jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Maicon Pierre Lourenço | Jiří Hostaš | Colin Bellinger | Alain Tchagang | Dennis R. Salahub
Source: Journal of Computational Chemistry (article, 2024-02-15)
Field: Materials Science — Materials Chemistry

Reinforcement learning for in silico determination of adsorbate—substrate structures Reinforcement learning (RL) methods have helped to define the state of the art in the field of modern artificial intelligence, mostly after the breakthrough involving AlphaGo and the discovery of novel algorithms. In this work, we present a RL method, based on Q-learning, for the structural determination of adsorbate@substrate models in silico, where the minimization of the energy landscape resulting from adsorbate interactions with a substrate is made by actions on states (translations and rotations) chosen from an agent's policy. The proposed RL method is implemented in an early version of the reinforcement learning software for materials design and discovery (RLMaterial), developed in Python3.x. RLMaterial interfaces with deMon2k, DFTB+, ORCA, and Quantum Espresso codes to compute the adsorbate@substrate energies. The RL method was applied for the structural determination of (i) the amino acid glycine and (ii) 2-amino-acetaldehyde, both interacting with a boron nitride (BN) monolayer, (iii) host-guest interactions between phenylboronic acid and β-cyclodextrin and (iv) ammonia on naphthalene. Density functional tight binding calculations were used to build the complex search surfaces with a reasonably low computational cost for systems (i)-(iii) and DFT for system (iv). Artificial neural network and gradient boosting regression techniques were employed to approximate the Q-matrix or Q-table for better decision making (policy) on next actions. Finally, we have developed a transfer-learning protocol within the RL framework that allows learning from one chemical system and transferring the experience to another, as well as from different DFT or DFTB levels. Reinforcement learning ; Computer science ; Chemistry ; Artificial intelligence ; Artificial neural network Machine Learning in Materials Science ; Molecular Junctions and Nanostructures ; Software Engineering Research
Authors: Tae‐Sung Ko | Taehyun Park | Minseon Kim | Kyoungmin Min
Source: Materials Today Communications (article, 2024-10-15)
Field: Materials Science — Materials Chemistry

Enhancing predictions of experimental band gap using machine learning and knowledge transfer Materials science ; Transfer of learning ; Machine learning ; Computer science Machine Learning in Materials Science ; Advancements in Semiconductor Devices and Circuit Design ; Advanced Materials Characterization Techniques
Authors: Nguyễn Thiên Phúc Từ | Siri Williamson | Erin R. Johnson | Christopher N. Rowley
Source: The Journal of Physical Chemistry B (review, 2024-08-21)
Field: Materials Science — Materials Chemistry

Modeling Intermolecular Interactions with Exchange-Hole Dipole Moment Dispersion Corrections to Neural Network Potentials Neural network potentials (NNPs) are an innovative approach for calculating the potential energy and forces of a chemical system. In principle, these methods are capable of modeling large systems with an accuracy approaching that of a high-level ab initio calculation, but with a much smaller computational cost. Due to their training to density-functional theory (DFT) data and neglect of long-range interactions, some classes of NNPs require an additional term to include London dispersion physics. In this Perspective, we discuss the requirements for a dispersion model for use with an NNP, focusing on the MLXDM (Machine Learned eXchange-Hole Dipole Moment) model developed by our groups. This model is based on the DFT-based XDM dispersion correction, which calculates interatomic dispersion coefficients in terms of atomic moments and polarizabilities, both of which can be approximated effectively using neural networks. Dipole ; Dispersion (optics) ; Moment (physics) ; Artificial neural network ; Intermolecular force ; Density functional theory ; Ab initio ; Statistical physics ; Physics ; Computer science ; Quantum mechanics ; Machine learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Kaja Bilińska | M. Winiarski
Source: Crystals (article, 2024-04-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning-Based Predictions of Power Factor for Half-Heusler Phases A support vector regression model for predictions of the thermoelectric power factor of half-Heusler phases was implemented based on elemental features of ions. The training subset was composed of 53 hH phases with 18 valence electrons. The target values were calculated within the density functional theory and Boltzmann equation. The best predictors out of over 2000 combinations regarded for the p-type power factor at room temperature are: electronegativity, the first ionization energy, and the valence electron count of constituent ions. The final results of support vector regression for 70 hH phases are compared with data available in the literature, revealing good ability to determine favorable thermoelectric materials, i.e., VRhGe, TaRhGe, VRuSb, NbRuAs, NbRuBi, LuNiAs, LuNiBi, TaFeBi, YNiAs, YNiBi, TaRuSb and NbFeSb. The results and discussion presented in this work should encourage further fusion of ab initio investigations and machine learning support, in which the elemental features of ions may be a sufficient input for reasonable predictions of intermetallics with promising thermoelectric performance. Power factor ; Factor (programming language) ; Materials science ; Condensed matter physics ; Power (physics) ; Economics ; Computer science ; Thermodynamics ; Physics Heusler alloys: electronic and magnetic properties ; Advanced Thermoelectric Materials and Devices
Authors: Shuhui Liu | Qiao Zhao | Shaobo Han | Zhenghao Jia | Xiaoling Hong | Wei Liu
Source: Nano Today (article, 2024-09-24)
Field: Materials Science — Materials Chemistry

