jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ali Davariashtiyani | Zahra Kadkhodaie | Sara Kadkhodaei
Source: Communications Materials (article, 2021-11-18)
Field: Materials Science — Materials Chemistry

Predicting synthesizability of crystalline materials via deep learning Abstract Predicting the synthesizability of hypothetical crystals is challenging because of the wide range of parameters that govern materials synthesis. Yet, exploring the exponentially large space of novel crystals for any future application demands an accurate predictive capability for synthesis likelihood to avoid a haphazard trial-and-error. Typically, benchmarks of synthesizability are defined based on the energy of crystal structures. Here, we take an alternative approach to select features of synthesizability from the latent information embedded in crystalline materials. We represent the atomic structure of crystalline materials by three-dimensional pixel-wise images that are color-coded by their chemical attributes. The image representation of crystals enables the use of a convolutional encoder to learn the features of synthesizability hidden in structural and chemical arrangements of crystalline materials. Based on the presented model, we can accurately classify materials into synthesizable crystals versus crystal anomalies across a broad range of crystal structure types and chemical compositions. We illustrate the usefulness of the model by predicting the synthesizability of hypothetical crystals for battery electrode and thermoelectric applications. Encoder ; Representation (politics) ; Computer science ; Crystal (programming language) ; Range (aeronautics) ; Materials science ; Artificial intelligence ; Pixel ; Chemical space ; Algorithm ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Pei‐Song Yang | Gang Lu | Qingyuan Yang | Lei Liu | Xin Lai | Duli Yu
Source: Green Energy & Environment (article, 2021-01-14)
Field: Chemistry — Inorganic Chemistry

Analyzing acetylene adsorption of metal–organic frameworks based on machine learning Metal–organic frameworks (MOFs) containing open metal sites are important materials for acetylene (C2H2) adsorption. However, it is inefficient or even impossible to search suitable MOFs by molecular simulation method in nearly infinite MOFs space. Therefore, machine learning (ML) methods are adopted in the material screening and prediction of high-performance MOFs. In this paper, architecture, chemical and structural features are used to analyze the C2H2 adsorption performance of the MOFs. Different ML algorithms are applied to perform classification and regression analysis to the factors affecting material adsorption. By decision tree (DT) algorithm, it is found that only PV, GSA, and Cu-OMS are sufficient to determine the high adsorption of the MOFs. Furthermore, the influence of topology on the performance of MOFs is obtained. Gradient Boosting Decision Tree (GBDT), Support Vector Machine (SVM), and Back Propagation Neural Network (BPNN), are introduced to analyze the quantitative structure–property relationship (QSPR) between C2H2 adsorption and the features of MOFs. The prediction of the GBDT model is found to have the highest accuracy, with R2 as 0.93 and RMSE as 11.58. In addition, the GBDT model is used for feature analysis, and the contribution of each feature to the performance is obtained, which is of great significance for the design and analysis of MOFs. The successful application of ML to MOFs screening greatly reduce the calculation time and provides important reference for the design and synthesis of new MOFs. Adsorption ; Support vector machine ; Acetylene ; Metal-organic framework ; Computer science ; Quantitative structure–activity relationship ; Decision tree ; Artificial neural network ; Feature (linguistics) ; Materials science ; Biological system ; Artificial intelligence ; Machine learning ; Pattern recognition (psychology) ; Chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Corrosion Behavior and Inhibition
Authors: Emily Mazeau | Priyanka Satpute | Katrín Blöndal | C. Franklin Goldsmith | Richard H. West
Source: ACS Catalysis (article, 2021-06-03)
Field: Materials Science — Materials Chemistry

