jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yijing Tang | Yonglan Liu | Dong Zhang | Jie Zheng
Source: Langmuir (article, 2023-12-28)
Field: Materials Science — Surfaces, Coatings and Films

Perspectives on Theoretical Models and Molecular Simulations of Polymer Brushes Polymer brushes have witnessed extensive utilization and progress, driven by their distinct attributes in surface modification, tethered group functionality, and tailored interactions at the nanoscale, enabling them for various scientific and industrial applications of coatings, sensors, switchable/responsive materials, nanolithography, and lab-on-a-chips. Despite the wealth of experimental investigations into polymer brushes, this review primarily focuses on computational studies of antifouling polymer brushes with a strong emphasis on achieving a molecular-level understanding and structurally designing antifouling polymer brushes. Computational exploration covers three realms of thermotical models, molecular simulations, and machine-learning approaches to elucidate the intricate relationship between composition, structure, and properties concerning polymer brushes in the context of nanotribology, surface hydration, and packing conformation. Upon acknowledging the challenges currently faced, we extend our perspectives toward future research directions by delineating potential avenues and unexplored territories. Our overarching objective is to advance our foundational comprehension and practical utilization of polymer brushes for antifouling applications, leveraging the synergy between computational methods and materials design to drive innovation in this crucial field. Nanotechnology ; Biofouling ; Context (archaeology) ; Materials science ; Polymer ; Computer science ; Chemistry Polymer Surface Interaction Studies ; Marine Biology and Environmental Chemistry ; Adhesion, Friction, and Surface Interactions
Authors: José Ferraz-Caetano | Filipe Teixeira | M. Natália D. S. Cordeiro
Source: Journal of Chemical Information and Modeling (article, 2023-08-21)
Field: Materials Science — Materials Chemistry

Explainable Supervised Machine Learning Model To Predict Solvation Gibbs Energy Many challenges persist in developing accurate computational models for predicting solvation free energy (Δ<i>G</i><sub>sol</sub>). Despite recent developments in Machine Learning (ML) methodologies that outperformed traditional quantum mechanical models, several issues remain concerning explanatory insights for broad chemical predictions with an acceptable speed-accuracy trade-off. To overcome this, we present a novel supervised ML model to predict the Δ<i>G</i><sub>sol</sub> for an array of solvent-solute pairs. Using two different ensemble regressor algorithms, we made fast and accurate property predictions using open-source chemical features, encoding complex electronic, structural, and surface area descriptors for every solvent and solute. By integrating molecular properties and chemical interaction features, we have analyzed individual descriptor importance and optimized our model though explanatory information form feature groups. On aqueous and organic solvent databases, ML models revealed the predictive relevance of solutes with increasing polar surface area and decreasing polarizability, yielding better results than state-of-the-art benchmark Neural Network methods (without complex quantum mechanical or molecular dynamic simulations). Both algorithms successfully outperformed previous Δ<i>G</i><sub>sol</sub> predictions methods, with a maximum absolute error of 0.22 ± 0.02 kcal mol<sup>-1</sup>, further validated in an external benchmark database and with solvent hold-out tests. With these explanatory and statistical insights, they allow a thoughtful application of this method for predicting other thermodynamic properties, stressing the relevance of ML modeling for further complex computational chemistry problems. Solvation ; Benchmark (surveying) ; Accessible surface area ; Machine learning ; Computer science ; Artificial intelligence ; Relevance (law) ; Polarizability ; Artificial neural network ; Feature (linguistics) ; Molecular descriptor ; Gibbs free energy ; Solvophobic ; Quantum chemical ; Chemistry ; Quantitative structure–activity relationship ; Computational chemistry ; Molecule ; Thermodynamics ; Physics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Alexander Kovacs | Johann Fischbacher | Harald Oezelt | Alexander Kornell | Qais Ali | Markus Gusenbauer | Masao Yano | Noritsugu Sakuma | Akihito Kinoshita | Tetsuya Shoji | Akira Kato | Yuan Hong | S. Grenier | Thibaut Devillers | Nora M. Dempsey | Tetsuya Fukushima | H. Akai | Naoki Kawashima | Takashi Miyake | T. Schrefl
Source: Frontiers in Materials (article, 2023-01-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Physics-informed machine learning combining experiment and simulation for the design of neodymium-iron-boron permanent magnets with reduced critical-elements content Rare-earth elements like neodymium, terbium and dysprosium are crucial to the performance of permanent magnets used in various green-energy technologies like hybrid or electric cars. To address the supply risk of those elements, we applied machine-learning techniques to design magnetic materials with reduced neodymium content and without terbium and dysprosium. However, the performance of the magnet intended to be used in electric motors should be preserved. We developed machine-learning methods that assist materials design by integrating physical models to bridge the gap between length scales, from atomistic to the micrometer-sized granular microstructure of neodymium-iron-boron permanent magnets. Through data assimilation, we combined data from experiments and simulations to build machine-learning models which we used to optimize the chemical composition and the microstructure of the magnet. We applied techniques that help to understand and interpret the results of machine learning predictions. The variables importance shows how the main design variables influence the magnetic properties. High-throughput measurements on compositionally graded sputtered films are a systematic way to generate data for machine data analysis. Using the machine learning models we show how high-performance, Nd-lean magnets can be realized. Neodymium ; Magnet ; Dysprosium ; Materials science ; Neodymium magnet ; Microstructure ; Mechanical engineering ; Computer science ; Machine learning ; Artificial intelligence ; Physics ; Composite material ; Engineering ; Optics ; Nuclear physics Magnetic Properties and Applications ; Magnetic properties of thin films ; Magnetic Properties of Alloys
Authors: Christian Sandoval‐Pauker | Sheng Yin | Alexandria Castillo | Neidy Ocuane | Diego Puerto-Diaz | D. Villagrán
Source: ACS ES&T Engineering (article, 2023-10-12)
Field: Materials Science — Materials Chemistry

