jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tobias Gensch | Gabriel dos Passos Gomes | Pascal Friederich | Ellyn Peters | Théophile Gaudin | Robert Pollice | Kjell Jorner | AkshatKumar Nigam | Michael Lindner D'Addario | Matthew S. Sigman | Alán Aspuru‐Guzik
Source: ChemRxiv (preprint, 2021-04-27)
Field: Materials Science — Materials Chemistry

A Comprehensive Discovery Platform for Organophosphorus Ligands for Catalysis The design of molecular catalysts typically involves reconciling multiple conflicting property requirements, largely relying on human intuition and local structural searches. However, the vast number of potential catalysts requires pruning of the candidate space by efficient property prediction with quantitative structure-property relationships. Data-driven workflows embedded in a library of potential catalysts can be used to build predictive models for catalyst performance and serve as a blueprint for novel catalyst designs. Herein we introduce &lt;i&gt;kraken&lt;/i&gt;, a discovery platform covering monodentate organophosphorus(III) ligands providing comprehensive physicochemical descriptors based on representative conformer ensembles. Using quantum-mechanical methods, we calculated descriptors for 1,558 ligands, including commercially available examples, and trained machine learning models to predict properties of over 300,000 new ligands. We demonstrate the application of &lt;i&gt;kraken&lt;/i&gt; to systematically explore the property space of organophosphorus ligands and how existing datasets in catalysis can be used to accelerate ligand selection during reaction optimization. Blueprint ; Computer science ; Workflow ; Intuition ; Chemical space ; Catalysis ; Biochemical engineering ; Chemistry ; Artificial intelligence ; Combinatorial chemistry ; Engineering ; Drug discovery ; Database ; Cognitive science ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: E.A. Périgo | Rubens Nunes de Faria
Source: Magnetochemistry (article, 2021-06-07)
Field: Materials Science — Materials Chemistry

Artificial Intelligence—Engineering Magnetic Materials: Current Status and a Brief Perspective The implementation of artificial intelligence into the research and development of (currently) the most economically relevant classes of engineering hard and soft magnetic materials is addressed. Machine learning is nowadays the key approach utilized in the discovery of new compounds, physical–chemical properties prediction, microstructural/magnetic characterization, and applicability of permanent magnets and crystalline/amorphous soft magnetic alloys. Future opportunities are envisioned on at least two fronts: (a) ultra-low losses materials, as well as processes that enable their manufacturing, unlocking the next step for higher efficiency electrification, power conversion, and distribution; (b) additively manufactured magnetic materials by predicting and developing novel powdered materials properties, generative design concepts, and optimal processing conditions. Generative grammar ; Magnet ; Characterization (materials science) ; Perspective (graphical) ; Materials science ; Nanotechnology ; Computer science ; Engineering physics ; Artificial intelligence ; Mechanical engineering ; Engineering Machine Learning in Materials Science ; Magnetic Properties of Alloys ; X-ray Diffraction in Crystallography
Authors: Fang Wu | Qiang Zhang | Dragomir Radev | Jiyu Cui | Wen Zhang | Huabin Xing | Ningyu Zhang | Huajun Chen
Source: arXiv (Cornell University) (preprint, 2021-10-04)
Field: Materials Science — Materials Chemistry

