jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Oliver T. Unke | Mihail Bogojeski | Michael Gastegger | Mario Geiger | Tess Smidt | Klaus‐Robert Müller
Source: arXiv (Cornell University) (preprint, 2021-06-04)
Field: Materials Science — Materials Chemistry

SE(3)-equivariant prediction of molecular wavefunctions and electronic densities Machine learning has enabled the prediction of quantum chemical properties with high accuracy and efficiency, allowing to bypass computationally costly ab initio calculations. Instead of training on a fixed set of properties, more recent approaches attempt to learn the electronic wavefunction (or density) as a central quantity of atomistic systems, from which all other observables can be derived. This is complicated by the fact that wavefunctions transform non-trivially under molecular rotations, which makes them a challenging prediction target. To solve this issue, we introduce general SE(3)-equivariant operations and building blocks for constructing deep learning architectures for geometric point cloud data and apply them to reconstruct wavefunctions of atomistic systems with unprecedented accuracy. Our model achieves speedups of over three orders of magnitude compared to ab initio methods and reduces prediction errors by up to two orders of magnitude compared to the previous state-of-the-art. This accuracy makes it possible to derive properties such as energies and forces directly from the wavefunction in an end-to-end manner. We demonstrate the potential of our approach in a transfer learning application, where a model trained on low accuracy reference wavefunctions implicitly learns to correct for electronic many-body interactions from observables computed at a higher level of theory. Such machine-learned wavefunction surrogates pave the way towards novel semi-empirical methods, offering resolution at an electronic level while drastically decreasing computational cost. Additionally, the predicted wavefunctions can serve as initial guess in conventional ab initio methods, decreasing the number of iterations required to arrive at a converged solution, thus leading to significant speedups without any loss of accuracy or robustness. Wave function ; Observable ; Artificial intelligence ; Computer science ; Equivariant map ; Electronic structure ; Statistical physics ; Algorithm ; Physics ; Quantum mechanics ; Mathematics ; Pure mathematics Machine Learning in Materials Science ; Topic Modeling ; Seismology and Earthquake Studies
Authors: Gabriela F. Giordano | Vitória M. S. Freitas | Gabriel R. Schleder | Murilo Santhiago | Ângelo L. Gobbi | Renato S. Lima
Source: ACS Applied Materials & Interfaces (article, 2021-07-26)
Field: Chemistry — Electrochemistry

Bifunctional Metal Meshes Acting as a Semipermeable Membrane and Electrode for Sensitive Electrochemical Determination of Volatile Compounds The monitoring of toxic inorganic gases and volatile organic compounds has brought the development of field-deployable, sensitive, and scalable sensors into focus. Here, we attempted to meet these requirements by using concurrently microhole-structured meshes as (i) a membrane for the gas diffusion extraction of an analyte from a donor sample and (ii) an electrode for the sensitive electrochemical determination of this target with the receptor electrolyte at rest. We used two types of meshes with complementary benefits, i.e., Ni mesh fabricated by robust, scalable, and well-established methods for manufacturing specific designs and stainless steel wire mesh (SSWM), which is commercially available at a low cost. The diffusion of gas (from a donor) was conducted in headspace mode, thus minimizing issues related to mesh fouling. When compared with the conventional polytetrafluoroethylene (PTFE) membrane, both the meshes (40 μm hole diameter) led to a higher amount of vapor collected into the electrolyte for subsequent detection. This inedited fashion produced a kind of reverse diffusion of the analyte dissolved into the electrolyte (receptor), i.e., from the electrode to bulk, which further enabled highly sensitive analyses. Using Ni mesh coated with Ni(OH)<sub>2</sub> nanoparticles, the limit of detection reached for ethanol was 24-fold lower than the data attained by a platform with a PTFE membrane and placement of the electrode into electrolyte bulk. This system was applied in the determination of ethanol in complex samples related to the production of ethanol biofuel. It is noteworthy that a simple equation fitted by machine learning was able to provide accurate assays (accuracies from 97 to 102%) by overcoming matrix effect-related interferences on detection performance. Furthermore, preliminary measurements demonstrated the successful coating of the meshes with gold films as an alternative raw electrode material and the monitoring of HCl utilizing Au-coated SSWMs. These strategies extend the applicability of the platform that may help to develop valuable volatile sensing solutions. Materials science ; Electrolyte ; Electrode ; Chemical engineering ; Membrane ; Gaseous diffusion ; Permeation ; Electrochemical gas sensor ; Electrochemistry ; Polygon mesh ; Nanotechnology ; Computer science ; Chemistry Electrochemical Analysis and Applications ; Analytical Chemistry and Sensors ; Advanced Chemical Sensor Technologies
Authors: Ke Ma | Boyu Qin | Xuewei Wang
Source: Electrochimica Acta (article, 2021-01-21)
Field: Materials Science — Polymers and Plastics

