jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jose Sena | Linus O. Johannissen | Jonny J. Blaker | Sam Hay
Source: ChemRxiv (preprint, 2024-09-23)
Field: Materials Science — Materials Chemistry

A Machine Learning Model for the Prediction of Water Contact Angles on Solid Polymers The interaction between water and solid surfaces is an active area of research, and the interaction can be generally defined as hydrophobic or hydrophilic depending on the level of wetting of the surface. This wetting level can be modified, among other methods, by applying coatings, which often modify the chemistry of the surface. With the increase in available computing power and computational algorithms, methods to develop new materials and coatings have shifted from being heavily experimental to include more theoretical approaches. In this work we use a range of experimental and computational features to develop a supervised machine learning (ML) model using the XGBoost <br/>algorithm that can predict the water contact angle (WCA) on the surface of a range of solid polymers. The mean absolute error (MAE) of the predictions is below 5.0°. Models comprised of only computational features where also explored with good results (MAE &lt; 5.0°), suggesting this approach could be used for the “bottom up” computational design of new polymers and coatings with specific <br/>water contact angles. Contact angle ; Polymer ; Solid surface ; Materials science ; Computer science ; Artificial intelligence ; Machine learning ; Composite material ; Physics ; Chemical physics High voltage insulation and dielectric phenomena ; Intravenous Infusion Technology and Safety ; Anomaly Detection Techniques and Applications
Authors: Takeo Sudo | Satoki Ishiai | Yuuki Ishiwatari | T. Yokoyama | Kenji Yasuoka | Noriyoshi Arai
Source: arXiv (Cornell University) (preprint, 2024-02-29)
Field: Materials Science — Materials Chemistry

Machine learning-enabled exploration of mesoscale architectures in amphiphilic-molecule self-assembly Amphiphilic molecules spontaneously form self-assembled structures of various shapes depending on their molecular structures, the temperature, and other physical conditions. The functionalities of these structures are dictated by their formations and their properties must be evaluated for reproduction using molecular simulations. However, the assessment of such intricate structures involves many procedural steps. This study investigates the potential of machine-learning models to extract structural features from mesoscale non-ordered self-assembled structures, and suggests a methodology in which machine-learning models for the structural analysis of self-assembled structures are trained on particle types and coordinate data. In the proposed approach, graph neural networks are utilised to extract local structural data for analysis. In simulations using several hundred self-assembled structures of up to 4050 coarse-grained particles, local structures are successfully extracted and classified with up to 78.35 % accuracy. As the machine-learning models learn structural characteristics without the need for human-made feature engineering, the proposed method has important potential applications in the field of materials science. Mesoscale meteorology ; Amphiphile ; Nanotechnology ; Amphiphilic molecule ; Self-assembly ; Molecule ; Computer science ; Materials science ; Chemistry ; Physics ; Polymer ; Copolymer ; Meteorology ; Organic chemistry Machine Learning in Materials Science
Authors: Ali Davariashtiyani | Busheng Wang | Samad Hajinazar | Eva Zurek | Sara Kadkhodaei
Source: arXiv (Cornell University) (preprint, 2024-06-25)
Field: Materials Science — Materials Chemistry

Impact of Data Bias on Machine Learning for Crystal Compound Synthesizability Predictions Machine learning models are susceptible to being misled by biases in training data that emphasize incidental correlations over the intended learning task. In this study, we demonstrate the impact of data bias on the performance of a machine learning model designed to predict the synthesizability likelihood of crystal compounds. The model performs a binary classification on labeled crystal samples. Despite using the same architecture for the machine learning model, we showcase how the model's learning and prediction behavior differs once trained on distinct data. We use two data sets for illustration: a mixed-source data set that integrates experimental and computational crystal samples and a single-source data set consisting of data exclusively from one computational database. We present simple procedures to detect data bias and to evaluate its effect on the model's performance and generalization. This study reveals how inconsistent, unbalanced data can propagate bias, undermining real-world applicability even for advanced machine learning techniques. Artificial intelligence ; Computer science ; Machine learning ; Materials science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallization and Solubility Studies
Authors: S. Rasaiya | Jeya Jeevahan
Source:  (article, 2024-12-12)
Field: Materials Science — Materials Chemistry

