jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Olivier Malenfant-Thuot | Kevin Ryczko | Isaac Tamblyn | Michel Côté
Source: Journal of Physics Condensed Matter (article, 2024-07-17)
Field: Materials Science — Materials Chemistry

Efficient determination of Born-effective charges, LO-TO splitting, and Raman tensors of solids with a real-space atom-centered deep learning approach We introduce a deep neural network (DNN) framework called the<b>R</b>eal-space<b>A</b>tomic<b>D</b>ecomposition<b>NET</b>work (radnet), which is capable of making accurate predictions of polarization and of electronic dielectric permittivity tensors in solids and aims to address limitations of previously available machine learning models for Raman predictions in periodic systems. This framework builds on previous, atom-centered approaches while utilizing deep convolutional neural networks. We report excellent accuracies on direct predictions for two prototypical examples: GaAs and BN. We then use automatic differentiation to efficiently calculate the Born-effective charges, longitudinal optical-transverse optical (LO-TO) splitting frequencies, and Raman tensors of these materials. We compute the Raman spectra, and find agreement with<i>ab initio</i>results. Lastly, we explore ways to generalize the predictions of polarization while taking into account periodic boundary conditions and symmetries. Raman spectroscopy ; Space (punctuation) ; Atom (system on chip) ; Physics ; Chemical physics ; Atomic physics ; Computer science ; Quantum mechanics ; Embedded system Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Marko Petković | José Manuel Vicent‐Luna | Vlado Menkovski | Sofı́a Calero
Source: ACS Applied Materials & Interfaces (article, 2024-10-02)
Field: Materials Science — Materials Chemistry

Graph Neural Networks for Carbon Dioxide Adsorption Prediction in Aluminum-Substituted Zeolites The ability to efficiently predict adsorption properties of zeolites can be of large benefit in accelerating the design process of novel materials. The existing configuration space for these materials is wide, while existing molecular simulation methods are computationally expensive. In this work, we propose a model which is 4 to 5 orders of magnitude faster at adsorption properties compared to molecular simulations. To validate the model, we generated data sets containing various aluminum configurations for the MOR, MFI, RHO and ITW zeolites along with their heat of adsorptions and Henry coefficients for CO<sub>2</sub>, obtained from Monte Carlo simulations. The predictions obtained from the Machine Learning model are in agreement with the values obtained from the Monte Carlo simulations, confirming that the model can be used for property prediction. Furthermore, we show that the model can be used for identifying adsorption sites. Finally, we evaluate the capability of our model for generating novel zeolite configurations by using it in combination with a genetic algorithm. Materials science ; Adsorption ; Carbon dioxide ; Artificial neural network ; Aluminium ; Chemical engineering ; Nanotechnology ; Organic chemistry ; Composite material ; Artificial intelligence ; Computer science Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Zeolite Catalysis and Synthesis
Authors: Shuang Lyu | Ruihuan Cheng | Haiqi Li | Yue Chen
Source: Applied Physics Letters (article, 2024-06-03)
Field: Materials Science — Materials Chemistry

Effects of local chemical ordering on the thermal transport in entropy-regulated PbSe-based thermoelectric materials Configurational entropy manipulation strategy has been proposed for designing high-performance thermoelectric materials. Understanding the phase stability is essential to regulate the thermal conductivity for optimizing the thermoelectric performance. Herein, the lattice thermal conductivity of PbSe is found to decrease from 1.87 to 0.76 Wm−1 K−1 of PbSe0.5Te0.25S0.25, which mainly results from the decreased contribution from the phonon modes in the frequency range of 0.5–2 THz. Moreover, we find local chemical ordering (LCO) in PbSe0.5Te0.25S0.25 by conducting hybrid Monte Carlo and molecular dynamics simulations based on our constructed machine-learning interatomic potential. The local chemical ordering can reduce phonon scattering with frequency in 0–2 THz, thus enhancing thermal conductivity by approximately 14%. This work unfolds the energy favorable structure with LCO in entropy-tailored thermoelectric material, which gives guidance for regulating thermal transport. Thermoelectric effect ; Materials science ; Thermoelectric materials ; Condensed matter physics ; Thermal ; Chemical physics ; Chemistry ; Thermodynamics ; Physics Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Advanced Thermodynamics and Statistical Mechanics
Authors: Samuel T. Cahill | Joseph E. B. Young | Max Howe | Ryan Clark | Andrew F. Worrall | Malcolm I. Stewart
Source: Journal of Chemical Education (article, 2024-06-20)
Field: Chemistry — Physical and Theoretical Chemistry

