jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kaiyang Ding | Wang Ming | Mengyuan Chen | Xiaohao Wang | Kai Ni | Qian Zhou | Benfeng Bai
Source: Nanophotonics (review, 2024-03-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Snapshot spectral imaging: from spatial‐spectral mapping to metasurface‐based imaging Snapshot spectral imaging technology enables the capture of complete spectral information of objects in an extremely short period of time, offering wide-ranging applications in fields requiring dynamic observations such as environmental monitoring, medical diagnostics, and industrial inspection. In the past decades, snapshot spectral imaging has made remarkable breakthroughs with the emergence of new computational theories and optical components. From the early days of using various spatial-spectral data mapping methods, they have evolved to later attempts to encode various dimensions of light, such as amplitude, phase, and wavelength, and then computationally reconstruct them. This review focuses on a systematic presentation of the system architecture and mathematical modeling of these snapshot spectral imaging techniques. In addition, the introduction of metasurfaces expands the modulation of spatial-spectral data and brings advantages such as system size reduction, which has become a research hotspot in recent years and is regarded as the key to the next-generation snapshot spectral imaging techniques. This paper provides a systematic overview of the applications of metasurfaces in snapshot spectral imaging and provides an outlook on future directions and research priorities. Snapshot (computer storage) ; Spectral imaging ; Computer science ; Hyperspectral imaging ; Hotspot (geology) ; Optics ; Artificial intelligence ; Physics Metamaterials and Metasurfaces Applications ; Random lasers and scattering media ; Optical Polarization and Ellipsometry
Authors: Bin Xing | Timothy J. Rupert | Xiaoqing Pan | Penghui Cao
Source: Nature Communications (article, 2024-05-09)
Field: Materials Science — Materials Chemistry

Neural network kinetics for exploring diffusion multiplicity and chemical ordering in compositionally complex materials Diffusion involving atom transport from one location to another governs many important processes and behaviors such as precipitation and phase nucleation. The inherent chemical complexity in compositionally complex materials poses challenges for modeling atomic diffusion and the resulting formation of chemically ordered structures. Here, we introduce a neural network kinetics (NNK) scheme that predicts and simulates diffusion-induced chemical and structural evolution in complex concentrated chemical environments. The framework is grounded on efficient on-lattice structure and chemistry representation combined with artificial neural networks, enabling precise prediction of all path-dependent migration barriers and individual atom jumps. To demonstrate the method, we study the temperature-dependent local chemical ordering in a refractory NbMoTa alloy and reveal a critical temperature at which the B2 order reaches a maximum. The atomic jump randomness map exhibits the highest diffusion heterogeneity (multiplicity) in the vicinity of this characteristic temperature, which is closely related to chemical ordering and B2 structure formation. The scalable NNK framework provides a promising new avenue to exploring diffusion-related properties in the vast compositional space within which extraordinary properties are hidden. Nucleation ; Randomness ; Diffusion ; Statistical physics ; Chemical physics ; Multiplicity (mathematics) ; Materials science ; Computer science ; Biological system ; Chemistry ; Thermodynamics ; Physics ; Mathematics Machine Learning in Materials Science ; Nuclear Materials and Properties ; Advanced Materials Characterization Techniques
Authors: Joshua A. Mysona | Paul F. Nealey | Juan Pablo
Source: Macromolecules (article, 2024-02-26)
Field: Materials Science — Materials Chemistry

Machine Learning Models and Dimensionality Reduction for Prediction of Polymer Properties Accurate prediction of block polymer properties as a function of monomer sequence is necessary for better material development. The number of permutations of chemistry and sequence is nearly infinite, and new methods are needed to predict and engineer properties as a function of molecular structure. In this work, we present a machine learning approach to determine polymer properties where a feed-forward neural network is trained to predict the period length of a diblock lamellar system as a function of block sequence and interaction parameters. These sequenced polymers are similar to experimentally explored polypeptoid systems. Additionally, we report on our efforts to explore dimensionality reduction as a method for gaining physical insights into these polymeric materials. Sequence (biology) ; Polymer ; Dimensionality reduction ; Block (permutation group theory) ; Artificial neural network ; Curse of dimensionality ; Function (biology) ; Lamellar structure ; Monomer ; Computer science ; Reduction (mathematics) ; Materials science ; Biological system ; Artificial intelligence ; Chemistry ; Mathematics Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Advanced Polymer Synthesis and Characterization
Authors: Junwu Chen | Philippe Schwaller
Source: The Journal of Chemical Physics (article, 2024-04-10)
Field: Materials Science — Materials Chemistry