Automated Mechanism Generation Using Linear Scaling Relationships and Sensitivity Analyses Applied to Catalytic Partial Oxidation of Methane Kinetic\nparameters for surface reactions can be predicted using\na combination of density functional theory calculations, scaling relations,\nand machine learning algorithms; however, construction of microkinetic\nmodels still requires a knowledge of all the possible, or at least\nreasonable, reaction pathways. The recently developed reaction mechanism\ngenerator (RMG) for heterogeneous catalysis, now included in RMG version\n3.0, is built upon well-established, open-source software that can\nprovide detailed reaction mechanisms from user-supplied initial conditions\nwithout making &lt;i&gt;a priori&lt;/i&gt; assumptions. RMG is now able\nto estimate adsorbate thermochemistry and construct detailed microkinetic\nmodels on a range of hypothetical metal surfaces using linear scaling\nrelationships. These relationships are a simple, computationally efficient\nway to estimate adsorption energies by scaling the energy of a calculated\nsurface species on one metal to any other metal. By conducting simulations\nwith sensitivity analyses, users can not only determine the rate-limiting\nstep on each surface by plotting a “volcano surface”\nfor the degree of rate control of each reaction as a function of elemental\nbinding energies but also screen novel catalysts for desirable properties.\nWe investigated the catalytic partial oxidation of methane to demonstrate\nthe utility of this new tool and determined that an inlet gas C/O\nratio of 0.8 on a catalyst with carbon and oxygen binding energies\nof −6.75 and −5.0 eV, respectively, yields the highest\namount of synthesis gas. Sensitivity analyses show that while the\ndissociative adsorption of O&lt;sub&gt;2&lt;/sub&gt; has the highest degree of\nrate control, the interactions between individual reactions and reactor\nconditions are complex, which result in a dynamic rate-limiting step\nacross differing metals. Catalysis ; Chemistry ; Scaling ; Thermochemistry ; Adsorption ; Methane ; Density functional theory ; Reaction rate ; Reaction mechanism ; Chemical physics ; Physical chemistry ; Computational chemistry ; Organic chemistry ; Mathematics Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Swarn Jha | Sourav Bandyopadhyay | Siddhi Mehta | Matthew Yen | Tariq Chagouri | Evan M. Palmer | Hong Liang
Source: Energy & Fuels (article, 2021-12-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Data-Driven Predictive Electrochemical Behavior of Lignin-Based Supercapacitors<i>via</i>Machine Learning Plant-derived lignin-based supercapacitors have shown strong potential as light and flexible electronic devices. To design a safe, reliable, and efficient supercapacitor, it is important to predict its electrochemical performance. To date, there is no reliable theoretical model to serve the purpose. In this work, four machine learning algorithms are developed and compared to predict the specific capacitance variation for lignin supercapacitors. Specifically, linear regression (LR), support vector machine (SVM), decision tree (DT), and artificial neural network (ANN) models are analyzed. Specific capacitance variation obtained by cyclic charge–discharge tests is modeled as a function of weight percentages of material constituents (lignin, nickel tungstate nanoparticles, and polyvinylidene fluoride) and the cycle number. The accuracy of the models is ranked as LR < SVM < DT < ANN and validated further using the F-test. The superior model fit of ANN shows the highest accuracy and applicability with excellent robustness. ANN can generalize the learned specific capacitance variation to material ratios excluded in the training set. This work provides an understanding of different machine learning techniques for predicting the specific capacitance variation and retention of lignin-based supercapacitors, demonstrating the strong potential of ANN to be used as a predictive tool to aid in supercapacitor optimization. Supercapacitor ; Capacitance ; Artificial neural network ; Support vector machine ; Materials science ; Lignin ; Biological system ; Robustness (evolution) ; Computer science ; Machine learning ; Artificial intelligence ; Chemistry ; Electrode Supercapacitor Materials and Fabrication ; Fuel Cells and Related Materials ; Electric and Hybrid Vehicle Technologies
Authors: Pascal Friederich | Mario Krenn | Isaac Tamblyn | Alán Aspuru‐Guzik
Source: Repository KITopen (Karlsruhe Institute of Technology) (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Scientific intuition inspired by machine learning-generated hypotheses Machine learning with application to questions in the physical sciences has become a widely used tool, successfully applied to classification, regression and optimization tasks in many areas. Research focus mostly lies in improving the accuracy of the machine learning models in numerical predictions, while scientific understanding is still almost exclusively generated by human researchers analysing numerical results and drawing conclusions. In this work, we shift the focus on the insights and the knowledge obtained by the machine learning models themselves. In particular, we study how it can be extracted and used to inspire human scientists to increase their intuitions and understanding of natural systems. We apply gradient boosting in decision trees to extract human-interpretable insights from big data sets from chemistry and physics. In chemistry, we not only rediscover widely know rules of thumb but also find new interesting motifs that tell us how to control solubility and energy levels of organic molecules. At the same time, in quantum physics, we gain new understanding on experiments for quantum entanglement. The ability to go beyond numerics and to enter the realm of scientific insight and hypothesis generation opens the door to use machine learning to accelerate the discovery of conceptual understanding in some of the most challenging domains of science. Artificial intelligence ; Computer science ; Realm ; Data science ; Intuition ; Gradient boosting ; Machine learning ; Cognitive science ; Random forest Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Chence Shi | Shitong Luo | Minkai Xu | Jian Tang
Source: arXiv (Cornell University) (preprint, 2021-05-09)
Field: Materials Science — Materials Chemistry