Dynamics of catalyst nanoparticles quantified from in situ TEM video In situ ; Nanoparticle ; Catalysis ; Materials science ; Nanotechnology ; Chemical engineering ; Engineering ; Chemistry ; Organic chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Yunfei Ren | Tao Hu | Songzhe Xu | Chaoyue Chen | Weidong Xuan | Zhongming Ren
Source: Journal of Alloys and Metallurgical Systems (article, 2024-04-21)
Field: Materials Science — Materials Chemistry

Rapid estimation of γ' solvus temperature for composition design of Ni-based superalloy via physics-informed generative artificial intelligence The exceptional high-temperature mechanical properties of Ni-based superalloys are mainly stemmed from the L12 γ' phase, therefore it is crucial to discover Ni-based superalloys with high γ' solvus temperatures. Utilizing generative artificial intelligence, we have developed a framework to swiftly evaluate the γ' solvus temperature and tailor Ni-based superalloys, accelerating the process of discovering Ni-based superalloys. Physics-informed artificial neural network emerged as the optimal choice for reverse engineering, outperforming other models with an R2 score of 0.917 and a mean absolute error of 15 K. In the reverse design process, 20000 virtual alloy samples were generated based on divide-and-conquer variational autoencoder which divides the dataset into distinct clusters by K-means algorithm provides a structured representation of the alloy composition space, thereby facilitating a more nuanced understanding of its inherent complexities. In a specific alloy design example, 563 samples were identified through screening based on criteria like γ' solvus temperature, composition deviation index, price, and density. Thermodynamic calculations were used to further screen Ni-based superalloys with exceptional high-temperature properties. The showcase of BA alloy discovery through generative artificial intelligence demonstrates the potential of our research to steer the creation of novel compositions for Ni-based superalloys with outstanding high-temperature properties. Solvus ; Superalloy ; Generative grammar ; Materials science ; Estimation ; Artificial intelligence ; Thermodynamics ; Metallurgy ; Computer science ; Physics ; Engineering ; Systems engineering ; Alloy Machine Learning in Materials Science ; High Temperature Alloys and Creep ; Advanced Materials Characterization Techniques
Authors: Mohammad Al Ktash | Mona Knoblich | Max Eberle | Frank Wackenhut | Marc Brecht
Source: Journal of Imaging (article, 2024-12-05)
Field: Chemistry — Analytical Chemistry

UV Hyperspectral Imaging with Xenon and Deuterium Light Sources: Integrating PCA and Neural Networks for Analysis of Different Raw Cotton Types Ultraviolet (UV) hyperspectral imaging shows significant promise for the classification and quality assessment of raw cotton, a key material in the textile industry. This study evaluates the efficacy of UV hyperspectral imaging (225-408 nm) using two different light sources: xenon arc (XBO) and deuterium lamps, in comparison to NIR hyperspectral imaging. The aim is to determine which light source provides better differentiation between cotton types in UV hyperspectral imaging, as each interacts differently with the materials, potentially affecting imaging quality and classification accuracy. Principal component analysis (PCA) and Quadratic Discriminant Analysis (QDA) were employed to differentiate between various cotton types and hemp plant. PCA for the XBO illumination revealed that the first three principal components (PCs) accounted for 94.8% of the total variance: PC1 (78.4%) and PC2 (11.6%) clustered the samples into four main groups-hemp (HP), recycled cotton (RcC), and organic cotton (OC) from the other cotton samples-while PC3 (6%) further separated RcC. When using the deuterium light source, the first three PCs explained 89.4% of the variance, effectively distinguishing sample types such as HP, RcC, and OC from the remaining samples, with PC3 clearly separating RcC. When combining the PCA scores with QDA, the classification accuracy reached 76.1% for the XBO light source and 85.1% for the deuterium light source. Furthermore, a deep learning technique called a fully connected neural network for classification was applied. The classification accuracy for the XBO and deuterium light sources reached 83.6% and 90.1%, respectively. The results highlight the ability of this method to differentiate conventional and organic cotton, as well as hemp, and to identify distinct types of recycled cotton, suggesting varying recycling processes and possible common origins with raw cotton. These findings underscore the potential of UV hyperspectral imaging, coupled with chemometric models, as a powerful tool for enhancing cotton classification accuracy in the textile industry. Hyperspectral imaging ; Principal component analysis ; Artificial intelligence ; Computer science ; Chemometrics ; Artificial neural network ; Pattern recognition (psychology) ; Environmental science ; Materials science ; Machine learning Spectroscopy and Chemometric Analyses ; Bee Products Chemical Analysis ; Cultural Heritage Materials Analysis
Authors: Kang N. Lee | Joshua Stuckner | Ke An | Rebekah I. Webster | Laura G. Wilson
Source: Surface and Coatings Technology (article, 2024-12-24)
Field: Materials Science — Ceramics and Composites

