the central challenge for Machine Learning Interatomic Potentials (ML-IAPs) in modeling complex bimetallic nanoclusters across their vast potential energy surfaces. We systematically investigate data selection strategies to optimize the Chebyshev Interaction…
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Predicting heavy-metal retention in montmorillonite: Physics-informed machine learning guided by XRD hydration states and molecular dynamics
Montmorillonite plays a central role in environmental barrier systems because its hydrated interlayers, high cation-exchange capacity (CEC), and structural flexibility strongly influence heavy-metal retention. However, predictive evaluation across varying humidity histories, cation identities, and…
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Quantitative assessment of the structure of (Na2O)0.1-(V2O5)0.6-(TeO2)0.3 glass: An ab initio and machine learning interatomic potential study of oxide systems with mixed-valence
This study investigates the atomic-scale structure of a glassy system using first-principles molecular dynamics (FPMD) and machine-learned interatomic potentials (MLIP). The structural models generated using MLIP demonstrate excellent agreement with FPMD results while offering substantial…
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Artikel Jurnal
Temperature driven morphological changes of palladium nanoparticles from machine learning force fields
Machine learning force fields trained on the fly on ab initio molecular dynamics simulations using density functional theory are developed to explore the evolution of five selected morphologies of Pd147 nanoparticles (≈1.6 nm) at finite temperature. At 373 K, the simulations performed from the…
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PepGen-FB: Multi-functional antimicrobial peptide generation via curriculum learning-guided multi-objective feedback optimization
Antimicrobial peptides (AMPs) emerge as a type of promising therapeutic compounds that exhibit broad spectrum antimicrobial activity with high specificity and good tolerability. However, current AI-based AMP design strategies, which primarily rely on learning the distribution of natural AMPs, fail…
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Artikel Jurnal
Prediction of heavy metal adsorption by activated carbon using machine learning
Heavy metal contamination in water is a major environmental challenge due to its toxicity, persistence, and potential for bioaccumulation in living organisms. Among the various treatment technologies, adsorption using activated carbon remains one of the most effective and economical methods for…
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A comparison of small molecule generation methods in structure-based drug design: Quantum-Aided Drug Design (QuADD) vs bond and interaction generating Diffusion model (BInD)
Quantum-Aided Drug Design (QuADD) is a platform that utilizes quantum computing to formulate molecular design as a multi-objective optimization problem, enabling the generation of drug-like molecules optimized for interactions within a defined binding pocket. Generative artificial intelligence has…
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Artikel Jurnal
Physics-guided neural network with adaptive constraint weighting for predicting the bulk modulus of high-entropy alloys
High-entropy alloys (HEAs) present significant challenges for property prediction due to their vast compositional freedom and limited reliable data. Without tractable governing equations, elastic properties have been rationalized using physically motivated descriptors such as elastic bounds and…
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Image 1 ChemPro: A progressive chemistry benchmark for Large Language Models
We introduce ChemPro, a progressive benchmark with 4100 natural language question-answer pairs in Chemistry, across 4 coherent sections of difficulty designed to assess the proficiency of Large Language Models (LLMs) in a broad spectrum of general chemistry topics. We include Multiple Choice…
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Graph attention network integrating sequence semantics and structural constraint information for robust antiviral peptide prediction
Viral infections remain a major global health challenge, with current antiviral therapies often limited by drug resistance and high development costs. Antiviral peptides (AVPs) are promising alternatives to traditional antiviral drugs owing to their safety and broad-spectrum activity. Most existing…
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Quantum-chemistry informed Bayesian optimization for the accelerated discovery of novel pyrazole-based energetic materials
Developing high-performance Energetic Materials (EMs) with small environmental footprint remains a critical challenge for both civilian and military applications. The conventional EMs such as Research Department Explosive (RDX) and trinitrotoluene (TNT) exhibit strong performance, but they tend to…
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Finding low-energy experimental metal-organic frameworks for carbon capture via Bayesian optimization in the manifold subspace of geometric descriptors
Carbon capture, utilization, and storage (CCUS) technologies are now being developed to meet the global net-zero emissions target. Due to their high surface area and high porosity, metal-organic frameworks (MOFs) are promising solid sorbent candidates for post-combustion carbon capture. Recent…
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A computational pipeline combining machine learning and molecular simulations identifies repurposed PI3Kα inhibitors from FDA-approved drugs
The computational repurposing of existing drugs has proven to be a fast-track strategy in the development of new cancer therapy. Nonetheless, integrated mechanistically grounded approaches are required for a more reliable in silico identification of new drug-target pairs. In this study, we…
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Transformative role of deep learning in Raman spectroscopy-based detection of microplastics and nanoplastics
The pervasive presence of microplastics and nanoplastics (MNPs) in aquatic, terrestrial, and atmospheric systems has emerged as an important environmental challenge. Accurate detection and classification of these particles are essential for understanding their sources, fate, and potential risks to…
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Beyond predicted zT: Machine learning strategies for the experimental discovery of thermoelectric materials
The discovery of high-performance thermoelectric (TE) materials for advancing green energy harvesting from waste heat is an urgent need in the context of looming energy crisis and climate change. The rapid advancement of machine learning (ML) has accelerated the design of thermoelectric materials…
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Accelerated green material and solvent discovery with chemistry- and physics-guided generative AI
Carbon capture, utilization and storage (CCUS), along with lignocellulosic biomass valorization (e.g., lignin, cellulose), are promising decarbonization strategies for hard-to-abate industries. Green solvents, such as deep eutectic solvents and ionic liquids, enable efficient CO₂ capture and…