Machine Learning Driven Alloy Property Analysis for Optimized Bio Medical Applications Within the context of the present era, biomaterials are of utmost importance. In recent decades, breakthroughs in medical and material processing fields have led to the development of numerous biomaterials with features that suits to wide variety of applications. The Biomaterials are mostly utilized in orthopaedic treatment, Odontology, delivering drugs, tissue engineering, and cardiac devices. A range of implant materials has been utilized throughout different fields of medical sciences that is based on the specific needs of each application. The Titanium based alloy combinations pulls many of the researcher's attention mainly because of its bio compatible properties that suits for various bio implantation applications. Hence, in this research paper, the Ti-Al-Nb-Mo alloy is considered and their properties are analysed using the machine learning based classifier and optimization can be done with the well-known efficient Particle-Swarm Optimization (PSO) algorithm. The suggested alloy is subjected to analyse the mechanical properties using the X-ray diffraction analysis (XRD) and Transmission Electron Microscopy (TEM) tests and then the model is trained with the Random Forest classifier that analyses the physical properties such as young's modulus and tensile strength of the proposed alloy with various ratios and then their features are optimized for better accuracy with proposed PSO. The results demonstrates that the proposed machine learning model with the integration of RF and PSO shows the better prediction accuracy of alloy properties that in turn reduces the complicated experimental testing and finds the suitable ratio of proposed alloy for the specific bio implant applications. Property (philosophy) ; Computer science ; Alloy ; Artificial intelligence ; Materials science ; Metallurgy Titanium Alloys Microstructure and Properties ; Orthopaedic implants and arthroplasty ; Machine Learning in Materials Science
Authors: Fei Shuang | Yucheng Ji | Zixiong Wei | Chaofang Dong | Wei Gao | Luca Laurenti | Poulumi Dey
Source: arXiv (Cornell University) (preprint, 2024-09-22)
Field: Materials Science — Materials Chemistry

Decoding the hidden dynamics of super-Arrhenius hydrogen diffusion in multi-principal element alloys via machine learning Understanding atomic hydrogen (H) diffusion in multi-principal element alloys (MPEAs) is essential for advancing clean energy technologies such as H transport, storage, and nuclear fusion applications. However, the vast compositional space and the intricate chemical environments inherent in MPEAs pose significant obstacles. In this work, we address this challenge by developing a multifaceted machine learning framework that integrates machine-learning force field, neural network-driven kinetic Monte Carlo, and machine-learning symbolic regression. This framework allows for accurate investigation of H diffusion across the entire compositional space of body-centered cubic (BCC) refractory MoNbTaW alloys, achieving density functional theory accuracy. For the first time, we discover that H diffusion in MPEAs exhibits super-Arrhenius behavior, described by the Vogel-Fulcher-Tammann model, where the Vogel temperature correlates with the 5th percentile of H solution energy spectrum. We also derive robust analytical expressions that can be used to predict H diffusivity in general BCC MPEAs. Our findings further elucidate that chemical short-range order (SRO) generally does not impact H diffusion, except it enhances diffusion when "H-favoring" elements (notably Nb and Ta) are present in low concentrations. These findings not only enhance our understanding of H diffusion dynamics in general MPEAs but also guide the development of advanced MPEAs in H-related applications by manipulating element type, composition and SRO. Decoding methods ; Principal (computer security) ; Diffusion ; Arrhenius equation ; Element (criminal law) ; Hydrogen ; Dynamics (music) ; Materials science ; Computer science ; Artificial intelligence ; Statistical physics ; Chemistry ; Thermodynamics ; Physics ; Algorithm ; Physical chemistry ; Activation energy ; Acoustics ; Quantum mechanics ; Political science Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Nuclear Physics and Applications
Authors: Hao Sun | Yihan Pan | Hui Dong | Canfeng Liu | Jintian Yang | Yihui Tao | Yuan Jia
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Generalized Predictive Analysis of Reactions in Paper Devices Via Graph Neural Networks Computer science ; Graph ; Artificial neural network ; Artificial intelligence ; Theoretical computer science Machine Learning in Materials Science ; Brain Tumor Detection and Classification ; Text and Document Classification Technologies
Authors: Yuqi Song | Rongzhi Dong | Wei Lai | Qin Li | Jianjun Hu
Source: arXiv (Cornell University) (preprint, 2024-04-07)
Field: Materials Science — Materials Chemistry