Influence of coating variables on the steam oxidation of modified silicon/ytterbium disilicate environmental barrier coatings Ytterbium ; Materials science ; Coating ; Thermal barrier coating ; Silicon ; Metallurgy ; Mineralogy ; Composite material ; Geology Advanced ceramic materials synthesis ; Semiconductor materials and devices ; Thermal properties of materials
Authors: Laura Shub | Wenjin Liu | Georgios Skiniotis | Michael J. Keiser | Michael J. Robertson
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-03-19)
Field: Materials Science — Materials Chemistry

Metric Ion Classification (MIC): A deep learning tool for assigning ions and waters in cryo-EM and x-ray crystallography structures Abstract At sufficiently high resolution, x-ray crystallography and cryogenic electron microscopy are capable of resolving small spherical map features corresponding to either water or ions. Correct classification of these sites provides crucial insight for understanding structure and function as well as guiding downstream design tasks, including structure-based drug discovery and de novo biomolecule design. However, direct identification of these sites from experimental data can prove extremely challenging, and existing empirical approaches leveraging the local environment can only characterize limited ion types. We present a novel representation of chemical environments using interaction fingerprints and develop a machine-learning model to predict the identity of input water and ion sites. We validate the method, named Metric Ion Classification (MIC), on a wide variety of biomolecular examples to demonstrate its utility, identifying many probable mismodeled ions deposited in the PDB. Finally, we collect all steps of this approach into an easy-to-use open-source package that can integrate with existing structure determination pipelines. Computer science ; Identification (biology) ; Ion ; Metric (unit) ; Representation (politics) ; Biomolecule ; Function (biology) ; Data mining ; Biological system ; Materials science ; Chemistry ; Nanotechnology ; Biology ; Engineering Enzyme Structure and Function ; Protein Structure and Dynamics ; RNA and protein synthesis mechanisms
Authors: Yoan Martínez‐López | Paulina Phoobane | Yanaima Jauriga | Juan A. Castillo‐Garit | Ansel Y. Rodríguez‐González | Oscar Martínez‐Santiago | Stephen J. Barigye | Julio Madera | Noel Enrique Rodríguez-Maya | Pablo R. Duchowicz
Source: Journal of Molecular Modeling (article, 2024-11-01)
Field: Chemistry — Electrochemistry

Exploring blood–brain barrier passage using atomic weighted vector and machine learning Blood–brain barrier ; Artificial intelligence ; Support vector machine ; Computer science ; Machine learning ; Neuroscience ; Chemistry ; Psychology Electrochemical Analysis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Electrostatics and Colloid Interactions
Authors: Shujia Wan | Ruiting Tong | Bing Han | Haochen Zhang
Source: Computational Materials Science (article, 2024-12-13)
Field: Materials Science — Materials Chemistry

Machine learning interatomic potential for friction study in silicon and molybdenum disulfide Molybdenum disulfide ; Interatomic potential ; Molybdenum ; Silicon ; Materials science ; Disulfide bond ; Metallurgy ; Molecular dynamics ; Chemical physics ; Crystallography ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Fuel Cells and Related Materials
Authors: Shengbin Chen | Wenming Zhang | Rui Luo | Yidong Zhao | Yang Yang | Bing Zhang | Qiang Lü | Bin Hu
Source: Journal of Molecular Structure (article, 2024-03-01)
Field: Materials Science — Materials Chemistry

System energy and band gap prediction of titanium dioxide based on machine learning Artificial neural network ; Chemistry ; Band gap ; Mean squared error ; Distortion (music) ; Ab initio ; Artificial intelligence ; Work (physics) ; Machine learning ; Electronic band structure ; Gaussian ; Titanium dioxide ; Crystal structure prediction ; Statistical physics ; Algorithm ; Crystal structure ; Computer science ; Statistics ; Computational chemistry ; Thermodynamics ; Condensed matter physics ; Physics ; Optoelectronics ; Crystallography ; Materials science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Huwen Ma | Yanchun Zhao | Mu He | Feng Li | Peter K. Liaw
Source: Transactions of the Indian Institute of Metals (article, 2024-05-13)
Field: Materials Science — Materials Chemistry

