jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ziyang Zhang | Yingtao Luo
Source: arXiv (Cornell University) (preprint, 2020-11-14)
Field: Materials Science — Materials Chemistry

Deep Spatial Learning with Molecular Vibration Machine learning over-fitting caused by data scarcity greatly limits the application of machine learning for molecules. Due to manufacturing processes difference, big data is not always rendered available through computational chemistry methods for some tasks, causing data scarcity problem for machine learning algorithms. Here we propose to extract the natural features of molecular structures and rationally distort them to augment the data availability. This method allows a machine learning project to leverage the powerful fit of physics-informed augmentation for providing significant boost to predictive accuracy. Successfully verified by the prediction of rejection rate and flux of thin film polyamide nanofiltration membranes, with the relative error dropping from 16.34% to 6.71% and the coefficient of determination rising from 0.16 to 0.75, the proposed deep spatial learning with molecular vibration is widely instructive for molecular science. Experimental comparison unequivocally demonstrates its superiority over common learning algorithms. Vibration ; Computer science ; Artificial intelligence ; Geography ; Acoustics ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Force Microscopy Techniques and Applications
Authors: Ömer Tayfuroğlu | Müslüm Yıldız | Lee-Wright Pearson | Abdulkadır Kocak
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-05-28)
Field: Materials Science — Materials Chemistry

An Accurate Free Energy Method for Solvation of Organic Compounds and Binding to Proteins ABSTRACT Here, we introduce a new strategy to estimate free energies using single end-state molecular dynamics simulation trajectories. The method is adopted from ANI-1ccx neural network potentials (Machine Learning) for the Atomic Simulation Environment (ASE) and predicts the single point energies at the accuracy of CCSD(T)/CBS level for the entire configurational space that is sampled by Molecular Dynamics (MD) simulations. Our preliminary results show that the method can be as accurate as Bennet-Acceptance-Ration (BAR) with much reduced computational cost. Not only does it enable to calculate solvation free energies of small organic compounds, but it is also possible to predict absolute and relative binding free energies in ligand-protein complex systems. Rapid calculation also enables to screen small organic molecules from databases as potent inhibitors to any drug targets. Solvation ; Molecular dynamics ; Implicit solvation ; Organic molecules ; Bar (unit) ; Artificial neural network ; Thermodynamic integration ; Chemistry ; Energy (signal processing) ; Statistical physics ; Molecule ; Computational chemistry ; Computer science ; Biological system ; Physics ; Artificial intelligence ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Jyri Kimari | Ville Jansson | Simon Vigonski | Ekaterina Baibuz | Roberto Pinheiro Domingos | Vahur Zadin | Flyura Djurabekova
Source: Computational Materials Science (preprint, 2020-06-06)
Field: Materials Science — Materials Chemistry

Application of artificial neural networks for rigid lattice kinetic Monte Carlo studies of Cu surface diffusion Kinetic Monte Carlo (KMC) is a powerful method for simulation of diffusion processes in various systems. The accuracy of the method, however, relies on the extent of details used for the parameterization of the model. Migration barriers are often used to describe diffusion on atomic scale, but the full set of these barriers may become easily unmanageable in materials with increased chemical complexity or a large number of defects. This work is a feasibility study for applying a machine learning approach for Cu surface diffusion. We train an artificial neural network on a subset of the large set of 226 barriers needed to correctly describe the surface diffusion in Cu. Our KMC simulations using the obtained barrier predictor show sufficient accuracy in modelling processes on the low-index surfaces and display the correct thermodynamical stability of these surfaces. Kinetic Monte Carlo ; Monte Carlo method ; Artificial neural network ; Statistical physics ; Diffusion ; Lattice (music) ; Surface diffusion ; Surface (topology) ; Computer science ; Kinetic energy ; Work (physics) ; Materials science ; Artificial intelligence ; Algorithm ; Thermodynamics ; Physics ; Chemistry ; Mathematics ; Physical chemistry ; Classical mechanics ; Geometry Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Microstructure and mechanical properties
Authors: Hillary Pan | Alex M. Ganose | Matthew K. Horton | Muratahan Aykol | Kristin A. Persson | Nils Zimmermann | Anubhav Jain
Source: ChemRxiv (preprint, 2020-06-22)
Field: Materials Science — Materials Chemistry

