jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sachit Mishra | Rajat Srivastava | Atta Muhammad | Amit Amit | Eliodoro Chiavazzo | Matteo Fasano | Pietro Asinari
Source: arXiv (Cornell University) (preprint, 2022-08-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

The impact of physicochemical features of carbon electrodes on the capacitive performance of supercapacitors: A machine learning approach Hybrid electric vehicles and portable electronic systems use supercapacitors for energy storage owing to their fast charging discharging rates, long life cycle, and low maintenance. Specific capacitance is regarded as one of the most important performance-related characteristics of a supercapacitor's electrode. In the current study, Machine Learning (ML) algorithms were used to determine the impact of various physicochemical properties of carbon-based materials on the capacitive performance of electric double-layer capacitors. Published experimental datasets from 147 references (4899 data entries) were extracted and then used to train and test the ML models, to determine the relative importance of electrode material features on specific capacitance. These features include current density, pore volume, pore size, presence of defects, potential window, specific surface area, oxygen, and nitrogen content of the carbon-based electrode material. Additionally, categorical variables as the testing method, electrolyte, and carbon structure of the electrodes are considered as well. Among five applied regression models, an extreme gradient boosting model was found to best correlate those features with the capacitive performance, highlighting that the specific surface area, the presence of nitrogen doping, and the potential window are the most significant descriptors for the specific capacitance. These findings are summarized in a modular and open-source application for estimating the capacitance of supercapacitors given, as only inputs, the features of their carbon-based electrodes, the electrolyte and testing method. In perspective, this work introduces a new wide dataset of carbon electrodes for supercapacitors extracted from the experimental literature, also giving an instance of how electrochemical technology can benefit from ML models. Supercapacitor ; Capacitive sensing ; Materials science ; Electrode ; Carbon fibers ; Nanotechnology ; Computer science ; Electrochemistry ; Composite material ; Engineering ; Electrical engineering ; Chemistry ; Composite number Supercapacitor Materials and Fabrication ; Fuel Cells and Related Materials
Authors: Chao Han | Hao Wang | Zhu Jian-bao | Qi Liu | Zhu Wenguang
Source: Chinese Journal of Chemical Physics (article, 2022-09-16)
Field: Materials Science — Materials Chemistry

Comparison of multi-task approaches on molecular property prediction With the bloom of deep learning algorithms, various models have been widely utilized in quantum chemistry calculation to design new molecules and explore molecular properties. However, limited studies focus on multi-task molecular property prediction, which offers more efficient ways to simultaneously learn different but related properties by leveraging the inter-task relationship. In this work, we apply the hard parameter sharing framework and advanced loss weighting methods to multi-task molecular property prediction. Based on the performance comparison between single-task baseline and multi-task models on several task sets, we find that the prediction accuracy largely depends on the inter-task relationship, and hard parameter sharing improves the performance when the correlation becomes complex. In addition, we show that proper loss weighting methods help achieve more balanced multi-task optimization and enhance the prediction accuracy. Our additional experiments on varying amount of training data further validate the multi-task advantages and show that multi-task models with proper loss weighting methods can achieve more accurate prediction of molecular properties with much less computational cost. Weighting ; Computer science ; Task (project management) ; Property (philosophy) ; Artificial intelligence ; Machine learning ; Data mining ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Tianchen Zhao | James Stokes | Shravan Veerapaneni
Source: arXiv (Cornell University) (preprint, 2022-08-11)
Field: Materials Science — Materials Chemistry

Scalable neural quantum states architecture for quantum chemistry Variational optimization of neural-network representations of quantum states has been successfully applied to solve interacting fermionic problems. Despite rapid developments, significant scalability challenges arise when considering molecules of large scale, which correspond to non-locally interacting quantum spin Hamiltonians consisting of sums of thousands or even millions of Pauli operators. In this work, we introduce scalable parallelization strategies to improve neural-network-based variational quantum Monte Carlo calculations for ab-initio quantum chemistry applications. We establish GPU-supported local energy parallelism to compute the optimization objective for Hamiltonians of potentially complex molecules. Using autoregressive sampling techniques, we demonstrate systematic improvement in wall-clock timings required to achieve CCSD baseline target energies. The performance is further enhanced by accommodating the structure of resultant spin Hamiltonians into the autoregressive sampling ordering. The algorithm achieves promising performance in comparison with the classical approximate methods and exhibits both running time and scalability advantages over existing neural-network based methods. Scalability ; Computer science ; Quantum ; Pauli exclusion principle ; Artificial neural network ; Ab initio ; Quantum computer ; Spin (aerodynamics) ; Statistical physics ; Computational science ; Physics ; Quantum mechanics ; Artificial intelligence Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Quantum Computing Algorithms and Architecture
Authors: Marek Gąsiorowski | Piotr Szymak | A. Patryn | Krzysztof Naus
Source: Electronics (article, 2022-06-22)
Field: Chemistry — Analytical Chemistry