Research Progress on Metallic Heterostructure Materials Materials science ; Formability ; Deformation mechanism ; Strengthening mechanisms of materials ; Toughness ; Grain boundary ; Mechanism (biology) ; Heterojunction ; Deformation (meteorology) ; Structural material ; Composite material ; Nanotechnology ; Microstructure Microstructure and mechanical properties ; Titanium Alloys Microstructure and Properties ; Aluminum Alloys Composites Properties
Authors: Hong Sun | Sébastien Hamel | Tim Hsu | Babak Sadigh | Vincenzo Lordi | Fei Zhou
Source: Journal of Chemical Information and Modeling (article, 2024-08-06)
Field: Materials Science — Materials Chemistry

Ice Phase Classification Made Easy with Score-Based Denoising Accurate identification of ice phases is essential for understanding various physicochemical phenomena. However, such classification for structures simulated with molecular dynamics is complicated by the complex symmetries of ice polymorphs and thermal fluctuations. For this purpose, both traditional order parameters and data-driven machine learning approaches have been employed, but they often rely on expert intuition, specific geometric information, or large training data sets. In this work, we present an unsupervised phase classification framework that combines a score-based denoiser model with a subsequent model-free classification method to accurately identify ice phases. The denoiser model is trained on perturbed synthetic data of ideal reference structures, eliminating the need for large data sets and labeling efforts. The classification step utilizes the smooth overlap of atomic position (SOAP) descriptors as the atomic fingerprint, ensuring Euclidean symmetries and transferability to various structural systems. Our approach achieves a remarkable 100% accuracy in distinguishing ice phases of test trajectories using only seven ideal reference structures of ice phases as model inputs. This demonstrates the generalizability of the score-based denoiser model in facilitating phase identification for complex molecular systems. The proposed classification strategy can be broadly applied to investigate structural evolution and phase identification for a wide range of materials, offering new insights into the fundamental understanding of water and other complex systems. Computer science ; Artificial intelligence ; Machine learning ; Data mining ; Pattern recognition (psychology) ; Algorithm Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Catalysis and Oxidation Reactions
Authors: Sagar Mohan | Atanu Banerjee
Source: Shape Memory and Superelasticity (article, 2024-10-07)
Field: Materials Science — Materials Chemistry

LSTM-Based Self-Sensing Application of Shape Memory Alloy Wire Actuators Under Practical Loading Conditions Shape-memory alloy ; Actuator ; Alloy ; Materials science ; Structural engineering ; Composite material ; Computer science ; Engineering ; Artificial intelligence Shape Memory Alloy Transformations ; Topology Optimization in Engineering ; Piezoelectric Actuators and Control
Authors: Moritz Fabian | Junaid R. Rajput | Jan‐Rüdiger Schüre | Simon Weinmüller | Angelika Mennecke | Tim Alexius Möhle | Stefan Rampp | Manuel Schmidt | Arnd Dörfler | Moritz Zaiß
Source: NMR in Biomedicine (article, 2024-02-11)
Field: Materials Science — Materials Chemistry

Comprehensive 7 T CEST: A clinical MRI protocol covering multiple exchange rate regimes Chemical exchange saturation transfer (CEST) is a magnetic resonance (MR) imaging method providing molecular image contrasts based on indirect detection of low concentrated solutes. Previous CEST studies focused predominantly on the imaging of single CEST exchange regimes (e.g., slow, intermediate or fast exchanging groups). In this work, we aim to establish a so-called comprehensive CEST protocol for 7 T, covering the different exchange regimes by three saturation B1 amplitude regimes: low, intermediate and high. We used the results of previous publications and our own simulations in pulseq-CEST to produce a 7 T CEST protocol that has sensitivity to these three B1 regimes. With postprocessing optimization (simultaneous mapping of water shift and B1, B0-fitting, multiple interleaved mode saturation B1 correction, neural network employment (deepCEST) and analytical input feature reduction), we are able to shorten our initially 40 min protocol to 15 min and generate six CEST contrast maps simultaneously. With this protocol, we measured four healthy subjects and one patient with a brain tumor. We established a comprehensive CEST protocol for clinical 7 T MRI, covering three different B1 amplitude regimes. We were able to reduce the acquisition time significantly by more than 50%, while still maintaining decent image quality and contrast in healthy subjects and one patient with a tumor. Our protocol paves the way to perform comprehensive CEST studies in clinical scan times for hypothesis generation regarding molecular properties of certain pathologies, for example, ischemic stroke or high-grade brain tumours. Magnetic resonance imaging ; Protocol (science) ; Contrast (vision) ; Nuclear magnetic resonance ; Saturation (graph theory) ; Nuclear medicine ; Computer science ; Medicine ; Radiology ; Artificial intelligence ; Physics ; Pathology ; Mathematics Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; MRI in cancer diagnosis