Benchmarking Coordination Number Prediction Algorithms on Inorganic Crystal Structures Coordination numbers and geometries form a theoretical framework for understanding and predicting materials properties. Algorithms to determine coordination numbers automatically are increasingly used for machine learning and automatic structural analysis. In this work, we introduce MaterialsCoord, a benchmark suite containing 56 experimentally-derived crystal structures (spanning elements, binaries, and ternary compounds) and their corresponding coordination environments as described in the research literature. We also describe CrystalNN, a novel algorithm for determining near neighbors. We compare CrystalNN against 7 existing near-neighbor algorithms on the MaterialsCoord benchmark, finding CrystalNN to be the most accurate overall. For each algorithm, we also assess computational demand and sensitivity towards small perturbations that mimic thermal motion. Finally, we investigate the similarity between bonding algorithms when applied to the Materials Project database. We expect that this work will aid the development of coordination prediction algorithms and improve the accuracy of structural descriptors for machine learning and other applications. Benchmarking ; Benchmark (surveying) ; Computer science ; Suite ; Algorithm ; Similarity (geometry) ; Machine learning ; Artificial intelligence ; Data mining Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Hillary Pan | Alex M. Ganose | Matthew K. Horton | Muratahan Aykol | Kristin A. Persson | Nils Zimmermann | Anubhav Jain
Source: ChemRxiv (article, 2020-06-22)
Field: Materials Science — Materials Chemistry

Benchmarking Coordination Number Prediction Algorithms on Inorganic Crystal Structures Coordination numbers and geometries form a theoretical framework for understanding and predicting materials properties. Algorithms to determine coordination numbers automatically are increasingly used for machine learning and automatic structural analysis. In this work, we introduce MaterialsCoord, a benchmark suite containing 56 experimentally-derived crystal structures (spanning elements, binaries, and ternary compounds) and their corresponding coordination environments as described in the research literature. We also describe CrystalNN, a novel algorithm for determining near neighbors. We compare CrystalNN against 7 existing near-neighbor algorithms on the MaterialsCoord benchmark, finding CrystalNN to perform similarly to several well-established algorithms. For each algorithm, we also assess computational demand and sensitivity towards small perturbations that mimic thermal motion. Finally, we investigate the similarity between bonding algorithms when applied to the Materials Project database. We expect that this work will aid the development of coordination prediction algorithms as well as improve structural descriptors for machine learning and other applications. Benchmark (surveying) ; Benchmarking ; Computer science ; Suite ; Algorithm ; Similarity (geometry) ; Ternary operation ; Artificial intelligence ; Machine learning ; Data mining ; Image (mathematics) Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Timur Gimadiev | Ramil Nugmanov | Dinar Batyrshin | Timur Madzhidov | Satoshi Maeda | Pavel Sidorov | Alexandre Varnek
Source: ChemRxiv (preprint, 2020-10-23)
Field: Materials Science — Materials Chemistry

Combined Graph/relational Database Management System for Calculated Chemical Reaction Pathway Data Nowadays quantum chemical calculations are widely used to generate extensive datasets for machine learning applications, however, generally these sets only include information on equilibrium structures and some close conformers. Exploration of potential energy surface provides an important information on ground and transition states, but analysis of such data is complicated due to the number of possible reaction pathways. Here, we present RePathDB, a database system for managing 3D structural data for both ground and transition states resulted from quantum chemical calculations. Our tool allows to store, to assemble and to analyze reaction pathway data. It combines relational database CGR DB for handling compounds and reactions as molecular graphs with a graph database architecture for the pathway analysis by graph algorithms. Original Condensed Graph of Reaction Technology is used to store any chemical reaction as a single graph. Graph database ; Computer science ; Graph ; Transition state ; Relational database ; Quantum chemical ; Theoretical computer science ; Potential energy surface ; Relational database management system ; Database ; Chemistry ; Data mining ; Molecule ; Catalysis ; Organic chemistry Machine Learning in Materials Science ; Data Quality and Management ; Graph Theory and Algorithms
Authors: Thanh-Dung Le | Rita Noumeir | Huu Luong Quach | Ji Hyung Kim | Jung Ho Kim | Ho Min Kim
Source: arXiv (Cornell University) (preprint, 2020-01-29)
Field: Materials Science — Materials Chemistry