Understanding the Impedance of CNOs-Graphene hybrid electrode through both experimental and simulated electrochemical impedance spectrum Constant phase element ; Electrical impedance ; Capacitance ; Graphene ; Electrode ; Equivalent circuit ; Dielectric spectroscopy ; Materials science ; Transmission line ; Topology (electrical circuits) ; Analytical Chemistry (journal) ; Chemistry ; Electrochemistry ; Nanotechnology ; Physics ; Voltage ; Electrical engineering Conducting polymers and applications ; Electrochemical Analysis and Applications ; Supercapacitor Materials and Fabrication
Authors: Tobias Schäfer | Nathan Daelman | Núria López
Source: The Journal of Physical Chemistry Letters (article, 2021-07-02)
Field: Materials Science — Materials Chemistry

Cerium Oxides without <i>U</i>: The Role of Many-Electron Correlation Electron transfer with changing occupation in the 4f subshell poses a considerable challenge for quantitative predictions in quantum chemistry. Using the example of cerium oxide, we identify the main deficiencies of common parameter-dependent one-electron approaches, such as density functional theory (DFT) with a Hubbard correction, or hybrid functionals. As a response, we present the first benchmark of ab initio many-electron theory for electron transfer energies and lattice parameters under periodic boundary conditions. We show that the direct random phase approximation clearly outperforms all DFT variations. From this foundation, we, then, systematically improve even further. Periodic second-order Møller-Plesset perturbation theory meanwhile manages to recover standard hybrid functional values. Using these approaches to eliminate parameter bias allows for highly accurate benchmarks of strongly correlated materials, the reliable assessment of various density functionals, and functional fitting via machine-learning. Cerium ; Electron ; Correlation ; Materials science ; Psychology ; Physics ; Nuclear physics ; Mathematics ; Metallurgy Catalytic Processes in Materials Science ; Advanced Condensed Matter Physics ; ZnO doping and properties
Authors: Robin Lawler | Yao-Hao Liu | Nessa Majaya | Omar Allam | Hyunchul Ju | Jin Young Kim | Seung Soon Jang
Source: The Journal of Physical Chemistry A (article, 2021-09-23)
Field: Chemistry — Organic Chemistry

DFT-Machine Learning Approach for Accurate Prediction of p<i>K</i><sub>a</sub> In this study, we propose a novel method of p<i>K</i><sub>a</sub> prediction in a diverse set of acids, which combines density functional theory (DFT) method with machine learning (ML) methods. First, the DFT method with B3LYP/6-31++G**/SM8 is used to predict p<i>K</i><sub>a</sub>, yielding a mean absolute error of 1.85 p<i>K</i><sub>a</sub> units. Subsequently, such p<i>K</i><sub>a</sub> values predicted from the DFT method are employed as one of 10 molecular descriptors for developing ML models trained on experimental data. Kernel Ridge Regression (KRR), Gaussian Process Regression, and Artificial Neural Network are optimized using three <i>Pipelines</i>: <i>Pipeline 1</i> involving only hyperparameter optimization (HPO), <i>Pipeline 2</i> involving HPO followed by a relative contribution analysis (RCA) and recursive feature elimination (RFE), and <i>Pipeline 3</i> involving HPO followed by RCA and RFE on an expanded set of composite features. Finally, it is demonstrated that KRR with <i>Pipeline 3</i> yields optimal p<i>K</i><sub>a</sub> prediction at an MAE of 0.60 log units. This algorithm was then utilized to predict the p<i>K</i><sub>a</sub> of 37 novel acids. The two most important features were determined to be the number of hydrogen atoms in the molecule and the degree of oxidation of the acid. The predicted p<i>K</i><sub>a</sub> values were documented for future reference. Hyperparameter ; Pipeline (software) ; Artificial neural network ; Test set ; Artificial intelligence ; Kernel (algebra) ; Computer science ; Regression ; Kriging ; Machine learning ; Density functional theory ; Algorithm ; Chemistry ; Mathematics ; Computational chemistry ; Statistics ; Discrete mathematics Free Radicals and Antioxidants ; Computational Drug Discovery Methods ; Machine Learning in Materials Science
Authors: Claudio Zeni | Kevin Rossi | Aldo Glielmo | Stefano de Gironcoli
Source: The Journal of Chemical Physics (article, 2021-06-11)
Field: Materials Science — Materials Chemistry

Compact atomic descriptors enable accurate predictions via linear models We probe the accuracy of linear ridge regression employing a three-body local density representation derived from the atomic cluster expansion. We benchmark the accuracy of this framework in the prediction of formation energies and atomic forces in molecules and solids. We find that such a simple regression framework performs on par with state-of-the-art machine learning methods which are, in most cases, more complex and more computationally demanding. Subsequently, we look for ways to sparsify the descriptor and further improve the computational efficiency of the method. To this aim, we use both principal component analysis and least absolute shrinkage operator regression for energy fitting on six single-element datasets. Both methods highlight the possibility of constructing a descriptor that is four times smaller than the original with a similar or even improved accuracy. Furthermore, we find that the reduced descriptors share a sizable fraction of their features across the six independent datasets, hinting at the possibility of designing material-agnostic, optimally compressed, and accurate descriptors. Benchmark (surveying) ; Principal component analysis ; Computer science ; Linear regression ; Representation (politics) ; Regression ; Principal component regression ; Artificial intelligence ; Operator (biology) ; Algorithm ; Pattern recognition (psychology) ; Data mining ; Machine learning ; Mathematics ; Chemistry ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Samuel Boobier | Yufeng Liu | Krishna Sharma | David R. J. Hose | A. John Blacker | Nikil Kapur | Bao N. Nguyen
Source: Journal of Chemical Information and Modeling (article, 2021-09-22)
Field: Chemistry — Organic Chemistry

Predicting Solvent-Dependent Nucleophilicity Parameter with a Causal Structure Property Relationship Solvent-dependent reactivity is a key aspect of synthetic science, which controls reaction selectivity. The contemporary focus on new, sustainable solvents highlights a need for reactivity predictions in different solvents. Herein, we report the excellent machine learning prediction of the nucleophilicity parameter <i>N</i> in the four most-common solvents for nucleophiles in the Mayr's reactivity parameter database (<i>R</i><sup>2</sup> = 0.93 and 81.6% of predictions within ±2.0 of the experimental values with Extra Trees algorithm). A Causal Structure Property Relationship (CSPR) approach was utilized, with focus on the physicochemical relationships between the descriptors and the predicted parameters, and on rational improvements of the prediction models. The nucleophiles were represented with a series of electronic and steric descriptors and the solvents were represented with principal component analysis (PCA) descriptors based on the ACS Solvent Tool. The models indicated that steric factors do not contribute significantly, because of bias in the experimental database. The most important descriptors are solvent-dependent HOMO energy and Hirshfeld charge of the nucleophilic atom. Replacing DFT descriptors with Parameterization Method 6 (PM6) descriptors for the nucleophiles led to an 8.7-fold decrease in computational time, and an ∼10% decrease in the percentage of predictions within ±2.0 and ±1.0 of the experimental values. Nucleophile ; Steric effects ; Reactivity (psychology) ; Chemistry ; Solvent ; Solvent effects ; Computational chemistry ; Selectivity ; Principal component analysis ; Stereochemistry ; Organic chemistry ; Artificial intelligence ; Computer science Organic Chemistry Cycloaddition Reactions ; Chemical Reaction Mechanisms ; Computational Drug Discovery Methods
Authors: I-Jung Tsai | Emily Chia‐Yu Su | I‐Lin Tsai | Ching‐Yu Lin
Source: Cancers (article, 2021-05-02)
Field: Chemistry — Spectroscopy