3D-Transformer: Molecular Representation with Transformer in 3D Space Spatial structures in the 3D space are important to determine molecular properties. Recent papers use geometric deep learning to represent molecules and predict properties. These papers, however, are computationally expensive in capturing long-range dependencies of input atoms; and have not considered the non-uniformity of interatomic distances, thus failing to learn context-dependent representations at different scales. To deal with such issues, we introduce 3D-Transformer, a variant of the Transformer for molecular representations that incorporates 3D spatial information. 3D-Transformer operates on a fully-connected graph with direct connections between atoms. To cope with the non-uniformity of interatomic distances, we develop a multi-scale self-attention module that exploits local fine-grained patterns with increasing contextual scales. As molecules of different sizes rely on different kinds of spatial features, we design an adaptive position encoding module that adopts different position encoding methods for small and large molecules. Finally, to attain the molecular representation from atom embeddings, we propose an attentive farthest point sampling algorithm that selects a portion of atoms with the assistance of attention scores, overcoming handicaps of the virtual node and previous distance-dominant downsampling methods. We validate 3D-Transformer across three important scientific domains: quantum chemistry, material science, and proteomics. Our experiments show significant improvements over state-of-the-art models on the crystal property prediction task and the protein-ligand binding affinity prediction task, and show better or competitive performance in quantum chemistry molecular datasets. This work provides clear evidence that biochemical tasks can gain consistent benefits from 3D molecular representations and different tasks require different position encoding methods. Computer science ; Transformer ; Molecular graph ; Embedding ; Theoretical computer science ; Artificial intelligence ; Graph ; Topology (electrical circuits) ; Algorithm ; Physics ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Chia‐Chen Wu | Fei Pan | Yen‐Hsun Su
Source: Advanced Photonics Research (article, 2021-05-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Surface Plasmon Resonance of Gold Nano‐Sea‐Urchins Controlled by Machine‐Learning‐Based Regulation in Seed‐Mediated Growth Gold sea‐urchin‐like nanoparticles (GSNPs) are promising candidates for cancer thermotherapy. Due to the complex growth of GSNPs and the need for a precise prediction of their surface plasmon wavelength, genetic‐algorithm‐based artificial neural networks (GANNs) are used to determine the relationship between synthesis parameters and the surface plasmon wavelength of GSNPs grown via seed‐mediated growth assisted by machine learning. Herein, a low‐data test is trained by varying the ratio and concentration of gold seeds, sodium citrate, hydroquinone, and HAuCl 4 . Then, a big data confirmation is conducted through massive parameter collection from over 684 samples. The well‐trained GANN can guide parameter selection for seed‐mediated growth to obtain the desired surface plasmon wavelength. An optimal model can be obtained after big data evolution to assist the growth method screening of the seed‐mediated growth of sea‐urchin‐like gold nanoparticles (SGNPs) to achieve a stronger electromagnetic field of the surface plasmons. Machine learning has an advantage over empirical method for seed‐mediated growth and surface plasmon wavelength prediction, which increases research efficiency and decreases cost. The performance of the grown SGNPs is substantially improved in the visible domain. Surface plasmon resonance ; Plasmon ; Surface plasmon ; Colloidal gold ; Materials science ; Wavelength ; Nanoparticle ; Nanotechnology ; Computer science ; Optoelectronics ; Machine learning Gold and Silver Nanoparticles Synthesis and Applications ; Quantum Dots Synthesis And Properties ; Nanoplatforms for cancer theranostics
Authors: Wenze Li | Donghan Wang | Zi-Rui Yang | Huijie Zhang | Lihong Hu | GuanHua Chen
Source: Journal of Chemical Information and Modeling (review, 2021-12-27)
Field: Materials Science — Materials Chemistry

DeepNCI: DFT Noncovalent Interaction Correction with Transferable Multimodal Three-Dimensional Convolutional Neural Networks A multimodal deep learning model, DeepNCI, is proposed for improving noncovalent interactions (NCIs) calculated via density functional theory (DFT). DeepNCI is composed of a three-dimensional convolutional neural network (3D CNN) for abstracting critical and comprehensive features from 3D electron density, and a neural network for modeling one-dimensional quantum chemical properties. By merging features from two networks, DeepNCI is able to reduce the root-mean-square error of DFT-calculated NCI from 1.19 kcal/mol to ∼0.2 kcal/mol for a NCI molecular database (>1000 molecules). The representativeness of the joint features can be visualized by t-distributed stochastic neighbor embedding (t-SNE), where they can distinguish categorized NCI systems quite well. Therefore, the fused model performs better than its component networks. In addition, the 3D CNN takes electron density as inputs that are in the same range, despite the size of molecular systems, so it can promote model applicability and transferability. To clarify the applicability of DeepNCI, an application domain (AD) has been defined with merged features using the <i>K</i>-nearest-neighbor method. The calculations for external test sets are shown that AD can properly monitor the reliability for a prediction. The model transferability is tested with a small database of homolysis bond dissociation energy including only dozens of samples. With NCI database pretrained parameters, the same or better performance than the reported results is achieved by transfer learning. This suggests that the DeepNCI model is transferable and it may transfer to other relative tasks, which possibly can resolve some small sampling problems. The source code of DeepNCI can be freely accessed at https://github.com/wenzelee/DeepNCI. Computer science ; Convolutional neural network ; Transferability ; Generalizability theory ; Artificial intelligence ; Density functional theory ; Artificial neural network ; Pattern recognition (psychology) ; Chemistry ; Theoretical computer science ; Machine learning ; Computational chemistry ; Mathematics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Siyuan Gong | Yutong Wang | Yajie Tian | Li Wang | Guozhu Liu
Source: AIChE Journal (article, 2021-04-03)
Field: Materials Science — Materials Chemistry

Rapid enthalpy prediction of transition states using molecular graph convolutional network Abstract The rapid development of machine learning has provided low‐cost and high‐throughput molecular energy prediction methods; however, the direct energy prediction of transition states (TSs) in radical reactions has received little attention, which can facilitate the rapid construction of potential energy surface (PES) and chemical reactivity inference. Here, we present a generic descriptor of TSs, molecules and radicals to realize the simultaneous prediction of their enthalpy of formation through the augmented molecular graph convolutional network (MGCN). It is proved that the network can rapidly (within milliseconds per prediction) and accurately predict the enthalpy of various types of samples, of which the mean absolute error (MAE) of the held‐out test set is 1.44 kcal/mol (&gt;50% samples less than 1.0 kcal/mol) with high R 2 (0.925–0.998) among all categories. The PES constructed from predictions is close to the calculated results by density functional theory (DFT) and conforms to the trend of chemical reactivity. Enthalpy ; Graph ; Inference ; Reactivity (psychology) ; Chemistry ; Graph theory ; Computer science ; Thermodynamics ; Computational chemistry ; Mathematics ; Artificial intelligence ; Physics ; Theoretical computer science ; Combinatorics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; CO2 Reduction Techniques and Catalysts
Authors: Piero Gasparotto | Maria Fischer | Daniele Scopece | Maciej Oskar Liedke | Maik Butterling | A. Wagner | O. Yıldırım | Mathis Trant | Daniele Passerone | Hans J. Hug | Carlo A. Pignedoli
Source: ACS Applied Materials & Interfaces (article, 2021-01-19)
Field: Materials Science — Materials Chemistry

Mapping the Structure of Oxygen-Doped Wurtzite Aluminum Nitride Coatings from <i>Ab Initio</i> Random Structure Search and Experiments Machine learning is changing how we design and interpret experiments in materials science. In this work, we show how unsupervised learning, combined with <i>ab initio</i> random structure searching, improves our understanding of structural metastability in multicomponent alloys. We focus on the case of Al-O-N alloys where the formation of aluminum vacancies in wurtzite AlN upon the incorporation of substitutional oxygen can be seen as a general mechanism of solids where crystal symmetry is reduced to stabilize defects. The ideal AlN wurtzite crystal structure occupation cannot be matched due to the presence of an aliovalent hetero-element into the structure. The traditional interpretation of the <i>c</i>-lattice shrinkage in sputter-deposited Al-O-N films from X-ray diffraction (XRD) experiments suggests the existence of a solubility limit at 8 at % oxygen content. Here, we show that such naive interpretation is misleading. We support XRD data with accurate <i>ab initio</i> modeling and dimensionality reduction on advanced structural descriptors to map structure-property relationships. No signs of a possible solubility limit are found. Instead, the presence of a wide range of non-equilibrium oxygen-rich defective structures emerging at increasing oxygen contents suggests that the formation of grain boundaries is the most plausible mechanism responsible for the lattice shrinkage measured in Al-O-N sputtered films. We further confirm our hypothesis using positron annihilation lifetime spectroscopy. Materials science ; Wurtzite crystal structure ; Ab initio ; Crystal structure ; Ab initio quantum chemistry methods ; Chemical physics ; Crystallography ; Metallurgy ; Chemistry ; Zinc ; Molecule Machine Learning in Materials Science ; Semiconductor materials and devices ; Metal and Thin Film Mechanics
Authors: Ryo Nagai | Ryosuke Akashi | Osamu Sugino
Source: arXiv (Cornell University) (preprint, 2021-11-30)
Field: Materials Science — Materials Chemistry