Assignment of Regioisomers Using Infrared Spectroscopy: A Python Coding Exercise in Data Processing and Machine Learning Machine learning is a set of tools that are increasingly used in the field of chemistry. The introduction of potential uses of machine learning to undergraduate chemistry students should help to increase their comprehension of and interest in machine learning processes and can help support them in their transition into graduate research and industrial environments that use such tools. Herein we present an exercise aimed at introducing machine learning alongside improving students’ general Python coding abilities. The exercise aims to identify the regioisomerism of disubstituted benzene systems solely from infrared spectra, a simple and ubiquitous undergraduate technique. The exercise culminates in students collecting their own spectra of compounds with unknown regioisomerism and predicting the results, allowing them to take ownership of their results and creating a larger database of information to draw upon for machine learning in the future. Python (programming language) ; Computer science ; Comprehension ; Machine learning ; Coding (social sciences) ; Artificial intelligence ; Programming language Various Chemistry Research Topics ; Computational Drug Discovery Methods ; Advanced Chemical Sensor Technologies
Authors: S. V. Dmitriev | Andrey A. Kistanov | Igor V. Kosarev | S. A. Scherbinin | Alexander V. Shapeev
Source: Russian Physics Journal (article, 2024-09-01)
Field: Materials Science — Materials Chemistry

Construction of Machine Learning Interatomic Potentials for Metals Interatomic potential ; Computer science ; Materials science ; Physics ; Molecular dynamics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; X-ray Diffraction in Crystallography
Authors: Maciej Sypetkowski | Frederik Wenkel | Farimah Poursafaei | Nia Dickson | Karush Suri | Philip Fradkin | Dominique Beaini
Source: arXiv (Cornell University) (preprint, 2024-04-17)
Field: Materials Science — Materials Chemistry

On the Scalability of GNNs for Molecular Graphs Scaling deep learning models has been at the heart of recent revolutions in language modelling and image generation. Practitioners have observed a strong relationship between model size, dataset size, and performance. However, structure-based architectures such as Graph Neural Networks (GNNs) are yet to show the benefits of scale mainly due to the lower efficiency of sparse operations, large data requirements, and lack of clarity about the effectiveness of various architectures. We address this drawback of GNNs by studying their scaling behavior. Specifically, we analyze message-passing networks, graph Transformers, and hybrid architectures on the largest public collection of 2D molecular graphs. For the first time, we observe that GNNs benefit tremendously from the increasing scale of depth, width, number of molecules, number of labels, and the diversity in the pretraining datasets. We further demonstrate strong finetuning scaling behavior on 38 highly competitive downstream tasks, outclassing previous large models. This gives rise to MolGPS, a new graph foundation model that allows to navigate the chemical space, outperforming the previous state-of-the-arts on 26 out the 38 downstream tasks. We hope that our work paves the way for an era where foundational GNNs drive pharmaceutical drug discovery. Scalability ; Computer science ; Database Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Advanced Graph Neural Networks
Authors: Maicon Pierre Lourenço | Hadi Zadeh‐Haghighi | Jiří Hostaš | Mosayeb Naseri | Daya Ram Gaur | Christoph Simon | Dennis R. Salahub
Source: ChemRxiv (preprint, 2024-07-18)
Field: Materials Science — Materials Chemistry