Critical Temperature Prediction for a Superconductor: A Variational\n Bayesian Neural Network Approach Much research in recent years has focused on using empirical machine learning\napproaches to extract useful insights on the structure-property relationships\nof superconductor material. Notably, these approaches are bringing extreme\nbenefits when superconductivity data often come from costly and arduously\nexperimental work. However, this assessment cannot be based solely on an open\nblack-box machine learning, which is not fully interpretable, because it can be\ncounter-intuitive to understand why the model may give an appropriate response\nto a set of input data for superconductivity characteristic analyses, e.g.,\ncritical temperature. The purpose of this study is to describe and examine an\nalternative approach for predicting the superconducting transition temperature\n$T_c$ from SuperCon database obtained by Japan's National Institute for\nMaterials Science. We address a generative machine-learning framework called\nVariational Bayesian Neural Network using superconductors chemical elements and\nformula to predict $T_c$. In such a context, the importance of the paper in\nfocus is twofold. First, to improve the interpretability, we adopt a\nvariational inference to approximate the distribution in latent parameter space\nfor the generative model. It statistically captures the mutual correlation of\nsuperconductor compounds and; then, gives the estimation for the $T_c$. Second,\na stochastic optimization algorithm, which embraces a statistical inference\nnamed Monte Carlo sampler, is utilized to optimally approximate the proposed\ninference model, ultimately determine and evaluate the predictive performance.\n Machine learning ; Interpretability ; Artificial intelligence ; Inference ; Computer science ; Artificial neural network ; Bayesian inference ; Context (archaeology) ; Generative model ; Generative grammar ; Bayesian probability Machine Learning in Materials Science ; Topic Modeling ; X-ray Diffraction in Crystallography
Authors: 伸宏 中村 | Nobuhiro Nakamura | 朋史 多田 | Tomofumi Tada (1674688) | 秀雄 細野 | HIDEO HOSONO
Source: Institutional Repositories DataBase (IRDB) (article, 2020-10-01)
Field: Materials Science — Materials Chemistry

How fluorine minimizes density fluctuations of silica glass: Molecular dynamics study with machine-learning assisted force-matching potential Molecular dynamics ; Work (physics) ; Fluorine ; Dynamics (music) Material Dynamics and Properties ; Glass properties and applications ; Advanced Physical and Chemical Molecular Interactions
Authors: Roy R. Lederman
Source: cIRcle (University of British Columbia) (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

On the Continuum Between Models, Data-Driven Discovery and Machine Learning: Mapping the Continuum of Molecular Conformations Using Cryo-Electron Microscopy Cryo-electron microscopy ; Microscopy ; Physics ; Computer science ; Artificial intelligence ; Nuclear magnetic resonance ; Optics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Yijie Xu | Yun Zong | Kedar Hippalgaonkar
Source: arXiv (Cornell University) (preprint, 2020-03-10)
Field: Materials Science — Materials Chemistry