AlphaCrystal-II: Distance matrix based crystal structure prediction using deep learning Computational prediction of stable crystal structures has a profound impact on the large-scale discovery of novel functional materials. However, predicting the crystal structure solely from a material's composition or formula is a promising yet challenging task, as traditional ab initio crystal structure prediction (CSP) methods rely on time-consuming global searches and first-principles free energy calculations. Inspired by the recent success of deep learning approaches in protein structure prediction, which utilize pairwise amino acid interactions to describe 3D structures, we present AlphaCrystal-II, a novel knowledge-based solution that exploits the abundant inter-atomic interaction patterns found in existing known crystal structures. AlphaCrystal-II predicts the atomic distance matrix of a target crystal material and employs this matrix to reconstruct its 3D crystal structure. By leveraging the wealth of inter-atomic relationships of known crystal structures, our approach demonstrates remarkable effectiveness and reliability in structure prediction through comprehensive experiments. This work highlights the potential of data-driven methods in accelerating the discovery and design of new materials with tailored properties. Matrix (chemical analysis) ; Artificial intelligence ; Crystal structure prediction ; Crystal (programming language) ; Computer science ; Materials science ; Crystal structure ; Crystallography ; Chemistry ; Composite material X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Crystallization and Solubility Studies
Authors: Shengxi Huang
Source:  (article, 2024-03-13)
Field: Materials Science — Materials Chemistry

Bioimaging enhanced by 2D materials and interpretable machine learning Bioimaging techniques with molecular specific information will provide important insights into diagnosis, treatment, and understanding of disease pathology. To obtain molecular information, it is important to have high multiplexity and high specificity hyperspectral imaging, together with analysis techniques to interpret spectral signatures. This talk introduces pioneering discoveries and novel approaches to achieve these goals. I will introduce a new bioimaging mechanism, Raman imaging enhanced through 2D materials, an enhancement effect of molecular Raman fingerprints on the atomically-flat 2D material surfaces. It offers a new paradigm of biochemical sensing with high specificity, high multiplexity, and low noise. The selection rule for the 2D material substrates has been revealed, which is critical for device design. Hyperspectral imaging examples for brain tissues with Alzheimer’s disease will be discussed, where interpretable machine learning was further applied for new knowledge discovery. The works presented offer important guidelines to design high-performance biosensing and imaging devices, and are significant in fundamental material science and quantum science. Computer science ; Artificial intelligence ; Machine learning Machine Learning in Materials Science
Authors: Martín A. Achondo | Jehanzeb H. Chaudhry | Christopher D. Cooper
Source: arXiv (Cornell University) (preprint, 2024-09-30)
Field: Materials Science — Materials Chemistry

An Investigation of Physics Informed Neural Networks to solve the Poisson-Boltzmann Equation in Molecular Electrostatics Physics-informed neural networks (PINN) is a machine learning (ML)-based method to solve partial differential equations that has gained great popularity due to the fast development of ML libraries in the last few years. The Poisson-Boltzmann equation (PBE) is widely used to model mean-field electrostatics in molecular systems, and in this work we present a detailed investigation of the use of PINN to solve the PBE. Starting from a multidomain PINN for the PBE with an interface, we assess the importance of incorporating different features into the neural network architecture. Our findings indicate that the most accurate architecture utilizes input and output scaling layers, a random Fourier features layer, trainable activation functions, and a loss balancing algorithm. The accuracy of our implementation is of the order of 10$^{-2}$ -- $10^{-3}$, which is similar to previous work using PINN to solve other differential equations. We also explore the possibility of incorporating experimental information into the model, and discuss challenges and future work, especially regarding the nonlinear PBE. Along with this manuscript, we are providing an open-source implementation to easily perform computations from a PDB file. We hope this work will motivate application scientists into using PINN to study molecular electrostatics, as ML technology continues to evolve at a high pace. Poisson–Boltzmann equation ; Electrostatics ; Statistical physics ; Artificial neural network ; Physics ; Poisson distribution ; Poisson's equation ; Computer science ; Classical mechanics ; Theoretical physics ; Mathematics ; Artificial intelligence ; Quantum mechanics ; Statistics Machine Learning in Materials Science ; Electrostatics and Colloid Interactions ; Neural Networks and Applications
Authors: Wenchao Wu | Hao Xu | Dongxiao Zhang | Fanyang Mo
Source: arXiv (Cornell University) (preprint, 2024-04-14)
Field: Chemistry — Spectroscopy

Intelligent Chemical Purification Technique Based on Machine Learning We present an innovative of artificial intelligence with column chromatography, aiming to resolve inefficiencies and standardize data collection in chemical separation and purification domain. By developing an automated platform for precise data acquisition and employing advanced machine learning algorithms, we constructed predictive models to forecast key separation parameters, thereby enhancing the efficiency and quality of chromatographic processes. The application of transfer learning allows the model to adapt across various column specifications, broadening its utility. A novel metric, separation probability ($S_p$), quantifies the likelihood of effective compound separation, validated through experimental verification. This study signifies a significant step forward int the application of AI in chemical research, offering a scalable solution to traditional chromatography challenges and providing a foundation for future technological advancements in chemical analysis and purification. Scalability ; Computer science ; Metric (unit) ; Artificial intelligence ; Machine learning ; Column (typography) ; Separation (statistics) ; Engineering ; Database Analytical Chemistry and Chromatography
Authors: Tao Fan | Artem R. Oganov
Source: arXiv (Cornell University) (preprint, 2024-05-04)
Field: Materials Science — Materials Chemistry

Combining Machine Learning Models with First-Principles High-Throughput Calculation to Accelerate the Search of Promising Thermoelectric Materials Thermoelectric materials can achieve direct energy conversion between electricity and heat, thus can be applied to waste heat harvesting and solid-state cooling. The discovery of new thermoelectric materials is mainly based on experiments and first-principles calculations. However, these methods are usually expensive and time-consuming. Recently, the prediction of properties via machine learning has emerged as a popular method in materials science. Herein, we firstly did first-principles high-throughput calculations for a large number of chalcogenides and built a thermoelectric database containing 796 compounds. Many novel and promising thermoelectric materials were discovered. Then, we trained four ensemble learning models and two deep learning models to distinguish the promising thermoelectric materials from the others for n type and p type doping, respectively. All the presented models achieve classification accuracy higher than 85% and area under the curve (AUC) higher than 0.9. Especially, the M3GNet model for n type data achieve accuracy, precision and recall all higher than 90%. Our works demonstrate a very efficient way of combining machine learning prediction and first-principles high-throughput calculations together to accelerate the discovery of advanced thermoelectric materials. Throughput ; Thermoelectric effect ; Computer science ; Materials science ; Artificial intelligence ; Machine learning ; Physics ; Telecommunications Machine Learning in Materials Science
Authors: Siya Zhu | Raymundo Arróyave | ‪Doğuhan Sarıtürk
Source: arXiv (Cornell University) (preprint, 2024-11-22)
Field: Materials Science — Materials Chemistry

Accelerating CALPHAD-based Phase Diagram Predictions in Complex Alloys Using Universal Machine Learning Potentials: Opportunities and Challenges Accurate phase diagram prediction is crucial for understanding alloy thermodynamics and advancing materials design. While traditional CALPHAD methods are robust, they are resource-intensive and limited by experimentally assessed data. This work explores the use of machine learning interatomic potentials (MLIPs) such as M3GNet, CHGNet, MACE, SevenNet, and ORB to significantly accelerate phase diagram calculations by using the Alloy Theoretic Automated Toolkit (ATAT) to map calculations of the energies and free energies of atomistic systems to CALPHAD-compatible thermodynamic descriptions. Using case studies including Cr-Mo, Cu-Au, and Pt-W, we demonstrate that MLIPs, particularly ORB, achieve computational speedups exceeding three orders of magnitude compared to DFT while maintaining phase stability predictions within acceptable accuracy. Extending this approach to liquid phases and ternary systems like Cr-Mo-V highlights its versatility for high-entropy alloys and complex chemical spaces. This work demonstrates that MLIPs, integrated with tools like ATAT within a CALPHAD framework, provide an efficient and accurate framework for high-throughput thermodynamic modeling, enabling rapid exploration of novel alloy systems. While many challenges remain to be addressed, the accuracy of some of these MLIPs (ORB in particular) are on the verge of paving the way toward high-throughput generation of CALPHAD thermodynamic descriptions of multi-component, multi-phase alloy systems. CALPHAD ; Phase diagram ; Phase (matter) ; Diagram ; Computer science ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Microstructure and Mechanical Properties of Steels
Authors: Francesca Venturini Francesca | Umberto Michelucci
Source: Research Square (preprint, 2024-07-16)
Field: Chemistry — Analytical Chemistry

Deep Learning Domain Adaptation to Understand Physico-Chemical Processes from Fluorescence Spectroscopy Small Datasets: Application to Ageing of Olive Oil Domain adaptation ; Adaptation (eye) ; Olive oil ; Ageing ; Fluorescence spectroscopy ; Spectroscopy ; Domain (mathematical analysis) ; Fluorescence ; Computer science ; Artificial intelligence ; Environmental science ; Biological system ; Chemistry ; Biology ; Mathematics ; Physics ; Food science ; Neuroscience ; Optics Spectroscopy and Chemometric Analyses
Authors: Audrey CampBell | Ligen Wang | Qi‐Jun Hong
Source: arXiv (Cornell University) (preprint, 2024-08-24)
Field: Materials Science — Materials Chemistry

Accelerating material melting temperature predictions by implementing machine learning potentials in the SLUSCHI package The SLUSCHI (Solid and Liquid in Ultra Small Coexistence with Hovering Interfaces) automated package, with interface to the first-principles code VASP (Vienna Ab initio Simulation Package), was developed by us for efficiently determining the melting temperatures of various materials. However, performing many molecular dynamics simulations for small liquid-solid coexisting supercells to predict the melting temperature of a material is still computationally expensive, often requiring weeks and tens to hundreds of thousands of CPU hours to complete. In the present paper, we made an attempt to interface the SLUSCHI package with the highly efficient molecular dynamics LAMMPS code and demonstrated that it achieves a much faster melting temperature determination, outperforming the original VASP-based approach by at least one order of magnitude. In our melting temperature calculations, the LAMMPS simulations were performed based on the LASP (Large-scale Atomic Simulation) machine learning potentials which are pre-built using first-principles data. Besides the dramatic CPU time reduction for melting temperature predictions the calculated melting temperatures for various materials (simple and transition metals, alloys, oxides and carbide) are reasonably accurate. Analysis of the calculated results shows that 60% of the melting temperatures are within 200 K of experimental values with the RMSE value of 187 K which is slightly worse than the first-principles DFT RMSE value of 151 K. Therefore, interfacing SLUSCHI with LAMMPS molecular dynamics simulations makes it possible to quickly screen out the best candidates from numerous materials in a much more efficient way, and facilitate the rational design of materials within the framework of the materials genome paradigm. Melting temperature ; R package ; Computer science ; Materials science ; Computational science ; Composite material Machine Learning in Materials Science
Authors: Joshua Stuckner | S. Mohadeseh Taheri-Mousavi | James E. Saal
Source: AM&P Technical Articles (article, 2024-05-01)
Field: Materials Science — Materials Chemistry

Artificial Intelligence and Machine Learning in Materials Science Abstract This article provides a brief overview of the many ways that artificial intelligence and machine learning are being used for materials and manufacturing research. Several case studies show how the discovery, development, and deployment of novel materials are being dramatically accelerated through automation and data-driven models. Automation ; Software deployment ; Artificial intelligence ; Computer science ; Manufacturing engineering ; Data science ; Engineering ; Machine learning ; Software engineering ; Mechanical engineering Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques