jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tom Vermeyen | Andrea N. L. Batista | Alessandra L. Valverde | Wouter Herrebout | João M. Batista
Source: Physical Chemistry Chemical Physics (review, 2023-01-01)
Field: Chemistry — Spectroscopy

Pushing the boundaries of VCD spectroscopy in natural product chemistry Vibrational circular dichroism (VCD) is one of the most powerful techniques to assess the stereochemistry of chiral molecules in solution state. The need for quantum chemical calculations to interpret experimental data, however, has precluded its widespread use by non-experts. Herein, we propose the search and validation of IR and VCD spectral markers to circumvent the requirement of DFT calculations allowing for absolute configuration assignments even in complex mixtures. To that end, a combination of visual inspection and machine learning based methods is used. Monoterpene mixtures are selected for this proof-of-concept study. Terpene ; Natural product ; Chemistry ; Spectroscopy ; Absolute configuration ; Infrared spectroscopy ; Computational chemistry ; Analytical Chemistry (journal) ; Organic chemistry ; Physics Molecular spectroscopy and chirality ; Analytical Chemistry and Chromatography ; Phytochemistry and Biological Activities
Authors: Marta Barea-Sepúlveda | José Luis P. Calle | Marta Ferreiro‐González | Miguel Palma
Source: Foods (article, 2023-09-07)
Field: Chemistry — Analytical Chemistry

Rapid Classification of Petroleum Waxes: A Vis-NIR Spectroscopy and Machine Learning Approach Petroleum-derived waxes are used in the food industry as additives to provide texture and as coatings for foodstuffs such as fruits and cheeses. Therefore, food waxes are subject to strict quality controls to comply with regulations. In this research, a combination of visible and near-infrared (Vis-NIR) spectroscopy with machine learning was employed to effectively characterize two commonly marketed petroleum waxes of food interest: macrocrystalline and microcrystalline. The present study employed unsupervised machine learning algorithms like hierarchical cluster analysis (HCA) and principal component analysis (PCA) to differentiate the wax samples based on their chemical composition. Furthermore, nonparametric supervised machine learning algorithms, such as support vector machines (SVMs) and random forest (RF), were applied to the spectroscopic data for precise classification. Results from the HCA and PCA demonstrated a clear trend of grouping the wax samples according to their chemical composition. In combination with five-fold cross-validation (CV), the SVM models accurately classified all samples as either macrocrystalline or microcrystalline wax during the test phase. Similar high-performance outcomes were observed with RF models along with five-fold CV, enabling the identification of specific wavelengths that facilitate discrimination between the wax types, which also made it possible to select the wavelengths that allow discrimination of the samples to build the characteristic <i>spectralprint</i> of each type of petroleum wax. This research underscores the effectiveness of the proposed analytical method in providing fast, environmentally friendly, and cost-effective quality control for waxes. The approach offers a promising alternative to existing techniques, making it a viable option for automated quality assessment of waxes in food industrial applications. Wax ; Principal component analysis ; Support vector machine ; Artificial intelligence ; Computer science ; Machine learning ; Random forest ; Pattern recognition (psychology) ; Biological system ; Materials science ; Environmental science Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Pesticide Residue Analysis and Safety
Authors: Liuzhou Gao | Xinyi Liang | Linke He | Guoao Li | Shengda Chen | Jia Cao | Jing Ma | Guoqiang Wang | Shuhua Li
Source: Chemical Science (article, 2023-01-01)
Field: Chemistry — Organic Chemistry

Base-mediated C–B bond activation of benzylic boronate for the rapid construction of β-silyl/boryl functionalized 1,1-diarylalkanes from aromatic alkenes The effect of <sup><i>t</i></sup>BuOK on the existing state of benzylic boronates in the solution phase has been investigated in detail by NMR analysis and DFT calculations. It was determined that simply using an excess of <sup><i>t</i></sup>BuOK (2.0 equivalents) can result in the full deborylation of benzylic boronates to afford free benzyl potassium species. These mechanistic insights were leveraged for the facile construction of β-silyl/boryl functionalized 1,1-diarylalkanes from aromatic alkenes <i>via</i> the combination of base-mediated silylboration or diborylation of aromatic alkenes and nucleophilic-type reactions with various electrophiles. Based on further machine-learning-assisted screening, the scope of electrophiles for this transformation can be generalized to the challenging aromatic heterocycles. Late-stage functionalization performed on several drug-relevant molecules generates the highly valuable 1,1-diaryl framework. Silylation ; Carbanion ; Chemistry ; Base (topology) ; Organic chemistry ; Medicinal chemistry ; Combinatorial chemistry ; Catalysis ; Mathematics Organoboron and organosilicon chemistry ; Catalytic C–H Functionalization Methods ; Catalytic Cross-Coupling Reactions
Authors: H Stevens | Jeffrey R. Olsen | Justin K. Kirkland | Daniel H. Ess
Source: Organometallics (article, 2023-12-28)
Field: Chemistry — Organic Chemistry

Tolman Electronic Parameter Predictions from a Machine Learning Model Provide Insight into Phosphine Ligand Electronic Effects Phosphines are extremely important ligands in organometallic chemistry, and their donor or acceptor ability can be measured through the Tolman electron parameter (TEP). Here, we describe the development of a TEP machine learning model (called TEPid) that provides nearly instantaneous calculation of experimentally calibrated CO vibrational stretch frequencies for (R)3P–Ni0(CO)3 complexes. This machine learning model with an error of less than 1 cm–1 (compared to density functional theory (DFT) calculated values) was developed using >4,000 DFT calculated (R)3P–Ni0(CO)3 TEP values and 19 connectivity-based descriptors associated with SMILES strings. We also built a web-based interface to run the machine learning model where phosphine SMILES strings can be entered and TEP values returned. We applied this TEPid model to examine the donor and acceptor capabilities of phosphines in the large Kraken phosphine database. This showed that the Kraken database is skewed toward donor phosphines. In the same spirit of the Kraken database, we generated tens of thousands of new experimentally based phosphines that, when combined with Kraken phosphines, provide an electronically balanced ligand library. Phosphine ; Chemistry ; Ligand (biochemistry) ; Density functional theory ; Computational chemistry ; Acceptor ; Artificial intelligence ; Topology (electrical circuits) ; Computer science ; Physics ; Quantum mechanics ; Engineering Organometallic Complex Synthesis and Catalysis ; Carbon dioxide utilization in catalysis ; Organoboron and organosilicon chemistry
Authors: Chuanwu Yang | Xinge You | Rongxiao Yu | Xu Yao | Jianfeng Zhang | Xiaobo Fan | Weifu Li | Zi Wang
Source: Materials Characterization (article, 2023-06-15)
Field: Materials Science — Metals and Alloys

Semi-supervised deep transfer learning for the microstructure recognition in the high-throughput characterization of nickel-based superalloys Superalloy ; Materials science ; Artificial intelligence ; Transfer of learning ; Throughput ; Feature (linguistics) ; Computer science ; Characterization (materials science) ; Pattern recognition (psychology) ; Microstructure ; Domain (mathematical analysis) ; Deep learning ; Machine learning ; Nanotechnology ; Metallurgy Hydrogen embrittlement and corrosion behaviors in metals ; High Temperature Alloys and Creep ; Machine Learning in Materials Science
Authors: James Goff | Yu Zhang | Christian F. A. Negre | Andrew Rohskopf | Anders M. N. Niklasson
Source: Journal of Chemical Theory and Computation (article, 2023-06-29)
Field: Materials Science — Materials Chemistry

Shadow Molecular Dynamics and Atomic Cluster Expansions for Flexible Charge Models A shadow molecular dynamics scheme for flexible charge models is presented where the shadow Born–Oppenheimer potential is derived from a coarse-grained approximation of range-separated density functional theory. The interatomic potential, including the atomic electronegativities and the charge-independent short-range part of the potential and force terms, is modeled by the linear atomic cluster expansion (ACE), which provides a computationally efficient alternative to many machine learning methods. The shadow molecular dynamics scheme is based on extended Lagrangian (XL) Born–Oppenheimer molecular dynamics (BOMD) [Eur. Phys. J. B 2021, 94, 164]. XL-BOMD provides stable dynamics while avoiding the costly computational overhead associated with solving an all-to-all system of equations, which normally is required to determine the relaxed electronic ground state prior to each force evaluation. To demonstrate the proposed shadow molecular dynamics scheme for flexible charge models using atomic cluster expansion, we emulate the dynamics generated from self-consistent charge density functional tight-binding (SCC-DFTB) theory using a second-order charge equilibration (QEq) model. The charge-independent potentials and electronegativities of the QEq model are trained for a supercell of uranium oxide (UO2) and a molecular system of liquid water. The combined ACE+XL-QEq molecular dynamics simulations are stable over a wide range of temperatures both for the oxide and for the molecular systems and provide a precise sampling of the Born–Oppenheimer potential energy surfaces. Accurate ground Coulomb energies are produced by the ACE-based electronegativity model during an NVE simulation of UO2, predicted to be within 1 meV of those from SCC-DFTB on average during comparable simulations. Molecular dynamics ; Chemistry ; Density functional theory ; Charge (physics) ; Cluster (spacecraft) ; Electronegativity ; Statistical physics ; Coupled cluster ; Supercell ; Computational chemistry ; Physics ; Quantum mechanics ; Molecule ; Computer science Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Electronic and Structural Properties of Oxides
Authors: Han Liu | Yuhan Liu | Kevin Li | Zhangji Zhao | Samuel S. Schoenholz | Ekin D. Cubuk | Puneet Gupta | Mathieu Bauchy
Source: npj Computational Materials (article, 2023-07-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

End-to-end differentiability and tensor processing unit computing to accelerate materials’ inverse design Abstract Numerical simulations have revolutionized material design. However, although simulations excel at mapping an input material to its output property, their direct application to inverse design has traditionally been limited by their high computing cost and lack of differentiability. Here, taking the example of the inverse design of a porous matrix featuring targeted sorption isotherm, we introduce a computational inverse design framework that addresses these challenges, by programming differentiable simulation on TensorFlow platform that leverages automated end-to-end differentiation. Thanks to its differentiability, the simulation is used to directly train a deep generative model, which outputs an optimal porous matrix based on an arbitrary input sorption isotherm curve. Importantly, this inverse design pipeline leverages the power of tensor processing units (TPU)—an emerging family of dedicated chips, which, although they are specialized in deep learning, are flexible enough for intensive scientific simulations. This approach holds promise to accelerate inverse materials design. Differentiable function ; Inverse ; Computer science ; Computational science ; Pipeline (software) ; Mathematical optimization ; Mathematics ; Mathematical analysis ; Geometry Metamaterials and Metasurfaces Applications ; Electromagnetic Scattering and Analysis ; Block Copolymer Self-Assembly
Authors: Yachan Liu | Gustavo Pérez | Zezhou Cheng | Aaron Sun | Samuel C. Hoover | Wei Fan | Subhransu Maji | Peng Bai
Source: Journal of Materials Chemistry A (article, 2023-01-01)
Field: Chemistry — Inorganic Chemistry

ZeoNet: 3D convolutional neural networks for predicting adsorption in nanoporous zeolites ZeoNet, based on 3D convolutional neural networks and a volumetric distance-grid representation, delivers an exceptional performance in predicting Henry's constants for adsorption of long-chain hydrocarbon molecules in all-silica zeolites. Nanoporous ; Adsorption ; Convolutional neural network ; Hydrocarbon ; Representation (politics) ; Grid ; Zeolite ; Computer science ; Molecule ; Materials science ; Artificial neural network ; Chemistry ; Artificial intelligence ; Nanotechnology ; Mathematics ; Organic chemistry ; Catalysis ; Geometry Zeolite Catalysis and Synthesis ; Mesoporous Materials and Catalysis ; Catalytic Processes in Materials Science
Authors: Sergei F. Vyboishchikov
Source: Journal of Chemical Theory and Computation (article, 2023-11-14)
Field: Materials Science — Materials Chemistry

Predicting Solvation Free Energies Using Electronegativity-Equalization Atomic Charges and a Dense Neural Network: A Generalized-Born Approach I propose a dense Neural Network, ESE-GB-DNN, for evaluation of solvation free energies Δ<i>G</i>°<sub>solv</sub> for molecules and ions in water and nonaqueous solvents. As input features, it employs generalized-Born monatomic and diatomic terms, as well as atomic surface areas and the molecular volume. The electrostatics calculation is based on a specially modified version of electronegativity-equalization atomic charges. ESE-GB-DNN evaluates Δ<i>G</i>°<sub>solv</sub> in a simple and highly efficient way, yet it offers a high accuracy, often challenging that of standard DFT-based methods. For neutral solutes, ESE-GB-DNN yields an RMSE between 0.7 and 1.3 kcal/mol, depending on the solvent class. ESE-GB-DNN performs particularly well for nonaqueous solutions of ions, with an RMSE of about 0.7 kcal/mol. For ions in water, the RMSE is larger (2.9 kcal/mol). Solvation ; Electronegativity ; Diatomic molecule ; Ion ; Artificial neural network ; Chemistry ; Solvent ; Electrostatics ; Computational chemistry ; Molecule ; Physical chemistry ; Computer science ; Machine learning ; Organic chemistry Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Computational Drug Discovery Methods
Authors: Eva Marie Freiberger | Julien Steffen | Natalie J. Waleska‐Wellnhofer | Felix Hemauer | Valentin Schwaab | Andreas Görling | Hans‐Peter Steinrück | Christian Papp
Source: Nanotechnology (article, 2023-12-04)
Field: Materials Science — Materials Chemistry

Bromination of 2D materials The adsorption, reaction and thermal stability of bromine on Rh(111)-supported hexagonal boron nitride (<i>h</i>-BN) and graphene were investigated. Synchrotron radiation-based high-resolution x-ray photoelectron spectroscopy (XPS) and temperature-programmed XPS allowed us to follow the adsorption process and the thermal evolution<i>in situ</i>on the molecular scale. On<i>h</i>-BN/Rh(111), bromine adsorbs exclusively in the pores of the nanomesh while we observe no such selectivity for graphene/Rh(111). Upon heating, bromine undergoes an on-surface reaction on<i>h</i>-BN to form polybromides (170-240 K), which subsequently decompose to bromide (240-640 K). The high thermal stability of Br/<i>h</i>-BN/Rh(111) suggests strong/covalent bonding. Bromine on graphene/Rh(111), on the other hand, reveals no distinct reactivity except for intercalation of small amounts of bromine underneath the 2D layer at high temperatures. In both cases, adsorption is reversible upon heating. Our experiments are supported by a comprehensive theoretical study. DFT calculations were used to describe the nature of the<i>h</i>-BN nanomesh and the graphene moiré in detail and to study the adsorption energetics and substrate interaction of bromine. In addition, the adsorption of bromine on<i>h</i>-BN/Rh(111) was simulated by molecular dynamics using a machine-learning force field. Halogenation ; Materials science ; Engineering physics ; Polymer science ; Engineering ethics ; Organic chemistry ; Engineering ; Chemistry Graphene research and applications ; Advancements in Battery Materials ; Nanomaterials for catalytic reactions
Authors: Sepideh Hashemi | Surya R. Kalidindi
Source: International Journal of Plasticity (article, 2023-01-19)
Field: Materials Science — Materials Chemistry

Gaussian process autoregression models for the evolution of polycrystalline microstructures subjected to arbitrary stretching tensors Crystal plasticity finite element models (CPFEM) have shown tremendous potential for simulating the microstructure evolution paths in polycrystalline aggregates subjected to large plastic strains. However, their high computational cost has hindered their broader deployment in design efforts where large process design spaces need to be explored. In this work, a novel machine learning framework is developed to establish low-computational cost reduced-order models for predicting the details of microstructure evolution in face-centered cubic (FCC) polycrystalline microstructures subjected to arbitrary stretching tensors. Within this framework, the previously established feature engineering of polycrystalline materials within the material knowledge system (MKS) framework is extended such that it is applicable to the highly deformed microstructures obtained during large deformations. Gaussian process autoregression (GPAR) approaches combined with Bayesian design of experiment strategies are employed for building the desired surrogate models to optimize the generation of the computationally expensive training data (produced using CPFEM). It is demonstrated that a relatively small training set of 1400 datapoints is adequate to produce a high-fidelity reduced-order model for predicting the details of the microstructure evolution in a very broad set of FCC polycrystalline aggregates subjected to arbitrary macroscopically imposed stretching tensors. Materials science ; Microstructure ; Crystallite ; Context (archaeology) ; Process (computing) ; Mechanical engineering ; Computer science ; Composite material ; Metallurgy ; Engineering Machine Learning in Materials Science ; Injection Molding Process and Properties
Authors: Yanmiao Wu | Zhongwen Shang | Tian Lu | Wenyan Zhou | Minjie Li | Wencong Lu
Source: Journal of Alloys and Compounds (article, 2023-10-26)
Field: Materials Science — Materials Chemistry

Target-directed discovery for low melting point alloys via inverse design strategy Melting point ; Inverse ; Point (geometry) ; Computer science ; Melting temperature ; Algorithm ; Materials science ; Mathematical optimization ; Process engineering ; Mechanical engineering ; Mathematics ; Engineering ; Composite material Machine Learning in Materials Science ; Thermography and Photoacoustic Techniques ; Thermal properties of materials
Authors: Balaji G. Ghule | Min-Kyeong Kim | Ji‐Hyun Jang
Source: Bulletin of the Korean Chemical Society (article, 2023-08-23)
Field: Materials Science — Materials Chemistry

Predicting photoresist sensitivity using machine learning Abstract We introduce a scheme for predicting photoresist sensitivity using machine learning (ML) work flow on the basis of previously reported experimental data. Different ML models, specifically Linear Regression, Kernel Ridge Regression, Gaussian Process Regressor, Random Forest Regressor, and Multilayer Perceptron Regressor, were evaluated to rapidly identify the best sensitivity prediction model. The experiment was carried out on the Google Colab platform using the Materials Simulation Toolkit for Machine Learning and Sci‐kit Learn. Different ensemble models were utilized without splitting the dataset to determine the prediction accuracy of the ML models. The hyperparameter optimization was established with a 70/30 ratio, followed by a K‐Fold cross‐validation to improve the model prediction performance. The optimized ML model showed a prediction performance of R 2 = 0.83, RMSE = 10.53, and MARE = 0.68. Hence, by optimizing the hyperparameters used in the ML model, the sensitivity of the photoresist materials can be predicted with improved prediction performance. Hyperparameter ; Random forest ; Sensitivity (control systems) ; Machine learning ; Artificial intelligence ; Computer science ; Perceptron ; Mean squared error ; Gaussian process ; Kriging ; Cross-validation ; Regression ; Linear regression ; Multilayer perceptron ; Mean squared prediction error ; Photoresist ; Artificial neural network ; Gaussian ; Mathematics ; Statistics ; Materials science ; Engineering ; Chemistry Machine Learning in Materials Science ; Advancements in Photolithography Techniques ; Industrial Vision Systems and Defect Detection
Authors: Jincheng Peng | Guoyue Chen | Kazuki Saruta | Yuki Terata
Source: Medical Imaging Process & Technology (article, 2023-10-09)
Field: Materials Science — Materials Chemistry

2D brain MRI image synthesis based on lightweight denoising diffusion probabilistic model In recent years, brain health has received increasing attention, but conventional acquisition of brain MRI (magnetic resonance imaging) images still suffer from issues such as missing data, artifacts, and high costs, which hinders research and diagnosis. With the application of deep learning in medical image synthesis, low-cost, efficient, and high-quality medical MRI synthesis techniques have become a prominent research focus and have gradually matured. However, traditional methods for synthesizing magnetic resonance imaging (MRI) mostly rely on generative adversarial networks, which require fine-tuning of parameters and learning rates to achieve stringent Nash equilibrium conditions, leading to problems such as gradient explosions and mode collapse. Building upon the latest research in synthetic models DDPM (denoising diffusion probabilistic model), we propose a novel approach for 2D brain MRI image synthesis based on a lightweight denoising diffusion probabilistic model. This method improves the attention module in the denoising diffusion probabilistic model to make it more lightweight. Additionally, we adopt the smooth L1 loss function as a replacement for the traditional mean absolute error (L1 loss) by comparing the error between the 2D brain MRI images with added noise and the real noise for training the model. Finally, we validate the proposed model on the MRI Brain Tumor Classification dataset, demonstrating that it achieves high-quality synthesis results while significantly reducing the parameter count of the DDPM model. Computer science ; Probabilistic logic ; Artificial intelligence ; Noise reduction ; Magnetic resonance imaging ; Noise (video) ; Deep learning ; Image quality ; Diffusion MRI ; Image (mathematics) ; Pattern recognition (psychology) ; Medicine ; Radiology Machine Learning in Materials Science ; Cell Image Analysis Techniques ; Generative Adversarial Networks and Image Synthesis