Clinical Assay for the Early Detection of Colorectal Cancer Using Mass Spectrometric Wheat Germ Agglutinin Multiple Reaction Monitoring Colorectal cancer (CRC) is currently the third leading cause of cancer-related mortality in the world. U.S. Food and Drug Administration-approved circulating tumor markers, including carcinoembryonic antigen, carbohydrate antigen (CA) 19-9 and CA125 were used as prognostic biomarkers of CRC that attributed to low sensitivity in diagnosis of CRC. Therefore, our purpose is to develop a novel strategy for novel clinical biomarkers for early CRC diagnosis. We used mass spectrometry (MS) methods such as nanoLC-MS/MS, targeted LC-MS/MS, and stable isotope-labeled multiple reaction monitoring (MRM) MS coupled to test machine learning algorithms and logistic regression to analyze plasma samples from patients with early-stage CRC, late-stage CRC, and healthy controls (HCs). On the basis of our methods, 356 peptides were identified, 6 differential expressed peptides were verified, and finally three peptides corresponding wheat germ agglutinin (WGA)-captured proteins were semi-quantitated in 286 plasma samples (80 HCs and 206 CRCs). The novel peptide biomarkers combination of PF4<sup>54-62</sup>, ITIH4<sup>429-438</sup>, and APOE<sup>198-207</sup> achieved sensitivity 84.5%, specificity 97.5% and an AUC of 0.96 in CRC diagnosis. In conclusion, our study demonstrated that WGA-captured plasma PF4<sup>54-62</sup>, ITIH4<sup>429-438</sup>, and APOE<sup>198-207</sup> levels in combination may serve as highly effective early diagnostic biomarkers for patients with CRC. Carcinoembryonic antigen ; Wheat germ agglutinin ; Colorectal cancer ; Medicine ; Internal medicine ; Oncology ; Selected reaction monitoring ; Cancer ; Gastroenterology ; Mass spectrometry ; Immunology ; Tandem mass spectrometry ; Chemistry ; Lectin ; Chromatography Advanced Proteomics Techniques and Applications ; RNA and protein synthesis mechanisms ; Molecular Biology Techniques and Applications
Authors: Varun Chaudhary | Ravi Panwar
Source: IEEE Sensors Journal (article, 2021-09-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Neural Network Topology-Based Terahertz Absorber Using Fractal Frequency Selective Surface This work offers a new kind of polarization-insensitive, angular stable, tunable fractal frequency selective surface (FSS) based terahertz (THz) absorber. An equivalent circuit model (ECM) of the absorber is developed and impedance is obtained analytically. Further, an ECM-backed artificial neural network (ANN) approach is proposed and used to optimize the absorber to the desired frequency and bandwidth. The minimum mean square error between targeted and observed outputs is only <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.86\times10$ </tex-math></inline-formula> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-6</sup> . The maximum absorption of 99% is achieved at a central frequency of 1.5 THz. The tunability is accomplished by varying the chemical potential ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu _{c}$ </tex-math></inline-formula> ) of the graphene layer introduced in the proposed absorbing structure. The resonance frequency shifts to 1.66 THz from 1.39 THz with a change in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu _{c}$ </tex-math></inline-formula> from 0.3eV to 0.6eV.The dual-band absorption peaks are noticed with fractal FSS based absorber within the operating frequency range of 0.1-4.0 THz. The maximum absorption of 0.99 and 0.98 at the resonant frequency of 1.0 THz and 3.1 THz with fractal FSS, respectively is achieved. Furthermore, the designed absorber works well for both perpendicular and parallel polarizations up to 60 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> angle of incidence. Moreover, the effect of structural parameters is also observed on the equivalent inductance and capacitance to understand the physics behind the structure. All the results obtained from ANN are verified with particle swarm optimization (PSO) and Whale optimization (WO) techniques. Good congruence among ANN, PSO, and WO results shows the effectiveness of the aforementioned technique. The possible applications of the proposed tunable absorber are THz imaging, sensing, detection, and stealth. Fractal ; Terahertz radiation ; Topology (electrical circuits) ; Physics ; Materials science ; Algorithm ; Mathematics ; Optoelectronics ; Mathematical analysis ; Combinatorics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Christiane Schotten | Richard A. Bourne | Nikil Kapur | Bao N. Nguyen | Charlotte E. Willans
Source: Advanced Synthesis & Catalysis (article, 2021-04-21)
Field: Chemistry — Organic Chemistry

Electrochemical Generation of <i>N</i>‐Heterocyclic Carbenes for Use in Synthesis and Catalysis Abstract The electrochemical generation of N ‐heterocyclic carbenes (NHCs) offers a mild and selective alternative to traditional synthetic methods that usually rely on strong bases and air‐sensitive materials. The use of electrons as reagents results in an efficient and clean synthesis that enables the direct use of NHCs in various applications. Herein, the use of electrogenerated NHCs in organocatalysis, synthesis and organometallic chemistry is explored. magnified image Chemistry ; Electrochemistry ; Organocatalysis ; Reagent ; Catalysis ; Combinatorial chemistry ; Organic synthesis ; Organometallic chemistry ; Nanotechnology ; Organic chemistry ; Enantioselective synthesis ; Electrode N-Heterocyclic Carbenes in Organic and Inorganic Chemistry ; Catalytic Cross-Coupling Reactions ; Asymmetric Hydrogenation and Catalysis
Authors: Martha A. Arellano-Treviño | Danielle Bartholet | Anh T. To | Andrew Bartling | Frederick G. Baddour | Teresa L. Alleman | Earl Christensen | Gina M. Fioroni | Cameron Hays | Jon Luecke | Junqing Zhu | Charles S. McEnally | Lisa D. Pfefferle | Kenneth F. Reardon | Thomas D. Foust | Daniel A. Ruddy
Source: ACS Sustainable Chemistry & Engineering (article, 2021-04-27)
Field: Materials Science — Materials Chemistry

Synthesis of Butyl-Exchanged Polyoxymethylene Ethers as Renewable Diesel Blendstocks with Improved Fuel Properties Methyl-terminated polyoxymethylene ethers (MM-POMEs), having the formula CH3O–(CH2O)n–CH3 (n = 3–5), are a high-cetane, low-sooting group of oxygenates that have recently attracted attention as potential diesel blendstocks. Despite these attractive fuel properties, MM-POMEs have shortcomings due to their low energy density and high water solubility. Guided by a computational fuel property assessment for POMEs with longer end-groups, the most promising improvements in the desired compression ignition fuel properties were observed for butyl-terminated POMEs. Here, an acid-catalyzed transacetalization reaction was developed to exchange the methyl end-groups of MM-POMEs (n = 3–6) with butyl end-groups. The reaction utilizes an ion-exchange resin as the acid catalyst at mild reaction conditions of 60 °C and atmospheric pressure. Approximately 100 mL of butyl-exchanged POMEs in the diesel boiling range were produced, enabling laboratory-scale fuel property testing. The butyl-terminated POME mixture possesses the advantaged fuel properties of the parent MM-POMEs (low-soot, high-cetane) while exhibiting improved energy density (lower heating value (LHV) of 30 MJ/kg) and substantially reduced water solubility (7.3 g/L) compared to the parent MM-POME mixture (LHV of 19 MJ/kg , water solubility of 258 g/L). Oxygenate ; Cetane number ; Diesel fuel ; Solubility ; Catalysis ; Chemistry ; Materials science ; Chemical engineering ; Boiling point ; Organic chemistry ; Biodiesel Catalytic Processes in Materials Science ; Zeolite Catalysis and Synthesis ; Catalysis and Oxidation Reactions
Authors: Xu Zhang | Hoang T. Nguyen | Jeffrey T. Paci | Subramanian K. R. S. Sankaranarayanan | José L. Mendoza-Cortés | Horacio D. Espinosa
Source: npj Computational Materials (article, 2021-07-21)
Field: Materials Science — Materials Chemistry

Multi-objective parametrization of interatomic potentials for large deformation pathways and fracture of two-dimensional materials Abstract This investigation presents a generally applicable framework for parameterizing interatomic potentials to accurately capture large deformation pathways. It incorporates a multi-objective genetic algorithm, training and screening property sets, and correlation and principal component analyses. The framework enables iterative definition of properties in the training and screening sets, guided by correlation relationships between properties, aiming to achieve optimal parametrizations for properties of interest. Specifically, the performance of increasingly complex potentials, Buckingham, Stillinger-Weber, Tersoff, and modified reactive empirical bond-order potentials are compared. Using MoSe 2 as a case study, we demonstrate good reproducibility of training/screening properties and superior transferability. For MoSe 2 , the best performance is achieved using the Tersoff potential, which is ascribed to its apparent higher flexibility embedded in its functional form. These results should facilitate the selection and parametrization of interatomic potentials for exploring mechanical and phononic properties of a large library of two-dimensional and bulk materials. Parametrization (atmospheric modeling) ; Interatomic potential ; Flexibility (engineering) ; Transferability ; Deformation (meteorology) ; Statistical physics ; Computer science ; Molecular dynamics ; Materials science ; Fracture (geology) ; Physics ; Mathematics ; Computational chemistry ; Chemistry ; Machine learning ; Quantum mechanics 2D Materials and Applications ; Machine Learning in Materials Science ; Graphene research and applications