Computational Chemistry as Applied in Environmental Research: Opportunities and Challenges The constant development of computer systems and infrastructure has allowed computational chemistry to become an important component of environmental chemistry research. In the past decade, the application of quantum and classical mechanical calculations to model and understand environmental systems has increased exponentially. In this review, we highlight various applications of computational chemistry techniques in areas of environmental chemistry research (e.g., wastewater/air treatment, sensing, biodegradation). We briefly describe each computational approach, starting with quantum chemistry principle methods followed by molecular mechanics (MM), molecular dynamics (MD), and hybrid QM/MM methods. The recent introduction of artificial intelligence and machine learning techniques and their potential to disrupt the field are also discussed. Challenges and current and future directions to address them are presented. Computer science ; Quantum chemistry ; Biochemical engineering ; Field (mathematics) ; Management science ; Molecular dynamics ; Nanotechnology ; Chemistry ; Computational chemistry ; Engineering ; Molecule ; Materials science ; Mathematics Machine Learning in Materials Science ; Water Quality Monitoring and Analysis ; Air Quality Monitoring and Forecasting
Authors: Yongtao Liu | Jonghee Yang | Rama K. Vasudevan | Kyle P. Kelley | Maxim Ziatdinov | Sergei V. Kalinin | Mahshid Ahmadi
Source: The Journal of Physical Chemistry Letters (article, 2023-03-30)
Field: Materials Science — Materials Chemistry

Exploring the Relationship of Microstructure and Conductivity in Metal Halide Perovskites via Active Learning-Driven Automated Scanning Probe Microscopy Electronic transport and hysteresis in metal halide perovskites (MHPs) are key to the applications in photovoltaics, light emitting devices, and light and chemical sensors. These phenomena are strongly affected by the materials microstructure including grain boundaries, ferroic domain walls, and secondary phase inclusions. Here, we demonstrate an active machine learning framework for "driving" an automated scanning probe microscope (SPM) to discover the microstructures responsible for specific aspects of transport behavior in MHPs. In our setup, the microscope can discover the microstructural elements that maximize the onset of conduction, hysteresis, or any other characteristic that can be derived from a set of current-voltage spectra. This approach opens new opportunities for exploring the origins of materials functionality in complex materials by SPM and can be integrated with other characterization techniques either before (prior knowledge) or after (identification of locations of interest for detail studies) functional probing. Microstructure ; Materials science ; Hysteresis ; Photovoltaics ; Scanning probe microscopy ; Characterization (materials science) ; Nanotechnology ; Grain boundary ; Optical microscope ; Scanning electron microscope ; Microscopy ; Optoelectronics ; Halide ; Optics ; Composite material ; Chemistry ; Electrical engineering ; Condensed matter physics ; Photovoltaic system ; Engineering ; Inorganic chemistry ; Physics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Electrochemical Analysis and Applications
Authors: Yi Xia | Dale Gaines | Jiangang He | Koushik Pal | Zhi Li | Mercouri G. Kanatzidis | Vidvuds Ozoliņš | Chris Wolverton
Source: Proceedings of the National Academy of Sciences (article, 2023-06-20)
Field: Materials Science — Materials Chemistry

A unified understanding of minimum lattice thermal conductivity We propose a first-principles model of minimum lattice thermal conductivity ([Formula: see text]) based on a unified theoretical treatment of thermal transport in crystals and glasses. We apply this model to thousands of inorganic compounds and find a universal behavior of [Formula: see text] in crystals in the high-temperature limit: The isotropically averaged [Formula: see text] is independent of structural complexity and bounded within a range from ∼0.1 to ∼2.6 W/(m K), in striking contrast to the conventional phonon gas model which predicts no lower bound. We unveil the underlying physics by showing that for a given parent compound, [Formula: see text] is bounded from below by a value that is approximately insensitive to disorder, but the relative importance of different heat transport channels (phonon gas versus diffuson) depends strongly on the degree of disorder. Moreover, we propose that the diffuson-dominated [Formula: see text] in complex and disordered compounds might be effectively approximated by the phonon gas model for an ordered compound by averaging out disorder and applying phonon unfolding. With these insights, we further bridge the knowledge gap between our model and the well-known Cahill-Watson-Pohl (CWP) model, rationalizing the successes and limitations of the CWP model in the absence of heat transfer mediated by diffusons. Finally, we construct graph network and random forest machine learning models to extend our predictions to all compounds within the Inorganic Crystal Structure Database (ICSD), which were validated against thermoelectric materials possessing experimentally measured ultralow <i>κ</i><sub>L</sub>. Our work offers a unified understanding of [Formula: see text], which can guide the rational engineering of materials to achieve [Formula: see text]. Algorithm ; Artificial intelligence ; Computer science Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Machine Learning in Materials Science
Authors: Tyler Sours | Ambarish Kulkarni
Source: The Journal of Physical Chemistry C (article, 2023-01-13)
Field: Materials Science — Materials Chemistry

Predicting Structural Properties of Pure Silica Zeolites Using Deep Neural Network Potentials Machine learning potentials (MLPs) capable of accurately describing complex <i>ab initio</i> potential energy surfaces (PESs) have revolutionized the field of multiscale atomistic modeling. In this work, using an extensive density functional theory (DFT) data set (denoted as Si-ZEO22) consisting of 219 unique zeolite topologies (350,000 unique DFT calculations) found in the International Zeolite Association (IZA) database, we have trained a DeePMD-kit MLP to model the dynamics of silica frameworks. The performance of our model is evaluated by calculating various properties that probe the accuracy of the energy and force predictions. This MLP demonstrates impressive agreement with DFT for predicting zeolite structural properties, energy-volume trends, and phonon density of states. Furthermore, our model achieves reasonable predictions for stress-strain relationships without including DFT stress data during training. These results highlight the ability of MLPs to capture the flexibility of zeolite frameworks and motivate further MLP development for nanoporous materials with near-<i>ab initio</i> accuracy. Nanoporous ; Zeolite ; Density functional theory ; Ab initio ; Artificial neural network ; Materials science ; Computer science ; Flexibility (engineering) ; Force field (fiction) ; Computational chemistry ; Artificial intelligence ; Chemistry ; Nanotechnology ; Mathematics Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis ; X-ray Diffraction in Crystallography
Authors: Zekun Chen | Margaret L. Berrens | Kam-Tung Chan | Zheyong Fan | Davide Donadio
Source: Journal of Chemical & Engineering Data (article, 2023-12-11)
Field: Materials Science — Materials Chemistry

Thermodynamics of Water and Ice from a Fast and Scalable First-Principles Neuroevolution Potential Machine learning potentials enable molecular dynamics simulations to exceed the size and time scales that can be accessed by first-principles methods like density functional theory, while still maintaining the accuracy of the underlying training data set. However, accurate machine learning potentials come with relatively high computational costs that limit their ability to predict properties requiring extensive sampling, large simulation cells, or long runs to converge. Here, we have developed and tested a neuroevolution-potential model for water trained to hybrid-dispersion-corrected density functional calculations. This model exhibits accuracy and transferability comparable to state-of-the-art machine learning potentials but at a much lower computational cost. As a result, it enabled us to compute well-converged thermodynamic averages and fluctuations. This allowed us to assess the ability of our model to reproduce several thermodynamic properties of water and ice as well as the anomalous behavior of water density, heat capacity, and compressibility. The efficient GPU acceleration of our model and its capability to reproduce water thermodynamics in good agreement with experiments make it suitable for simulating phase transitions and slow dynamic processes in aqueous systems. Statistical physics ; Scalability ; Limit (mathematics) ; Chemistry ; Artificial neural network ; Molecular dynamics ; Computer science ; Artificial intelligence ; Physics Machine Learning in Materials Science ; Quantum, superfluid, helium dynamics ; Quantum many-body systems
Authors: Jaimie Greasley | Patrick Hosein
Source: Journal of Materials Science (article, 2023-03-01)
Field: Materials Science — Materials Chemistry

Exploring supervised machine learning for multi-phase identification and quantification from powder X-ray diffraction spectra Materials science ; Diffraction ; Powder diffraction ; Artificial intelligence ; Identification (biology) ; Characterization (materials science) ; Rietveld refinement ; Machine learning ; Phase (matter) ; Computer science ; Crystallography ; Nanotechnology ; Optics ; Physics ; Chemistry X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Advanced materials and composites
Authors: Aleksandr Fedorov | Anna Perechodjuk | David Linke
Source: Chemical Engineering Journal (article, 2023-10-25)
Field: Materials Science — Materials Chemistry

Kinetics-constrained neural ordinary differential equations: Artificial neural network models tailored for small data to boost kinetic model development Artificial neural network ; Ordinary differential equation ; Computer science ; Key (lock) ; Range (aeronautics) ; Experimental data ; Kinetic energy ; Small data ; Data-driven ; Machine learning ; Artificial intelligence ; Differential equation ; Mathematics ; Engineering ; Physics Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Neural Networks and Applications
Authors: Joseph B. Choi | Phong Nguyen | Oishik Sen | H. S. Udaykumar | Stephen Baek
Source: Propellants Explosives Pyrotechnics (article, 2023-02-18)
Field: Materials Science — Materials Chemistry

Artificial Intelligence Approaches for Energetic Materials by Design: State of the Art, Challenges, and Future Directions Abstract Artificial intelligence (AI) is rapidly emerging as a enabling tool for solving complex materials design problems. This paper aims to review recent advances in AI‐driven materials‐by‐design and their applications to energetic materials (EM). Trained with data from numerical simulations and/or physical experiments, AI models can assimilate trends and patterns within the design parameter space, identify optimal material designs (micro‐morphologies, combinations of materials in composites, etc.), and point to designs with superior/targeted property and performance metrics. We review approaches focusing on such capabilities with respect to the three main stages of materials‐by‐design, namely representation learning of microstructure morphology (i. e., shape descriptors), structure‐property‐performance (S−P−P) linkage estimation, and optimization/design exploration. We leave out “process” as much work remains to be done to establish the connectivity between process and structure. We provide a perspective view of these methods in terms of their potential, practicality, and efficacy towards the realization of materials‐by‐design. Specifically, methods in the literature are evaluated in terms of their capacity to learn from a small/limited number of data, computational complexity, generalizability/scalability to other material species and operating conditions, interpretability of the model predictions, and the burden of supervision/data annotation. Finally, we suggest a few promising future research directions for EM materials‐by‐design, such as meta‐learning, active learning, Bayesian learning, and semi‐/weakly‐supervised learning, to bridge the gap between machine learning research and EM research. State (computer science) ; Nanotechnology ; Engineering physics ; Engineering ; Computer science ; Systems engineering ; Management science ; Materials science ; Programming language Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Fuel Cells and Related Materials
Authors: Jawaher AlYammahi | Ahmad S. Darwish | Ghaiath Almustafa | Tarek Lemaoui | Inas M. AlNashef | Shadi W. Hasan | Hanifa Taher | Fawzi Banat
Source: Ultrasonics Sonochemistry (article, 2023-07-04)
Field: Materials Science — Biomaterials