Exploring Quantum Active Learning for Materials Design and Discovery The meeting of artificial intelligence (AI) and quantum computing is already a reality; quantum machine learning (QML) promises the design of better regression models. In this work, we extend our previous studies of materials discovery using classical active learning (AL), which showed remarkable economy of data, to explore the use of quantum algorithms within the AL framework (QAL) as implemented in the MLChem4D and QMLMaterials codes. The proposed QAL uses quantum support vector regressor (QSVR) or a quantum Gaussian process regressor (QGPR) with various quantum kernels and different feature maps. Data sets include perovskite properties (piezoelectric coefficient, band gap, energy storage) and the structure optimization of a doped nanoparticle (3Al@Si11) chosen to compare with classical AL results. Our results revealed that the QAL method improved the searches in most cases, but not all, seemingly correlated with the “roughness” of the data. QAL has the potential of finding optimum solutions, within chemical space, in materials science and elsewhere in chemistry. Data science ; Computer science ; Nanotechnology ; Materials science Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides
Authors: Mohamed M. Elsenety
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2024-12-10)
Field: Materials Science — Polymers and Plastics

Ultra-fast prediction of D-π-A organic dye absorption maximum with advanced ensemble deep learning models Chemistry ; Absorption (acoustics) ; Analytical Chemistry (journal) ; Chromatography ; Optics Conducting polymers and applications ; Water Quality Monitoring and Analysis ; Advanced Nanomaterials in Catalysis
Authors: Emil T. S. Kjær | Andy S. Anker | Andrea Kirsch | Joakim Lajer | Olivia Aalling‐Frederiksen | Simon J. L. Billinge | Kirsten M. Ø. Jensen
Source: Digital Discovery (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

MLstructureMining: a machine learning tool for structure identification from X-ray pair distribution functions Synchrotron X-ray techniques are essential for studies of the intrinsic relationship between synthesis, structure, and properties of materials. Modern synchrotrons can produce up to 1 petabyte of data per day. Such amounts of data can speed up materials development, but also comes with a staggering growth in workload, as the data generated must be stored and analyzed. We present an approach for quickly identifying an atomic structure model from pair distribution function (PDF) data from (nano)crystalline materials. Our model, MLstructureMining, uses a tree-based machine learning (ML) classifier. MLstructureMining has been trained to classify chemical structures from a PDF and gives a top-3 accuracy of 99% on simulated PDFs not seen during training, with a total of 6062 possible classes. We also demonstrate that MLstructureMining can identify the chemical structure from experimental PDFs from nanoparticles of CoFe<sub>2</sub>O<sub>4</sub> and CeO<sub>2</sub>, and we show how it can be used to treat an <i>in situ</i> PDF series collected during Bi<sub>2</sub>Fe<sub>4</sub>O<sub>9</sub> formation. Additionally, we show how MLstructureMining can be used in combination with the well-known methods, principal component analysis (PCA) and non-negative matrix factorization (NMF) to analyze data from <i>in situ</i> experiments. MLstructureMining thus allows for real-time structure characterization by screening vast quantities of crystallographic information files in seconds. Identification (biology) ; Distribution (mathematics) ; Computer science ; Artificial intelligence ; Biological system ; Mathematics ; Mathematical analysis ; Biology X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Mineral Processing and Grinding
Authors: Yuma Iwasaki | Ryo Toyama | Takahiro Yamazaki | Yasuhiko Igarashi | Masato Kotsugi | Yuya Sakuraba
Source: Science and Technology of Advanced Materials Methods (article, 2024-09-12)
Field: Materials Science — Materials Chemistry

Autonomous search for half-metallic materials with <i>B</i> 2 structure Exploring vast material spaces efficiently is challenging in materials science. Autonomous methods for material search – integrating machine learning and ab initio calculations – have emerged as powerful alternatives to traditional approaches, which are often time-consuming and limited in scope. Although these autonomous methods have been applied to various material systems, the extensive material space of B2 structured materials for half-metallicity remains largely unexplored. Herein, we introduce a simulation-based autonomous search approach to identify B2 structured alloys exhibiting high spin polarization of sp conduction electrons (Psp), sp minority spin band gap (Gsp), and Curie temperature (Tc). The proposed method explores the material space of disordered quaternary B2 magnetic alloys using the Korringa – Kohn – Rostoker coherent potential approximation and Bayesian optimization. Over a continuous search of approximately 100 days, the system identified Co1.0Mn0.7Al0.3 as a promising candidate, demonstrating high values of Psp, Gsp, and Tc. Although additional experimental and theoretical validation is necessary, this study demonstrates the potential of autonomous material search methods to expedite material discovery and enhance material property optimization. Materials science ; Metal ; Metallurgy Machine Learning in Materials Science ; Heusler alloys: electronic and magnetic properties ; Intermetallics and Advanced Alloy Properties
Authors: Alan S. S. Daou | Hanjun Fang | Salah Eddine Boulfelfel | Peter I. Ravikovitch | David S. Sholl
Source: The Journal of Physical Chemistry C (article, 2024-03-29)
Field: Chemistry — Inorganic Chemistry

Machine Learning and IAST-Aided High-Throughput Screening of Cationic and Silica Zeolites for Alkane Capture, Storage, and Separations We present an approach for quantitatively predicting the temperature-dependent single-component adsorption behavior of linear alkanes in silica and Na-exchanged cationic zeolites using machine learning (ML) models trained from extensive molecular simulations based on force fields with coupled cluster accuracy. A high-performing classification model was developed to distinguish between instances with negligible and non-negligible adsorption. Subsequently, two ML models were trained to predict the single-component adsorption loading and the heat of adsorption at any pressure at 300 K for any zeolite topology and silicon-to-aluminum ratio. The ML models were trained on International Zeolite Association (IZA) zeolites, and their transferability to hypothetical zeolites was successfully validated. We then expand the power of these predictions to adsorbed mixtures at arbitrary temperatures by integrating them with the Clausius–Clapeyron equation and ideal adsorbed solution theory (IAST). This approach was validated and then applied to a temperature swing adsorption separation process to demonstrate its practical utility. We demonstrate how predictions from this ML-enabled approach can allow the selection of high-performing materials that are then validated using detailed molecular simulations based on quantitatively accurate force fields. Cationic polymerization ; Adsorption ; Zeolite ; Component (thermodynamics) ; Cluster (spacecraft) ; Throughput ; Alkane ; Chemistry ; Process (computing) ; Computer science ; Materials science ; Thermodynamics ; Process engineering ; Biological system ; Hydrocarbon ; Physical chemistry ; Organic chemistry ; Catalysis ; Physics ; Engineering Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Xiliang Lian | Damien Dambournet | Mathieu Salanne
Source: Chemistry of Materials (article, 2024-12-16)
Field: Chemistry — Inorganic Chemistry

Stereoactive Electron Lone Pairs Facilitate Fluoride Ion Diffusion in Tetragonal BaSnF <sub>4</sub> Solid-state ionic conductors are of primary importance for the design of tomorrow’s batteries. In lithium- or sodium-ion-based materials, the alkali cations diffuse through three-dimensional channels consisting of interconnected tetrahedral or octahedral sites with low free energy barriers between them. Fluoride ion conductors stand out in this landscape since the materials with the highest conductivities belong to the MSnF4 family (in which M2+ is a divalent cation), whose structure is layered and characterized by double-layers of Sn2+ and M2+ cations along a given direction. Importantly, these materials display stereoactive electron lone pairs (LPs) that seemingly play an important role not only in stabilizing the Sn–Sn layer but also in modulating the fluoride ion diffusive behavior. However, despite previous experimental and simulation studies, the involvement of the LPs in the fluoride ion conduction mechanism remains to be quantitatively understood. In this work, we simulate the BaSnF4 tetragonal structure using machine learning-based molecular dynamics, in which the interaction potential is trained on density functional theory data. We investigated the role of the Sn–LP–Sn layer in lowering the diffusion energy landscape. In particular, we show how the F– ions jump across this layer and occur much more frequently than in the Ba–F–Ba one, resulting in the formation of vacancies in the Ba–Sn layers. Concurrently, the LP stereochemical activity fluctuates to accommodate the F ions jumping. In addition, the presence of the LP layer enhances the flexibility of the Sn ions, which leads to an increase in two-dimensional diffusion by several orders of magnitude. These results contribute to our understanding of the interplay between LPs and ionic diffusion, helping to explain the good performance of the material in fluoride-ion batteries. Ion ; Lone pair ; Delocalized electron ; Tetragonal crystal system ; Chemical physics ; Ionic bonding ; Fluoride ; Diffusion ; Octahedron ; Density functional theory ; Divalent ; Crystallography ; Chemistry ; Fast ion conductor ; Ionic conductivity ; Alkali metal ; Materials science ; Inorganic chemistry ; Computational chemistry ; Crystal structure ; Molecule ; Physical chemistry ; Thermodynamics ; Electrolyte Inorganic Fluorides and Related Compounds ; Inorganic Chemistry and Materials ; Advanced Condensed Matter Physics
Authors: Bhasha Sathyan | Gaurav Banerjee | Ajinkya Ashok Jagtap | Abhishek Verma | Jobin Cyriac
Source: ACS Applied Bio Materials (article, 2024-01-31)
Field: Materials Science — Materials Chemistry

Deep-Learning-Assisted Discriminative Detection of Vitamin B<sub>12</sub> and Vitamin B<sub>9</sub> by Fluorescent MoSe<sub>2</sub> Quantum Dots A facile and environmentally mindful approach for the synthesis of MoSe<sub>2</sub> QDs was developed via the hydrothermal method from bulk MoSe<sub>2</sub>. In this, the exfoliation of MoSe<sub>2</sub> was enhanced with the aid of an intercalation agent (KOH), which could reduce the exfoliation time and increase the exfoliation efficiency to form MoSe<sub>2</sub> QDs. We found that MoSe<sub>2</sub> QDs display blue emission that is suitable for different applications. This fluorescence property of MoSe<sub>2</sub> QDs was harnessed to fabricate a dual-modal sensor for the detection of both vitamin B<sub>12</sub> (VB<sub>12</sub>) and vitamin B<sub>9</sub> (VB<sub>9</sub>), employing fluorescence quenching. We performed a detailed study on the fluorescence quenching mechanism of both analytes. The predominant quenching mechanism for VB<sub>12</sub> is via Förster resonance energy transfer. In contrast, the recognition of VB<sub>9</sub> primarily relies on the inner filter effect. We applied an emerging and captivating approach to pattern recognition, the deep-learning method, which enables machines to "learn" patterns through training, eliminating the need for explicit programming of recognition methods. This attribute endows deep-learning with immense potential in the realm of sensing data analysis. Here, analyzing the array-based sensing data, the deep-learning technique, "convolution neural networks", has achieved 93% accuracy in determining the contribution of VB<sub>12</sub> and VB<sub>9</sub>. Fluorescence ; Quenching (fluorescence) ; Materials science ; Deep learning ; Quantum dot ; Exfoliation joint ; Förster resonance energy transfer ; Computer science ; Convolutional neural network ; Artificial intelligence ; Nanotechnology ; Optoelectronics ; Graphene ; Optics ; Physics 2D Materials and Applications ; Advanced biosensing and bioanalysis techniques ; Carbon and Quantum Dots Applications
Authors: Hua Yan | Qiang Li | Bin Yang | Yang Yang | Ying Wang | Hao Zhang
Source: JOM (article, 2024-11-20)
Field: Materials Science — Materials Chemistry

Modeling and Prediction Method for Young’s Moduli of Ti Alloys Based on Residual Muti-layer Perceptron Perceptron ; Moduli ; Residual ; Layer (electronics) ; Materials science ; Multilayer perceptron ; Layer by layer ; Composite material ; Computer science ; Artificial intelligence ; Artificial neural network ; Algorithm ; Physics Titanium Alloys Microstructure and Properties ; Metal and Thin Film Mechanics ; Welding Techniques and Residual Stresses