Machine-Learning-Based Exchange-Correlation Functional with Physical\n Asymptotic Constraints Density functional theory is the standard theory for computing the electronic\nstructure of materials, which is based on a functional that maps the electron\ndensity to the energy. However, a rigorous form of the functional is not known\nand has been heuristically constructed by interpolating asymptotic constraints\nknown for extreme situations, such as isolated atoms and uniform electron gas.\nRecent studies have demonstrated that the functional can be effectively\napproximated using machine learning (ML) approaches. However, most ML models do\nnot satisfy asymptotic constraints. In this study, by applying a novel ML model\narchitecture, we demonstrate a neural network-based exchange-correlation\nfunctional satisfying physical asymptotic constraints. Calculations reveal that\nthe trained functional is applicable to various materials with an accuracy\nhigher than that of existing functionals, even for materials whose electronic\nproperties are different from the properties of materials in the training\ndataset. Our proposed approach thus improves the accuracy and generalization\nperformance of the ML-based functional by combining the advantages of ML and\nanalytical modeling.\n Density functional theory ; Generalization ; Energy functional ; Orbital-free density functional theory ; Hybrid functional ; Computer science ; Artificial neural network ; Applied mathematics ; Mathematics ; Artificial intelligence ; Computational chemistry ; Mathematical analysis ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Hideo Doi | Kazuaki Z. Takahashi | Takeshi Aoyagi
Source: The Journal of Physical Chemistry A (article, 2021-10-22)
Field: Materials Science — Materials Chemistry

Mining of Effective Local Order Parameters to Classify Ice Polymorphs Order parameters make it possible to quantify the degree of structural ordering in a material and thus to apply as the reaction coordinates during the free-energy analysis of phase or structure transitions. Furthermore, order parameters are useful in determining the local structures of molecular groups during transition stages. However, identifying or developing local order parameters (LOPs) that are sensitive for specific materials and phases is a non-trivial task. In this study, the ability of LOPs to classify the solid and liquid structures of water at coexistence or triple points is investigated with the aid of supervised machine learning. The classification accuracy of a total of 179,738,433 combinations of 493 LOPs is automatically and systematically compared for water structures at the ice Ih-Ic-liquid coexistence point and the ice III-V-liquid and ice V-VI-liquid triple points. The optimal sets of two LOPs are found for each point, and sets of three LOPs are suggested for better accuracy. Triple point ; Phase transition ; Materials science ; Thermodynamics ; Order (exchange) ; Computer science ; Statistical physics ; Physics Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Advanced Chemical Physics Studies
Authors: Houli Li | Jin Hu | Yixiao Wang | Xuebin An | Mingzhu Tang | Zhiying Wang | Yunshan Wang | Gang Yang | Weijun Bao | Yong Sun
Source: Process Safety and Environmental Protection (article, 2021-10-21)
Field: Chemistry — Inorganic Chemistry

Utilization of phosphogypsum waste through a temperature swing recyclable acid process and its application for transesterification Phosphogypsum ; Transesterification ; Leaching (pedology) ; Calcination ; Catalysis ; Biodiesel ; Leachate ; Crystallization ; Chemistry ; Materials science ; Solvent ; Chemical engineering ; Nuclear chemistry ; Waste management ; Environmental science ; Organic chemistry ; Raw material ; Environmental chemistry Radioactive element chemistry and processing ; Metal Extraction and Bioleaching ; Thermal and Kinetic Analysis
Authors: Behnam Jahangiri | Kaveh Barri | Amir H. Alavi | William G. Buttlar
Source: Measurement (article, 2021-05-09)
Field: Chemistry — Analytical Chemistry

A molecular sensing method integrated with support vector machines to characterize asphalt mixtures Asphalt ; Natural rubber ; Process engineering ; Cracking ; Materials science ; Process (computing) ; Support vector machine ; Computer science ; Crumb rubber ; Environmental science ; Composite material ; Engineering ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Advanced Chemical Sensor Technologies
Authors: Kourosh Shariat | Dmitry Kirsanov | Alejandro C. Olivieri | Hadi Parastar
Source: Analytica Chimica Acta (article, 2021-05-26)
Field: Chemistry — Analytical Chemistry

Sensitivity and generalized analytical sensitivity expressions for quantitative analysis using convolutional neural networks Sensitivity (control systems) ; Convolutional neural network ; Artificial neural network ; Regularization (linguistics) ; Figure of merit ; Calibration ; Algorithm ; Artificial intelligence ; Chemistry ; Pattern recognition (psychology) ; Computer science ; Statistics ; Mathematics Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems ; Computational Drug Discovery Methods
Authors: Felix Ott | Kersten S. Rabe | Christof M. Niemeyer | Gudrun Gygli
Source: ACS Catalysis (article, 2021-08-12)
Field: Chemistry — Physical and Theoretical Chemistry

Toward Reproducible Enzyme Modeling with Isothermal Titration Calorimetry To apply enzymes in technical processes, a detailed understanding of the molecular mechanisms is required. Kinetic and thermodynamic parameters of enzyme catalysis are crucial to plan, model, and implement biocatalytic processes more efficiently. While the kinetic parameters, Km and kcat, are often accessible by optical methods, the determination of thermodynamic parameters requires more sophisticated methods. Isothermal titration calorimetry (ITC) allows the label-free and highly sensitive analysis of kinetic and thermodynamic parameters of individual steps in the catalytic cycle of an enzyme reaction. However, since ITC is susceptible to interferences due to denaturation or agglomeration of the enzymes, the homogeneity of the enzyme sample must always be considered, and this can be accomplished by means of dynamic light scattering (DLS) analysis. The presented ITCdependent workflow was used to determine both the kinetic and the thermodynamic data for a cofactor-dependent enzyme. Using a standardized approach with the implementation of sample quality control by DLS, we obtained high-quality data suitable for the advanced modeling of the enzyme reaction mechanism. Specifically, we investigated stereoselective reactions catalyzed by the NADPH-dependent ketoreductase Gre2p under different reaction conditions. The results revealed that this enzyme operates with an ordered sequential mechanism in which the cofactor NADPH binds first, resulting in Gre2pholo, and that only Gre2pholo can then bind the substrate NDK. In addition, the enzyme was found to be affected by substrate or product inhibition depending on the reaction buffer. Data reproducibility, a mandatory prerequisite to achieve robust modeling, is ensured by specifying standard operating procedures, using programmed workflows for data analysis and storing all data in a F.A.I.R. (findable, accessible, interoperable, and reusable) repository. Our approach highlights the utility for combined binding and kinetic studies for such complex multisubstrate reactions. Because it is amenable to automation and scale-up for high-throughput, the combination of such diverse approaches will provide the high-quality data needed for the engineering of enzymes and biocatalytic processes through machine learning to accelerate future development of industrial biocatalysis. Isothermal titration calorimetry ; Enzyme kinetics ; Chemistry ; Enzyme catalysis ; Isothermal process ; Titration ; Catalysis ; Thermodynamics ; Physical chemistry ; Active site ; Organic chemistry thermodynamics and calorimetric analyses ; Enzyme Catalysis and Immobilization ; Computational Drug Discovery Methods