Molecular hypergraph neural networks Graph neural networks (GNNs) have demonstrated promising performance across various chemistry-related tasks. However, conventional graphs only model the pairwise connectivity in molecules, failing to adequately represent higher order connections, such as multi-center bonds and conjugated structures. To tackle this challenge, we introduce molecular hypergraphs and propose Molecular Hypergraph Neural Networks (MHNNs) to predict the optoelectronic properties of organic semiconductors, where hyperedges represent conjugated structures. A general algorithm is designed for irregular high-order connections, which can efficiently operate on molecular hypergraphs with hyperedges of various orders. The results show that MHNN outperforms all baseline models on most tasks of organic photovoltaic, OCELOT chromophore v1, and PCQM4Mv2 datasets. Notably, MHNN achieves this without any 3D geometric information, surpassing the baseline model that utilizes atom positions. Moreover, MHNN achieves better performance than pretrained GNNs under limited training data, underscoring its excellent data efficiency. This work provides a new strategy for more general molecular representations and property prediction tasks related to high-order connections. Hypergraph ; Pairwise comparison ; Computer science ; Artificial neural network ; Conjugated system ; Chromophore ; Baseline (sea) ; Theoretical computer science ; Molecular graph ; Graph ; Artificial intelligence ; Materials science ; Chemistry ; Mathematics ; Polymer ; Combinatorics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Molecular Junctions and Nanostructures
Authors: Angus B. Keto | Taicheng Guo | Morgan Underdue | Thijs Stuyver | Connor W. Coley | Xiangliang Zhang | Elizabeth H. Krenske | Olaf Wiest
Source: Journal of the American Chemical Society (article, 2024-06-01)
Field: Materials Science — Materials Chemistry

Data-Efficient, Chemistry-Aware Machine Learning Predictions of Diels–Alder Reaction Outcomes The application of machine learning models to the prediction of reaction outcomes currently needs large and/or highly featurized data sets. We show that a chemistry-aware model, NERF, which mimics the bonding changes that occur during reactions, allows for highly accurate predictions of the outcomes of Diels-Alder reactions using a relatively small training set, with no pretraining and no additional features. We establish a diverse data set of 9537 intramolecular, hetero-, aromatic, and inverse electron demand Diels-Alder reactions. This data set is used to train a NERF model, and the performance is compared against state-of-the-art classification and generative machine learning models across low- and high-data regimes, with and without pretraining. The predictive accuracy (regio- and site selectivity in the major product) achieved by NERF exceeds 90% when as little as 40% of the data set is used for training. Another high-performing model, Chemformer, requires a larger training data set (>45%) and pretraining to reach 90% Top-1 accuracy. Accurate predictions of less-represented reaction subclasses, such as those involving heteroatomic or aromatic substrates, require higher percentages of training data. We also show how NERF can use small amounts of additional training data to quickly learn new systems and improve its overall understanding of reactivity. Synthetic chemists stand to benefit as this model can be rapidly expanded and tailored to areas of chemistry corresponding to the low-data regime. Chemistry ; Set (abstract data type) ; Training set ; Intramolecular force ; Reactivity (psychology) ; Data set ; Machine learning ; Artificial intelligence ; Diels–Alder reaction ; Computer science ; Biological system ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: U. S. Vaitesswar | Daniil Bash | Tan Huang | Jose Recatala‐Gomez | Tianqi Deng | Shuo‐Wang Yang | Xiaonan Wang | Kedar Hippalgaonkar
Source: Digital Discovery (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Machine learning based feature engineering for thermoelectric materials by design We train several machine learning models on a dataset comprised by Materials Project and calculated thermoelectric power factor. We show that a random forest model outperforms more complex approaches for the dataset and allows for interpretability. Interpretability ; Random forest ; Feature (linguistics) ; Computer science ; Machine learning ; Feature engineering ; Artificial intelligence ; Thermoelectric effect ; Power (physics) ; Data mining ; Deep learning Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Thermography and Photoacoustic Techniques
Authors: Xinyuan Cao | Jisi Huang | Kexin Du | Yawen Tian | Zhixin Hu | Zhu Luo | Jinlong Wang | Yanbing Guo
Source: Environmental Science & Technology (article, 2024-05-01)
Field: Materials Science — Materials Chemistry

Machine-Learning-Assisted Descriptors Identification for Indoor Formaldehyde Oxidation Catalysts The development of highly efficient catalysts for formaldehyde (HCHO) oxidation is of significant interest for the improvement of indoor air quality. Up to 400 works relating to the catalytic oxidation of HCHO have been published to date; however, their analysis for collective inference through conventional literature search is still a challenging task. A machine learning (ML) framework was presented to predict catalyst performance from experimental descriptors based on an HCHO oxidation catalysts database. MnO<sub><i>x</i></sub>, CeO<sub>2</sub>, Co<sub>3</sub>O<sub>4</sub>, TiO<sub>2</sub>, FeO<sub><i>x</i></sub>, ZrO<sub>2</sub>, Al<sub>2</sub>O<sub>3</sub>, SiO<sub>2</sub>, and carbon-based catalysts with different promoters were compiled from the literature. Notably, 20 descriptors including reaction catalyst composition, reaction conditions, and catalyst physical properties were collected for data mining (2263 data points). Furthermore, the eXtreme Gradient Boosting algorithm was employed, which successfully predicted the conversion efficiency of HCHO with an R-square value of 0.81. Shapley additive analysis suggested Pt/MnO<sub>2</sub> and Ag/Ce-Co<sub>3</sub>O<sub>4</sub> exhibited excellent catalytic performance of HCHO oxidation based on the analysis of the entire database. Validated by experimental tests and theoretical simulations, the key descriptor identified by ML, i.e., the first promoter, was further described as metal-support interactions. This study highlights ML as a useful tool for database establishment and the catalyst rational design strategy based on the importance of analysis between experimental descriptors and the performance of complex catalytic systems. Catalysis ; Formaldehyde ; Computer science ; Catalytic oxidation ; Chemistry ; Inference ; Database ; Artificial intelligence ; Organic chemistry Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Jin‐Soo Kim | Juhwan Noh | Jino Im
Source: npj Computational Materials (article, 2024-05-08)
Field: Materials Science — Materials Chemistry

Machine learning-enabled chemical space exploration of all-inorganic perovskites for photovoltaics Abstract The vast compositional and configurational spaces of multi-element metal halide perovskites (MHPs) result in significant challenges when designing MHPs with promising stability and optoelectronic properties. In this paper, we propose a framework for the design of B-site-alloyed ABX 3 MHPs by combining density functional theory (DFT) and machine learning (ML). We performed generalized gradient approximation with Perdew–Burke–Ernzerhof functional for solids (PBEsol) on 3,159 B-site-alloyed perovskite structures using a compositional step of 1/4. Crystal graph convolution neural networks (CGCNNs) were trained on the 3159 DFT datasets to predict the decomposition energy, bandgap, and types of bandgaps. The trained CGCNN models were used to explore the compositional and configurational spaces of 41,400 B-site-alloyed ABX 3 MHPs with a compositional step of 1/16, by accessing all possible configurations for each composition. The electronic band structures of the selected compounds were calculated using the hybrid functional (PBE0). Then, we calculated the optical absorption spectra and spectroscopic limited maximum efficiency of the selected compounds. Based on the DFT/ML-combined screening, 10 promising compounds with optimal bandgaps were selected, and from among these 10 compounds, CsGe 0.3125 Sn 0.6875 I 3 and CsGe 0.0625 Pb 0.3125 Sn 0.625 Br 3 were suggested as photon absorbers for single-junction and tandem solar cells, respectively. The design framework presented herein is a good starting point for the design of mixed MHPs for optoelectronic applications. Photovoltaics ; Chemical space ; Space (punctuation) ; Materials science ; Astrobiology ; Nanotechnology ; Perovskite (structure) ; Engineering physics ; Computer science ; Photovoltaic system ; Chemistry ; Chemical engineering ; Engineering ; Physics ; Electrical engineering Machine Learning in Materials Science ; Perovskite Materials and Applications ; Quantum Dots Synthesis And Properties
Authors: Marco Bertani | Thibault Charpentier | Francesco Faglioni | Alfonso Pedone
Source: Journal of Chemical Theory and Computation (article, 2024-01-13)
Field: Materials Science — Ceramics and Composites

Accurate and Transferable Machine Learning Potential for Molecular Dynamics Simulation of Sodium Silicate Glasses An accurate and transferable machine learning (ML) potential for the simulation of binary sodium silicate glasses over a wide range of compositions (from 0 to 50% Na<sub>2</sub>O) was developed. The potential energy surface is approximated by the sum of atomic energy contributions mapped by a neural network algorithm from the local geometry comprising information on atomic distances and angles with neighboring atoms using the DeePMD code [Wang, H. <i>Comput. Phys. Commun.</i> 2018, 228, 178-184]. Our model was trained on a large data set of total energies and atomic forces computed at the density functional theory level on structures extracted from classical molecular dynamics (MD) simulations performed at several temperatures from 300 to 3000 K. This allows for the generation of a robust and transferable ML potential applicable over the full compositional range of glass formability at different temperatures that outperforms the empirical potentials available in the literature in reproducing structures and properties such as bond angle distribution, total distribution functions, and vibrational density of state. The generality of the approach enables the future training of a potential with other or more elements allowing for simulations of structures, properties, and behavior of ternary and multicomponent oxide glasses with nearly ab initio accuracy at a fraction of the computational cost. Molecular dynamics ; Ternary operation ; Statistical physics ; Sodium silicate ; Range (aeronautics) ; Potential energy ; Ab initio ; Density functional theory ; Energy (signal processing) ; Pair potential ; Binary number ; Computer science ; Materials science ; Thermodynamics ; Algorithm ; Chemistry ; Physics ; Computational chemistry ; Mathematics ; Atomic physics ; Quantum mechanics Glass properties and applications ; Phase-change materials and chalcogenides ; Luminescence Properties of Advanced Materials
Authors: Hai Xin | Akashdeep Singh Virk | Sabitoj Singh Virk | Foluso Akin-Ige | Samiul Amin
Source: Current Opinion in Colloid & Interface Science (article, 2024-08-03)
Field: Chemistry — Analytical Chemistry

Applications of artificial intelligence and machine learning on critical materials used in cosmetics and personal care formulation design Cosmetics ; Self-healing hydrogels ; Personal care ; Biochemical engineering ; Computer science ; New product development ; Engineering ; Business ; Medicine ; Marketing Spectroscopy and Chemometric Analyses ; Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies
Authors: Xiao Luo | Yue Li | Qiuao Wang | Jinlei Mu | Yunze Liu
Source: Journal of Cleaner Production (article, 2024-02-01)
Field: Materials Science — Materials Chemistry

Machine learning based modeling for predicting the compressive strength of solid waste material-incorporated Magnesium Phosphate Cement Compressive strength ; Magnesium phosphate ; Materials science ; Cement ; Curing (chemistry) ; Magnesium ; Universal testing machine ; Municipal solid waste ; Composite material ; Ultimate tensile strength ; Metallurgy ; Waste management ; Engineering Magnesium Oxide Properties and Applications
Authors: Hongtao Zhang | Huadong Fu | Weidong Li | Lei Jiang | Yong Wei | Jingtai Sun | Long‐Qing Chen | Jianxin Xie
Source: Advanced Materials (article, 2024-10-14)
Field: Materials Science — Materials Chemistry

Empowering the Sustainable Development of High‐End Alloys via Interpretive Machine Learning The extensive use of scarce, expensive, and toxic elements in high-performance metal alloys restricts their sustainable development. Here we propose a novel alternative alloying-element design strategy that combines physicochemical-factor screening, a "black-box" interpretative method based on SHapley Additive exPlanation analysis, and sensitivity analyses of elemental influence. A "white-box" model of alloy compositions and properties is therefore established that enables the rational selection of abundant elements and the efficient designs of alloys with substitution for scarce alloying elements. The success of this design strategy is demonstrated by reducing the Co content in the C70350 alloy series (e.g., Cu-1.3Ni-1.4Co-0.56Si-0.03Mg (wt.%)). Indeed, Cu-1.95Ni-0.5Co-0.6Si-0.2Mg-0.1Cr (wt.%) is obtained as an ultra-low-Co-containing alloy by substituting only a trace amount of Cr for Co in the Cu-Ni-Co-Si-Mg system, followed by compositional optimization. Although the Co content is reduced by 64% (i.e., from 1.4 to 0.5 wt.%), the properties of the alloy (ultimate tensile strength and electrical conductivity of 850 MPa and 47.2%IACS, respectively) are comparable to those of the C70350 alloy. This study contributes to the sustainable and green development of metallic materials by providing a new avenue for substituting abundant elements for scarce and undesired elements in metal alloys. Alloy ; Materials science ; Ultimate tensile strength ; Metal ; Metallurgy ; Chemical engineering Machine Learning in Materials Science ; Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Sreenivas Raguraman | Maitreyee Sharma Priyadarshini | Tram Nguyen | Ryan McGovern | Andrew Kim | Adam J. Griebel | Paulette Clancy | Timothy P. Weihs
Source: Journal of Magnesium and Alloys (preprint, 2024-06-01)
Field: Materials Science — Biomaterials

Machine learning-guided accelerated discovery of structure-property correlations in lean magnesium alloys for biomedical applications Magnesium alloys are emerging as promising alternatives to traditional orthopedic implant materials thanks to their biodegradability, biocompatibility, and impressive mechanical characteristics. However, their rapid in-vivo degradation presents challenges, notably in upholding mechanical integrity over time. This study investigates the impact of high-temperature thermal processing on the mechanical and degradation attributes of a lean Mg-Zn-Ca-Mn alloy, ZX10. Utilizing rapid, cost-efficient characterization methods like X-ray diffraction and optical microscopy, we swiftly examine microstructural changes post-thermal treatment. Employing Pearson correlation coefficient analysis, we unveil the relationship between microstructural properties and critical targets (properties): hardness and corrosion resistance. Additionally, leveraging the least absolute shrinkage and selection operator (LASSO), we pinpoint the dominant microstructural factors among closely correlated variables. Our findings underscore the significant role of grain size refinement in strengthening and the predominance of the ternary Ca2Mg6Zn3 phase in corrosion behavior. This suggests that achieving an optimal blend of strength and corrosion resistance is attainable through fine grains and reduced concentration of ternary phases. This thorough investigation furnishes valuable insights into the intricate interplay of processing, structure, and properties in magnesium alloys, thereby advancing the development of superior biodegradable implant materials. Materials science ; Corrosion ; Biocompatibility ; Magnesium ; Microstructure ; Alloy ; Ternary operation ; Metallurgy ; Grain size ; Magnesium alloy ; Computer science Magnesium Alloys: Properties and Applications ; Metal and Thin Film Mechanics ; Aluminum Alloys Composites Properties
Authors: Tristan Maxson | Ademola Soyemi | Benjamin W. J. Chen | Tibor Szilvási
Source: The Journal of Physical Chemistry C (article, 2024-03-20)
Field: Materials Science — Materials Chemistry

Enhancing the Quality and Reliability of Machine Learning Interatomic Potentials through Better Reporting Practices Recent developments in machine learning interatomic potentials (MLIPs) have empowered even nonexperts in machine learning to train MLIPs for accelerating materials simulations. However, reproducibility and independent evaluation of presented MLIP results is hindered by a lack of clear standards in current literature. In this Perspective, we aim to provide guidance on best practices for documenting MLIP use while walking the reader through the development and deployment of MLIPs including hardware and software requirements, generating training data, training models, validating predictions, and MLIP inference. We also suggest useful plotting practices and analyses to validate and boost confidence in the deployed models. Finally, we provide a step-by-step checklist for practitioners to use directly before publication to standardize the information to be reported. Altogether, we hope that our work will encourage the reliable and reproducible use of these MLIPs, which will accelerate their ability to make a positive impact in various disciplines including materials science, chemistry, and biology, among others. Computer science ; Inference ; Reliability (semiconductor) ; Checklist ; Software deployment ; Perspective (graphical) ; Quality (philosophy) ; Machine learning ; Software ; Data science ; Work (physics) ; Artificial intelligence ; Software engineering ; Psychology ; Engineering ; Cognitive psychology Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Electron and X-Ray Spectroscopy Techniques