Monitoring Time-Non-Stable Surfaces Using Mobile NIR DLP Spectroscopy In recent years, Near Infrared (NIR) spectroscopy has increased in popularity and usage for different purposes, including the detection of particular substances, evaluation of food quality, etc. Usually, mobile handheld NIR spectroscopy devices are used on the surfaces of different materials, very often organic ones. The features of these materials change as they age, leading to changes in their spectra. The ageing process often occurs only slowly, i.e., corresponding reflection spectra can be analyzed each hour or at an even longer interval. This paper undertakes the problem of analyzing surfaces of non-stable, rapidly changing materials such as waxes or adhesive materials. To obtain their characteristic spectra, NIR spectroscopy using a Digital Light Projection (DLP) spectrometer was used. Based on earlier experiences and the current state of the art, Artificial Neural Networks (ANNs) were used to process spectral sequences to proceed with an enormous value of spectra gathered during measurements. Near-infrared spectroscopy ; Spectroscopy ; Materials science ; Spectrometer ; Spectral line ; Mobile device ; Optics ; Computer science ; Analytical Chemistry (journal) ; Chemistry ; Physics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Kirill Shmilovich | Devin Willmott | Ivan Batalov | Mordechai Kornbluth | Jonathan P. Mailoa | J. Zico Kolter
Source: Journal of Chemical Theory and Computation (article, 2022-09-19)
Field: Materials Science — Materials Chemistry

Orbital Mixer: Using Atomic Orbital Features for Basis-Dependent Prediction of Molecular Wavefunctions Leveraging ab initio data at scale has enabled the development of machine learning models capable of extremely accurate and fast molecular property prediction. A central paradigm of many previous studies focuses on generating predictions for only a fixed set of properties. Recent lines of research instead aim to explicitly learn the electronic structure via molecular wavefunctions, from which other quantum chemical properties can be directly derived. While previous methods generate predictions as a function of only the atomic configuration, in this work we present an alternate approach that directly purposes basis-dependent information to predict molecular electronic structure. Our model, Orbital Mixer, is composed entirely of multi-layer perceptrons (MLPs) using MLP-Mixer layers within a simple, intuitive, and scalable architecture that achieves competitive Hamiltonian and molecular orbital energy and coefficient prediction accuracies compared to the state-of-the-art. Wave function ; Computer science ; Molecular orbital ; Scalability ; Hamiltonian (control theory) ; Ab initio ; Electronic structure ; Basis (linear algebra) ; Perceptron ; Statistical physics ; Artificial intelligence ; Artificial neural network ; Computational chemistry ; Physics ; Chemistry ; Molecule ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Chen Qu | Qi Yu | Paul L. Houston | Riccardo Conte | Apurba Nandi | Joel M. Bowman
Source: Research Square (preprint, 2022-07-19)
Field: Materials Science — Materials Chemistry

Many-body Δ-Machine Learning brings theaccuracy of conventional force field to coupledcluster: application to the TTM2.1 waterforce field Cluster (spacecraft) ; Field (mathematics) ; Force field (fiction) ; Body force ; Computer science ; Artificial intelligence ; Physics ; Classical mechanics ; Mathematics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Nuclear Physics and Applications
Authors: Olga Sarmanova | Kirill Laptinskiy | Sergey Burikov | S. A. Dolenko | Daria B. Trushina | Tatiana A. Dolenko
Source: Optical Memory and Neural Networks (article, 2022-09-01)
Field: Materials Science — Materials Chemistry

Decoding Optical Spectra with Neural Networks to Monitor the Elimination of Carbon Nanoagents from the Body This study is devoted to the problem of controlling the removal of carbon nanocomplexes from the body via neural networks. We model the renal excretion of nanocomplexes based on carbon dots (CD) that deliver anticancer drug—doxorubicin (Dox). Optical absorption and fluorescence spectra of nanocomposites containing CD and Dox in urine were used to model the process of elimination of nanocomposites components from the body. These spectral data were decoded with multilayer perceptrons to determine CD and Dox concentrations in the studied samples. To increase the accuracy of monitoring the excretion of CD and Dox with urine, principal component analysis was additionally used. Our sensing technique allows controlling the excretion of CD and Dox with 43.8 and 20.9 µg/L mean absolute errors, which are comparable to similar values of analogues. However, the proposed method can be used for simultaneous rapid monitoring of numerous substances, while most CD-based sensors in literature are calibrated to measure only one or two parameters. The obtained results have great value for the analysis of the parameters of nanostructured objects in microvolumes, particularly for monitoring in microfluidic devices. Combination of microfluidics and the use of machine learning methods shows a future innovative approach for conduction massive biological material study and implement smart decision making systems in support of clinical diagnostics. Multilayer perceptron ; Materials science ; Computer science ; Artificial neural network ; Biological system ; Nanotechnology ; Biomedical engineering ; Artificial intelligence ; Medicine ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Shanshan Li | Fang Bian | Xinge Wu | Lele Sun | Hongwei Yang | Xiangying Meng | Gaowu Qin
Source: Materials (article, 2022-05-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Microstructure Evolution and Its Correlation with Performance in Nitrogen-Containing Porous Carbon Prepared by Polypyrrole Carbonization: Insights from Hybrid Calculations The preparation of nitrogen-containing porous carbon (NCPC) materials by controlled carbonization is an exciting topic due to their high surface area and good conductivity for use in the fields of electrochemical energy storage and conversion. However, the poor controllability of amorphous porous carbon prepared by carbonization has always been a tough problem due to the unclear carbonation mechanism, which thus makes it hard to reveal the microstructure-performance relationship. To address this, here, we comprehensively employed reactive molecular dynamics (ReaxFF-MD) simulations and first-principles calculations, together with machine learning technologies, to clarify the carbonation process of polypyrrole, including the deprotonation and formation of pore structures with temperature, as well as the relationship between microstructure, conductance, and pore size. This work constructed ring expressions for PPy thermal conversion at the atomic level. It revealed the structural factors that determine the conductivity and pore size of carbonized products. More significantly, physically interpretable machine learning models were determined to quantitatively express structure factors and performance structure-activity relationships. Our study also confirmed that deprotonation preferentially occurred by desorbing the dihydrogen atom on nitrogen atoms during the carbonization of PPy. This theoretical work clearly reproduces the microstructure evolution of polypyrrole on an atomic scale that is hard to do via experimentation, thus paving a new way to the design and development of nitrogen-containing porous carbon materials with controllable morphology and performance. Carbonization ; Polypyrrole ; Microstructure ; Materials science ; Porosity ; Nitrogen ; Carbon fibers ; Chemical engineering ; Composite material ; Nanotechnology ; Organic chemistry ; Composite number ; Polymerization ; Scanning electron microscope ; Polymer ; Chemistry ; Engineering Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Fuel Cells and Related Materials
Authors: Shuang Tang | Jenna Jean-Baptiste | Schuyler Vecchiano | Adam Lukasiewicz | Alexandria Burger
Source: Engineered Science (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning to Predict the L-Point Direct Bandgap of Bi1-xSbx Nanomaterials With the development of modern nanoscience and nanotechnology, Bi 1-x Sb x can be synthesized into different nanoscale and nanostructured forms, including thin films, nanowires, nanotubes, nanoribbons, and many others.However, due to the strong correlation between electrons and holes at the L-point in the Brillouin zone, the direct band evolves in an anomalous manner under the quantum confinement when nanostructured.Due to the alloying and the low symmetry, predicting the L-point direct bandgap in a nanomaterial using either ab initio calculations or k•p perturbations can be computationally costive or inaccurate.We here try to solve this problem using the machine learning methods, including the support vector regression, the regression tree, the Gaussian process regression, and the artificial neural network.A goodness-of-fit of ~0.99 can be achieved for Bi 1-x Sb x thin films and nanowires. Nanomaterials ; Brillouin zone ; Band gap ; Materials science ; Nanowire ; Condensed matter physics ; Nanoscopic scale ; Percolation (cognitive psychology) ; Nanotechnology ; Support vector machine ; Quantum dot ; Ab initio ; Artificial neural network ; Statistical physics ; Machine learning ; Physics ; Computer science ; Optoelectronics ; Quantum mechanics Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Catalysis and Oxidation Reactions
Authors: Jim Boelrijk | Saer Samanipour | Denice van Herwerden | Bernd Ensing | Patrick Forré
Source: ChemRxiv (preprint, 2022-05-19)
Field: Chemistry — Spectroscopy

Predicting RP-LC retention indices of structurallyunknown chemicals from mass spectrometry data Non-target analysis combined with high resolution mass spectrometry is considered one of the most comprehensive strategies for the detection and identification of known and unknown chemicals in complex samples. However, many compounds remain unidentified due to data complexity and limited structures in chemical databases. In this work, we have developed and validated a novel machine learning algorithm to predict the retention index (r$_i$) values for structurally (un)known chemicals based on their measured fragmentation pattern. The developed model, for the first time, enabled the predication of r$_i$ values without the need for the exact structure of the chemicals, with an $R^2$ of 0.91 and 0.77 and root mean squared error (RMSE) of 47 and 67 r$_i$ units for the Norman and amide test set, respectively. This fragment based model showed comparable accuracy in r$_i$ prediction compared to conventional descriptor-based models that rely on known chemical structure, which obtained a $R^2$ of 0.85 with and RMSE of 67. Mean squared error ; Mass spectrometry ; Fragmentation (computing) ; High resolution ; Chemistry ; Kovats retention index ; Resolution (logic) ; Test set ; Data set ; Mathematics ; Chromatography ; Artificial intelligence ; Computer science ; Statistics ; Gas chromatography Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies ; Computational Drug Discovery Methods
Authors: Fang Wu | Stan Z. Li
Source: arXiv (Cornell University) (preprint, 2022-04-19)
Field: Materials Science — Materials Chemistry

DiffMD: A Geometric Diffusion Model for Molecular Dynamics Simulations Molecular dynamics (MD) has long been the de facto choice for simulating complex atomistic systems from first principles. Recently deep learning models become a popular way to accelerate MD. Notwithstanding, existing models depend on intermediate variables such as the potential energy or force fields to update atomic positions, which requires additional computations to perform back-propagation. To waive this requirement, we propose a novel model called DiffMD by directly estimating the gradient of the log density of molecular conformations. DiffMD relies on a score-based denoising diffusion generative model that perturbs the molecular structure with a conditional noise depending on atomic accelerations and treats conformations at previous timeframes as the prior distribution for sampling. Another challenge of modeling such a conformation generation process is that a molecule is kinetic instead of static, which no prior works have strictly studied. To solve this challenge, we propose an equivariant geometric Transformer as the score function in the diffusion process to calculate corresponding gradients. It incorporates the directions and velocities of atomic motions via 3D spherical Fourier-Bessel representations. With multiple architectural improvements, we outperform state-of-the-art baselines on MD17 and isomers of C7O2H10 datasets. This work contributes to accelerating material and drug discovery. Molecular dynamics ; Computer science ; Force field (fiction) ; Statistical physics ; Computation ; Algorithm ; Physics ; Artificial intelligence ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Advanced NMR Techniques and Applications
Authors: Wataru TAKAHARA | Hiroshi OSAWA | Ren OKADA
Source: Journal of Computer Chemistry Japan (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Evaluating the Impact of Scaling Considering the Extrapolation Domain on the Prediction Performance of Machine Learning Algorithms In this study, we used a benchmark dataset to evaluate the impact of scaling with the extrapolation domain on the prediction performance of machine learning algorithms. We pseudo-divided the data into the interpolation domain (training data) and the extrapolation domain (test data) using a combination of UMAP (Uniform Manifold Approximation and Projection) and material domain knowledge. In anticipation of bridging interpolation and extrapolation domains in nonlinear machine learning algorithms, we evaluated how the scaling considering the extrapolation domain affects prediction performance in the extrapolation domain. For this evaluation, we used three nonlinear algorithms widely used in the MI (Materials Informatics) domain: XGB (XGBoost) regression, GP (Gaussian Process) regression, and SVR (Support Vector Regression). In this study, by defining the pseudo extrapolation domain, we established the approach for evaluating the prediction accuracy of machine learning models in the extrapolation domain, which is considered difficult to evaluate quantitatively. We also demonstrated that this method, which uses scaling that considers the extrapolation domain, is an effective method for improving prediction accuracy in the extrapolation domain while maintaining prediction accuracy in the interpolation domain. Extrapolation ; Interpolation (computer graphics) ; Computer science ; Support vector machine ; Algorithm ; Machine learning ; Domain (mathematical analysis) ; Artificial intelligence ; Scaling ; Mathematics ; Statistics Machine Learning in Materials Science ; Neural Networks and Applications ; X-ray Diffraction in Crystallography