Learning Gradient Fields for Molecular Conformation Generation We study a fundamental problem in computational chemistry known as molecular conformation generation, trying to predict stable 3D structures from 2D molecular graphs. Existing machine learning approaches usually first predict distances between atoms and then generate a 3D structure satisfying the distances, where noise in predicted distances may induce extra errors during 3D coordinate generation. Inspired by the traditional force field methods for molecular dynamics simulation, in this paper, we propose a novel approach called ConfGF by directly estimating the gradient fields of the log density of atomic coordinates. The estimated gradient fields allow directly generating stable conformations via Langevin dynamics. However, the problem is very challenging as the gradient fields are roto-translation equivariant. We notice that estimating the gradient fields of atomic coordinates can be translated to estimating the gradient fields of interatomic distances, and hence develop a novel algorithm based on recent score-based generative models to effectively estimate these gradients. Experimental results across multiple tasks show that ConfGF outperforms previous state-of-the-art baselines by a significant margin. Computer science ; Langevin dynamics ; Molecular dynamics ; Translation (biology) ; Algorithm ; Field (mathematics) ; Statistical physics ; Force field (fiction) ; Artificial intelligence ; Physics ; Computational chemistry ; Chemistry ; Mathematics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Mass Spectrometry Techniques and Applications
Authors: Hideaki Mamada | Kazuhiko Iwamoto | Yukihiro Nomura | Yoshihiro Uesawa
Source: Molecular Diversity (article, 2021-02-10)
Field: Chemistry — Analytical Chemistry

Predicting blood-to-plasma concentration ratios of drugs from chemical structures and volumes of distribution in humans Despite their importance in determining the dosing regimen of drugs in the clinic, only a few studies have investigated methods for predicting blood-to-plasma concentration ratios (Rb). This study established an Rb prediction model incorporating typical human pharmacokinetics (PK) parameters. Experimental Rb values were compiled for 289 compounds, offering reliable predictions by expanding the applicability domain. Notably, it is the largest list of Rb values reported so far. Subsequently, human PK parameters calculated from plasma drug concentrations, including the volume of distribution (Vd), clearance, mean residence time, and plasma protein binding rate, as well as 2702 kinds of molecular descriptors, were used to construct quantitative structure-PK relationship models for Rb. Among the evaluated PK parameters, logVd correlated best with Rb (correlation coefficient of 0.47). Thus, in addition to molecular descriptors selected by XGBoost, logVd was employed to construct the prediction models. Among the analyzed algorithms, artificial neural networks gave the best results. Following optimization using six molecular descriptors and logVd, the model exhibited a correlation coefficient of 0.64 and a root-mean-square error of 0.205, which were superior to those previously reported for other Rb prediction methods. Since Vd values and chemical structures are known for most medications, the Rb prediction model described herein is expected to be valuable in clinical settings. Molecular descriptor ; Applicability domain ; Quantitative structure–activity relationship ; Correlation coefficient ; Chemistry ; Volume of distribution ; Pharmacokinetics ; Mathematics ; Statistics ; Bioinformatics ; Stereochemistry ; Biology Analytical Methods in Pharmaceuticals ; Computational Drug Discovery Methods ; Pharmacogenetics and Drug Metabolism
Authors: Gerardo Campos-Villalobos | Emanuele Boattini | Laura Filion | Marjolein Dijkstra
Source: The Journal of Chemical Physics (article, 2021-10-11)
Field: Materials Science — Materials Chemistry