Natural deep eutectic solvents for Ultrasonic-Assisted extraction of nutritious date Sugar: Molecular Screening, Experimental, and prediction The aim of this study is to develop an environmentally friendly and effective method for the extraction of nutritious date sugar using natural deep eutectic solvents (NADES) and ultrasound-assisted extraction (USAE). The careful design of a suitable NADES-USAE system was systematically supported by COSMO-RS screening, response surface method (RSM) and artificial neural network (ANN). Initially, 26 natural hydrogen bond donors (HBDs) were carefully screened for sugar affinity using COSMO-RS. The best performing HBDs were then used for the synthesis of 5 NADES using choline chloride (ChCl) as HBA. Among the synthesized NADES, the mixture of ChCl, citric acid (CA) and water (1:1:1 with 20 wt% water) resulted in the highest sugar yield of 78.30 ± 3.91 g/100 g, which is superior to conventional solvents such as water (29.92 ± 1.50 g/100 g). Further enhancements using RSM and ANN led to an even higher sugar recovery of 87.81 ± 2.61 g/100 g, at conditions of 30 °C, 45 min, and a solvent to DFP ratio of 40 mL/g. The method NADES-USAE was then compared with conventional hot water extraction (CHWE) (61.36 ± 3.06) and showed 43.1% higher sugar yield. The developed process not only improves the recovery of the nutritious date sugar but also preserves the heat-sensitive bioactive compounds in dates, making it an attractive alternative to CHWE for industrial utilization. Overall, this study shows a promising approach for the extraction of nutritive sugars from dates using environmentally friendly solvents and advanced technology. It also highlights the potential of this approach for valorizing underutilized fruits and preserving their bioactive compounds. Deep eutectic solvent ; Choline chloride ; Sugar ; Extraction (chemistry) ; Response surface methodology ; Chemistry ; Environmentally friendly ; Yield (engineering) ; Citric acid ; Solvent ; Food science ; Eutectic system ; Nuclear chemistry ; Organic chemistry ; Chromatography ; Materials science ; Biology Nanocomposite Films for Food Packaging ; Date Palm Research Studies ; Essential Oils and Antimicrobial Activity
Authors: Ning Wang | Baoyin Yuan | Fangyuan Zheng | Shanyun Mo | Xiaohan Zhang | Lei Du | Lixin Xing | Ling Meng | Lei Zhao | Yoshitaka Aoki | Chunmei Tang | Siyu Ye
Source: Advanced Functional Materials (article, 2023-12-08)
Field: Materials Science — Materials Chemistry

Machine‐Learning Assisted Screening Proton Conducting Co/Fe based Oxide for the Air Electrode of Protonic Solid Oxide Cell Abstract Proton‐conducting solid oxide cells (P‐SOCs) as energy conversion devices for power generation and hydrogen production have attracted increasing attention recently. The lack of efficient proton‐conducting air electrodes is a huge obstacle to developing high‐performance P‐SOCs. The currently widely used air electrode material is Co/Fe based perovskite oxide, however, there is still no systematic research on studying and comparing the roles of diversiform elements at the B site for Co/Fe based perovskite oxide. Here, a machine learning (ML) model with eXtreme Gradient Boosting (XGBoost) algorithm is built to quickly and accurately predict the proton absorption amount of Co/Fe based perovskite oxides with 27 elements dopant at B site. Hereafter, La(Co 0.9 Ni 0.1 )O 3 (LCN91) is screened by a combination of the ML model and the density functional theory calculation. Finally, LCN91 is applied to the air electrode of P‐SOC, and the cell exhibits excellent electrochemical performances in fuel cell and electrolysis modes. The current study not only provides a useful model for screening air electrodes of P‐SOC, but also extends the application of ML in exploring the key materials for P‐SOCs and other fuel cells/electrolyzers. Materials science ; Oxide ; Electrode ; Electrolysis ; Perovskite (structure) ; Proton ; Nanotechnology ; Chemical engineering ; Physical chemistry ; Metallurgy ; Chemistry Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion