arXiv subjects

Peter Klein

Publications and source records attributed to Peter Klein.

3 recordsLinked to original sources

arXiv2026

Artificial Intelligence in Materials Science and Engineering: Current Landscape, Key Challenges, and Future Trajectorie

Artificial Intelligence is rapidly transforming materials science and engineering, offering powerful tools to navigate complexity, accelerate discovery, and optimize material design in ways previously unattainable. Driven by the accelerating pace of algorithmic advancements and increasing data availability, AI is becoming an essential competency for materials researchers. This review provides a comprehensive and structured overview of the current landscape, synthesizing recent advancements and methodologies for materials scientists seeking to effectively leverage these data-driven techniques. We survey the spectrum of machine learning approaches, from traditional algorithms to advanced deep learning architectures, including CNNs, GNNs, and Transformers, alongside emerging generative AI and probabilistic models such as Gaussian Processes for uncertainty quantification. The review also examines the pivotal role of data in this field, emphasizing how effective representation and featurization strategies, spanning compositional, structural, image-based, and language-inspired approaches, combined with appropriate preprocessing, fundamentally underpin the performance of machine learning models in materials research. Persistent challenges related to data quality, quantity, and standardization, which critically impact model development and application in materials science and engineering, are also addressed.

cond-mat.mtrl-sci↗

arXiv2023

Adsorption of Diclofenac and PFBS on Hair Keratin Dimer

Environmental pollution by man-made toxic and persistent organic compounds, found throughout the world in surface and groundwater, has various negative effects on aquatic life systems and even humans. Therefore, it is important to develop and improve water treatment technologies capable of removing such substances from wastewater or purifying drinking water. The two substances investigated are the widely used painkiller diclofenac and a member of the class of "forever chemicals", perfluorobutane sulfonate. Both are known to have serious negative effects on living organisms, especially under long-term exposure, and are detectable in human hair, suggesting adsorption to a part of the hair fiber complex. In this study, a human hair keratin dimer is investigated for its ability to absorb diclofenac and perfluorobutane sulfonate. Initial predictions for binding sites are obtained via molecular docking and subjected to molecular dynamics simulations for more than $1$ $\mathrm{\mu s}$. The binding affinities obtained by the linear interaction energy method are high enough to motivate further research on human hair keratins as a sustainable, low-cost, and easily allocatable filtration material.

physics.comp-ph↗

arXiv2014

Molecular modelling and simulation of the surface tension of real quadrupolar fluids

Molecular modelling and simulation of the surface tension of fluids with force fields is discussed. 29 real fluids are studied, including nitrogen, oxygen, carbon dioxide, carbon monoxide, fluorine, chlorine, bromine, iodine, ethane, ethylene, acetylene, propyne, propylene, propadiene, carbon disulfide, sulfur hexafluoride, and many refrigerants. The fluids are represented by two-centre Lennard-Jones plus point quadrupole models from the literature. These models were adjusted only to experimental data of the vapour pressure and saturated liquid density so that the results for the surface tension are predictions. The deviations between the predictions and experimental data for the surface tension are of the order of 20 percent. The surface tension is usually overestimated by the models. For further improvements, data on the surface tension can be included in the model development. A suitable strategy for this is multi-criteria optimization based on Pareto sets. This is demonstrated using the model for carbon dioxide as an example.

physics.comp-ph↗