Machine learning many-body potentials for colloidal systems Simulations of colloidal suspensions consisting of mesoscopic particles and smaller species such as ions or depletants are computationally challenging as different length and time scales are involved. Here, we introduce a machine learning (ML) approach in which the degrees of freedom of the microscopic species are integrated out and the mesoscopic particles interact with effective many-body potentials, which we fit as a function of all colloid coordinates with a set of symmetry functions. We apply this approach to a colloid-polymer mixture. Remarkably, the ML potentials can be assumed to be effectively state-independent and can be used in direct-coexistence simulations. We show that our ML method reduces the computational cost by several orders of magnitude compared to a numerical evaluation and accurately describes the phase behavior and structure, even for state points where the effective potential is largely determined by many-body contributions. Mesoscopic physics ; Colloid ; Symmetry (geometry) ; Function (biology) ; Degrees of freedom (physics and chemistry) ; Phase (matter) ; Statistical physics ; Set (abstract data type) ; Biological system ; Physics ; Computer science ; Chemical physics ; Chemistry ; Mathematics ; Condensed matter physics ; Quantum mechanics ; Geometry ; Physical chemistry Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Protein Structure and Dynamics
Authors: Marcus Wieder | Josh Fass | John D. Chodera
Source: Chemical Science (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Fitting quantum machine learning potentials to experimental free energy data: predicting tautomer ratios in solution The computation of tautomer ratios of druglike molecules is enormously important in computer-aided drug discovery, as over a quarter of all approved drugs can populate multiple tautomeric species in solution. Unfortunately, accurate calculations of aqueous tautomer ratios-the degree to which these species must be penalized in order to correctly account for tautomers in modeling binding for computer-aided drug discovery-is surprisingly difficult. While quantum chemical approaches to computing aqueous tautomer ratios using continuum solvent models and rigid-rotor harmonic-oscillator thermochemistry are currently state of the art, these methods are still surprisingly inaccurate despite their enormous computational expense. Here, we show that a major source of this inaccuracy lies in the breakdown of the standard approach to accounting for quantum chemical thermochemistry using rigid rotor harmonic oscillator (RRHO) approximations, which are frustrated by the complex conformational landscape introduced by the migration of double bonds, creation of stereocenters, and introduction of multiple conformations separated by low energetic barriers induced by migration of a single proton. Using quantum machine learning (QML) methods that allow us to compute potential energies with quantum chemical accuracy at a fraction of the cost, we show how rigorous relative alchemical free energy calculations can be used to compute tautomer ratios in vacuum free from the limitations introduced by RRHO approximations. Furthermore, since the parameters of QML methods are tunable, we show how we can train these models to correct limitations in the underlying learned quantum chemical potential energy surface using free energies, enabling these methods to learn to generalize tautomer free energies across a broader range of predictions. Tautomer ; Quantum chemical ; Energy (signal processing) ; Quantum ; Computer science ; Artificial intelligence ; Computational chemistry ; Statistical physics ; Algorithm ; Machine learning ; Chemistry ; Physics ; Quantum mechanics ; Stereochemistry ; Molecule Machine Learning in Materials Science ; Protein Structure and Dynamics ; Spectroscopy and Quantum Chemical Studies
Authors: Peter S. Rice | Zhi‐Pan Liu | P. Hu
Source: The Journal of Physical Chemistry Letters (article, 2021-10-27)
Field: Materials Science — Materials Chemistry

Hydrogen Coupling on Platinum Using Artificial Neural Network Potentials and DFT To date, the understanding of reactions at solid-liquid interfaces has proven challenging, mainly because of the inaccessible nature of such systems to current experimental techniques with atomic resolution. This has meant that many important features, including free energy barriers and the atomistic structure of intermediates, remain unknown. To tackle these issues, we construct and utilize a high-dimensional neural network (HDNN) potential for the simulation of hydrogen evolution at the HCl(aq)/Pt(111) interface, taking into consideration the influence of adsorbate-adsorbate, adsorbate-solvent interactions, and ion solvation explicitly. Long time scale MD simulations reveal coadsorbed H<sub>ad</sub>/H<sub>2</sub>O<sub>ad</sub> on the surface. The free energy profiles for the Tafel and Heyrovsky type hydrogen coupling are extracted using umbrella sampling. It is found that the preferential mechanism can change depending on the surface coverage, highlighting the dual mechanistic nature for HER on Pt(111). Our work demonstrates the importance of controlling the solvent-substrate interactions in developing catalysts beyond Pt. Solvation ; Chemistry ; Chemical physics ; Platinum ; Coupling (piping) ; Tafel equation ; Hydrogen ; Solvent ; Catalysis ; Implicit solvation ; Computational chemistry ; Materials science ; Physical chemistry ; Electrochemistry ; Electrode ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: Jakob Timmermann | Yonghyuk Lee | Carsten G. Staacke | Johannes T. Margraf | Christoph Scheurer | Karsten Reuter
Source: The Journal of Chemical Physics (article, 2021-11-10)
Field: Materials Science — Materials Chemistry

Data-efficient iterative training of Gaussian approximation potentials: Application to surface structure determination of rutile IrO2 and RuO2 Machine-learning interatomic potentials, such as Gaussian Approximation Potentials (GAPs), constitute a powerful class of surrogate models to computationally involved first-principles calculations. At a similar predictive quality but significantly reduced cost, they could leverage otherwise barely tractable extensive sampling as in global surface structure determination (SSD). This efficiency is jeopardized though, if an a priori unknown structural and chemical search space as in SSD requires an excessive number of first-principles data for the GAP training. To this end, we present a general and data-efficient iterative training protocol that blends the creation of new training data with the actual surface exploration process. Demonstrating this protocol with the SSD of low-index facets of rutile IrO<sub>2</sub> and RuO<sub>2</sub>, the involved simulated annealing on the basis of the refining GAP identifies a number of unknown terminations even in the restricted sub-space of (1 × 1) surface unit cells. Particularly in an O-poor environment, some of these, then metal-rich terminations, are thermodynamically most stable and are reminiscent of complexions as discussed for complex ceramic materials. Leverage (statistics) ; Computer science ; Gaussian ; Simulated annealing ; Rutile ; Interatomic potential ; Algorithm ; Machine learning ; Molecular dynamics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Bryn Crawford | Reza Sourki | Hamid Khayyam | Abbas S. Milani
Source: Computers in Industry (article, 2021-07-23)
Field: Materials Science — Materials Chemistry

A machine learning framework with dataset-knowledgeability pre-assessment and a local decision-boundary crispness score: An industry 4.0-based case study on composite autoclave manufacturing Context (archaeology) ; Computer science ; Machine learning ; Process (computing) ; Artificial intelligence ; Domain (mathematical analysis) ; Pipeline (software) ; Engineering ; Manufacturing engineering ; Data mining ; Mechanical engineering Machine Learning in Materials Science ; Industrial Vision Systems and Defect Detection ; Manufacturing Process and Optimization
Authors: V. Ajesh Kumar | Prem Prakash Srivastav | M. Pravitha | Muzaffar Hasan | S. Mangaraj | V. Prithviraj | Deepak Kumar Verma
Source: Food Packaging and Shelf Life (article, 2021-12-29)
Field: Materials Science — Biomaterials

Comparative study on the optimization and characterization of soybean aqueous extract based composite film using response surface methodology (RSM) and artificial neural network (ANN) Response surface methodology ; Beeswax ; Ultimate tensile strength ; Central composite design ; Composite number ; Materials science ; Aqueous solution ; Solubility ; Composite material ; Chromatography ; Chemistry ; Wax ; Organic chemistry Nanocomposite Films for Food Packaging ; Postharvest Quality and Shelf Life Management ; Food Chemistry and Fat Analysis