AIMC Topic: Inorganic Chemicals

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Rational Design of Safer Inorganic Nanoparticles via Mechanistic Modeling-Informed Machine Learning.

ACS nano Jun 3, 2025
The safety of inorganic nanoparticles (NPs) remains a critical challenge for their clinical translation. To address this, we developed a machine learning (ML) framework that predicts NP toxicity both and , leveraging physicochemical properties and e...
Humans Nanoparticles Inorganic Chemicals Animals Machine Learning
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Separating Daily 1 km PM Inorganic Chemical Composition in China since 2000 via Deep Learning Integrating Ground, Satellite, and Model Data.

Environmental science & technology Apr 28, 2023
Fine particulate matter (PM) chemical composition has strong and diverse impacts on the planetary environment, climate, and health. These effects are still not well understood due to limited surface observations and uncertainties in chemical model si...
Environmental Monitoring Air Pollutants China Seasons Aerosols Particulate Matter Air Pollution Deep Learning Reproducibility of Results Inorganic Chemicals Respiratory Aerosols and Droplets
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Prediction of permeability across intestinal cell monolayers for 219 disparate chemicals using in vitro experimental coefficients in a pH gradient system and in silico analyses by trivariate linear regressions and machine learning.

Biochemical pharmacology Aug 27, 2021
For medicines, the apparent membrane permeability coefficients (P) across human colorectal carcinoma cell line (Caco-2) monolayers under a pH gradient generally correlate with the fraction absorbed after oral intake. Furthermore, the in vitro P value...
Inorganic Chemicals Humans Linear Models Intestinal Absorption Caco-2 Cells Forecasting Cell Membrane Permeability Computer Simulation Machine Learning
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Ab Initio Machine Learning in Chemical Compound Space.

Chemical reviews Aug 13, 2021
Chemical compound space (CCS), the set of all theoretically conceivable combinations of chemical elements and (meta-)stable geometries that make up matter, is colossal. The first-principles based virtual sampling of this space, for example, in search...
Inorganic Chemicals Quantum Theory Machine Learning Organic Chemicals
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Energy-Geometry Dependency of Molecular Structures: A Multistep Machine Learning Approach.

ACS combinatorial science Aug 21, 2019
There is growing interest in estimating quantum observables while circumventing expensive computational overhead for facile in silico materials screening. Machine learning (ML) methods are implemented to perform such calculations in shorter times. He...
Small Molecule Libraries Thermodynamics Inorganic Chemicals Machine Learning Ions Molecular Structure Databases, Chemical Organic Chemicals Models, Chemical Quantum Theory Computer Simulation
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Intuition-Enabled Machine Learning Beats the Competition When Joint Human-Robot Teams Perform Inorganic Chemical Experiments.

Journal of chemical information and modeling May 22, 2019
Traditionally, chemists have relied on years of training and accumulated experience in order to discover new molecules. But the space of possible molecules is so vast that only a limited exploration with the traditional methods can be ever possible. ...
Inorganic Chemicals Models, Chemical Machine Learning Tungsten Compounds Computational Chemistry Crystallization Humans Robotics
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Machine Learning Accelerated Discovery of Antimicrobial Inorganic Nanomaterials.

The journal of physical chemistry letters Jun 12, 2025
The growing prevalence of infectious diseases and the increasing threat of bacterial resistance have drawn widespread attention to antimicrobial inorganic nanomaterials. However, the diversity, abundance, and complex mechanisms of these materials pre...
Inorganic Chemicals Nanostructures Microbial Sensitivity Tests Machine Learning Structure-Activity Relationship Anti-Bacterial Agents Bacteria
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