Machine-learning assisted cross-domain prediction of ionic conductivity in sodium and lithium-based superionic conductors Solid state lithium- and sodium-ion batteries utilize solid ionicly conducting compounds as electrolytes. However, the ionic conductivity of such materials tends to be lower than their liquid counterparts, necessitating research efforts into finding suitable alternatives. The process of electrolyte screening is often based on a mixture of domain expertise and trial-and-error, both of which are time and resource-intensive. Data-driven and machine learning approaches have recently come to the fore to accelerate learnings towards discovery. In this work, we present a simple machine-learning based approach to predict the ionic conductivity of sodium and lithium-based SICON compounds. Using primarily theoretical elemental feature descriptors derivable from tabulated information on the unit cell and the atomic properties of the components of a target compound on a limited dataset of 70 NASICON-examples, we have designed a logistic regression-based model capable of distinguishing between poor and good superionic conductors with a cross-validation accuracy of over 82%. Moreover, we demonstrate how such a system is capable of cross-domain classification on lithium-based examples at the same accuracy, despite being introduced to zero lithium-based compounds during training. Through a systematic permutation-based evaluation process, we reduced the number of considered features from 47 to 7, reduction of over 83%, while simultaneously improving model performance. The contributions of different electronic and structural features to overall ionic conductivity is also discussed, and contrasted with accepted theories in literature. Our results demonstrate the utility of such a simple, yet interpretable tool provides opportunities for initial screening of potential candidates as solid-state electrolytes through the use of existing data. Lithium (medication) ; Ionic conductivity ; Fast ion conductor ; Electrolyte ; Conductivity ; Computer science ; Domain (mathematical analysis) ; Machine learning ; Ionic bonding ; Materials science ; Artificial intelligence ; Chemistry ; Ion ; Mathematics ; Physical chemistry Machine Learning in Materials Science
Authors: 裕之 寺前 | 哲秀 松尾 | 一眞 庭月野 | 竜太 井上 | 晋治 野口 | 美燕 玄 | 司 山下 | 淳 高山 | 真理 岡﨑 | 武史 坂本
Source: Institutional Repositories DataBase (IRDB) (article, 2020-10-01)
Field: Materials Science — Materials Chemistry

分子軌道エネルギーと機械学習による分子物性の予測 The values of the internuclear distances and the dipole moments of 14 small molecules have been estimated by machine learning with only molecular orbital energies as the explanatory variables. We use four regression methods, partial least square (PLS), random forest (RF), Radial Basis Function Kernel Regularized Least Squares (krlsRadial), and Baysian Regularized Neural Networks (BRNN) and we report only BRNN results for the internuclear distances, and PLS results for the dipole moments. The coefficients of determination for the internulear distances and the dipole moments are 0.9318 and 0.7265, respectively. It has been proved that the internuclear distances and the dipole moments can be predicted by the molecular orbital energies only. Dipole ; Kernel (algebra) ; Moment (physics) ; Function (biology) ; Square (algebra) ; Partial least squares regression ; Basis (linear algebra) Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Advanced Chemical Physics Studies
Authors: Kevin Maik Jablonka | Daniele Ongari | Seyed Mohamad Moosavi | Berend Smit
Source: ChemRxiv (preprint, 2020-01-17)
Field: Materials Science — Materials Chemistry

Using Collective Knowledge to Assign Oxidation States &lt;div&gt;&lt;div&gt;&lt;div&gt;&lt;p&gt;Knowledge of the oxidation state of a metal centre in a material is essential to understand its properties. Chemists have developed several theories to predict the oxidation state on the basis of the chemical formula. These methods are quite successful for simple compounds but often fail to describe the oxidation states of more complex systems, such as metal-organic frameworks. In this work, we present a data-driven approach to automatically assign oxidation states, using a machine learning algorithm trained on the assignments by chemists encoded in the chemical names in the Cambridge Crystallographic Database. Our approach only considers the immediate local chemical environment around a metal centre and, in this way, is robust to most of the experimental uncertainties in these structures (like incorrect protonation or unbound solvents). We find such excellent accuracy (&gt; 98 %) in our predictions that we can use our method to identify a large number of incorrect assignments in the database. The predictions of our model follow chemical intuition, without explicitly having taught the model those heuristics. This work nicely illustrates how powerful the collective knowledge of chemists actually is. Machine learning can harvest this knowledge and convert it into a useful tool for chemists.&lt;/p&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt; Heuristics ; Intuition ; Protonation ; Computer science ; Chemistry ; Artificial intelligence ; Organic chemistry ; Ion ; Cognitive science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Šević Dragutin | Vlašić Ana | Rabasović Maja S. | Savić-Šević Svetlana | Rabasović Mihailo D. | Nikolić Marko G. | Murić Branka D. | Marinković Bratislav P. | Janez Križan
Source: SHILAP Revista de lepidopterología (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Temperature effects on luminescent properties of Sr2CeO4:Eu3+ nanophosphor: A machine learning approach In this paper we analyze possibilities of application of Sr2CeO4:Eu3+ nanopowder for temperature sensing using machine learning. The material was prepared by simple solution combustion synthesis. Photoluminescence technique has been used to measure the optical emission temperature dependence of the prepared material. Principal Component Analysis, the basic machine learning algorithm, provided insight into temperature dependent spectral data from another point of view than usual approach. Luminescence ; Materials science ; Computer science ; Environmental science ; Engineering physics ; Optoelectronics ; Physics Luminescence Properties of Advanced Materials ; Radiation Detection and Scintillator Technologies ; Solid-state spectroscopy and crystallography
Authors: Adam M. Krajewski | Jonathan W. Siegel | Jinchao Xu | Zi‐Kui Liu
Source: arXiv (Cornell University) (preprint, 2020-08-31)
Field: Materials Science — Materials Chemistry

Extensible Structure-Informed Prediction of Formation Energy with Improved Accuracy and Usability employing Neural Networks In the present paper, we introduce a new neural network-based tool for the prediction of formation energies of atomic structures based on elemental and structural features of Voronoi-tessellated materials. We provide a concise overview of the connection between the machine learning and the true material-property relationship, how to improve the generalization accuracy by reducing overfitting, how new data can be incorporated into the model to tune it to a specific material system, and preliminary results on using models to preform local structure relaxations. The present work resulted in three final models optimized for (1) highest test accuracy on the Open Quantum Materials Database (OQMD), (2) performance in the discovery of new materials, and (3) performance at a low computational cost. On a test set of 21,800 compounds randomly selected from OQMD, they achieve a mean absolute error (MAE) of 28, 40, and 42 meV/atom, respectively. The second model provides better predictions in a test case of interest not present in the OQMD, while the third reduces the computational cost by a factor of 8. We collect our results in a new open-source tool called SIPFENN (Structure-Informed Prediction of Formation Energy using Neural Networks). SIPFENN not only improves the accuracy beyond existing models but also ships in a ready-to-use form with pre-trained neural networks and a GUI interface. By virtue of this, it can be included in DFT calculations routines at nearly no cost. Overfitting ; Computer science ; Usability ; Artificial neural network ; Extensibility ; Generalization ; Voronoi diagram ; Atom (system on chip) ; Artificial intelligence ; Test set ; Machine learning ; Energy (signal processing) ; Set (abstract data type) ; Data mining ; Algorithm ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Marco Bragato | Guido Falk von Rudorff | O. Anatole von Lilienfeld
Source: arXiv (Cornell University) (preprint, 2020-04-30)
Field: Materials Science — Materials Chemistry

Data Enhanced Reaction Predictions in Chemical Space With Hammett's Equation By separating the effect of substituents from chemical process variables, such as reaction mechanism, solvent, or temperature, the Hammett equation enables control of chemical reactivity throughout chemical space. We used global regression to optimize Hammett parameters $ρ$ and $σ$ in two datasets, experimental rate constants for benzylbromides reacting with thiols and the decomposition of ammonium salts, and a synthetic dataset consisting of computational activation energies of $\sim$ 1400 $S_N2$ reactions, with various nucleophiles and leaving groups (-H, -F, -Cl, -Br) and functional groups (-H, -NO$_2$, -CN, -NH$_3$, -CH$_3$). The original approach is generalized to predict potential energies of activation in non aromatic molecular scaffolds with multiple substituents. Individual substituents contribute additively to molecular $σ$ with a unique regression term, which quantifies the inductive effect. Moreover, the position dependence of the substituent can be replaced by a distance decaying factor for $S_N2$. Use of the Hammett equation as a base-line model for $Δ$-Machine learning models of the activation energy in chemical space results in substantially improved learning curves for small training set sizes. Hammett equation ; Substituent ; Chemistry ; Chemical space ; Nucleophile ; Sigma ; Reaction rate constant ; Chemical reaction ; Computational chemistry ; Activation energy ; Reactivity (psychology) ; Solvent effects ; Solvent ; Thermodynamics ; Physical chemistry ; Stereochemistry ; Organic chemistry ; Quantum mechanics ; Physics ; Kinetics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis