jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Guido Falk von Rudorff | Stefan Heinen | Marco Bragato | O. Anatole von Lilienfeld
Source: arXiv (Cornell University) (preprint, 2020-05-31)
Field: Materials Science — Materials Chemistry

Thousands of reactants and transition states for competing E2 and\n S$_\\text{N}$2 reactions Reaction barriers are a crucial ingredient for first principles based\ncomputational retro-synthesis efforts as well as for comprehensive reactivity\nassessments throughout chemical compound space. While extensive databases of\nexperimental results exist, modern quantum machine learning applications\nrequire atomistic details which can only be obtained from quantum chemistry\nprotocols. For competing E2 and S$_\\text{N}$2 reaction channels we report 4'466\ntransition state and 143'200 reactant complex geometries and energies at\nrespective MP2/6-311G(d) and single point DF-LCCSD/cc-pVTZ level of theory\ncovering the chemical compound space spanned by the substituents NO$_2$, CN,\nCH$_3$, and NH$_2$ and early halogens (F, Cl, Br) as nucleophiles and leaving\ngroups. Reactants are chosen such that the activation energy of the competing\nE2 and S$_\\text{N}$2 reactions are of comparable magnitude. The correct\nconcerted motion for each of the one-step reactions has been validated for all\ntransition states. We demonstrate how quantum machine learning models can\nsupport data set extension, and discuss the distribution of key internal\ncoordinates of the transition states.\n Transition state ; Halogen ; Nucleophile ; Reactivity (psychology) ; Chemical space ; Chemistry ; Reaction coordinate ; Space (punctuation) ; Quantum ; Computational chemistry ; Quantum chemistry ; Transition state theory ; Catalysis ; Reaction mechanism ; Statistical physics ; Chemical physics ; Physics ; Computer science ; Quantum mechanics ; Reaction rate constant ; Organic chemistry ; Kinetics ; Drug discovery Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; History and advancements in chemistry
Authors: Scott E. Denmark | Andrew F. Zahrt | William Darrow | Brennan Rose | Jeremy Henle
Source: ChemRxiv (preprint, 2020-11-16)
Field: Materials Science — Materials Chemistry

Computational Methods for Training Set Selection and Error Assessment Applied to Catalyst Design: Guidelines for Deciding Which Reactions to Run First and Which to Run Next The application of machine learning (ML) to problems in homogeneous catalysis has emerged as a promising avenue for catalyst optimization. An important aspect of such optimization campaigns is determining which reactions to run at the outset of experimentation and which future predictions are the most reliable. Herein, we explore methods for these two tasks in the context of our previously developed chemoinformatics workflow. First, different methods for training set selection are compared, including algorithmic selection and selection informed by unsupervised learning methods. Next, an array of different metrics for assessment of prediction confidence are examined in multiple catalyst manifolds. These approaches will inform future computer-guided studies to accelerate catalyst selection and reaction optimization. Finally, this work demonstrates the generality of the Average Steric Occupancy (ASO) and Average Electronic Indicator Field (AEIF) descriptors in their application to transition metal catalysts for the first time. &lt;br&gt; Cheminformatics ; Generality ; Workflow ; Selection (genetic algorithm) ; Computer science ; Context (archaeology) ; Set (abstract data type) ; Field (mathematics) ; Machine learning ; Artificial intelligence ; Homogeneous ; Database ; Chemistry ; Mathematics Machine Learning in Materials Science ; Topic Modeling ; Advanced Graph Neural Networks
Authors: Lucas J. Stanek | Shaunak D. Bopardikar | Michael S. Murillo
Source: APS Division of Plasma Physics Meeting Abstracts (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Multi-Fidelity Machine Learning for Extending the Range of High-Fidelity Molecular Dynamics Data Fidelity ; Computer science ; Range (aeronautics) ; Dynamics (music) ; High fidelity ; Artificial intelligence ; Engineering ; Physics ; Aerospace engineering ; Telecommunications Machine Learning in Materials Science
Authors: Nikolay Borodinov | Wan‐Yu Tsai | Vladimir V. Korolkov | Nina Balke | Sergei V. Kalinin | Olga S. Ovchinnikova
Source: ChemRxiv (preprint, 2020-01-09)
Field: Materials Science — Materials Chemistry

Machine Learning-Based Multidomain Processing for Texture-Based Image Segmentation and Analysis &lt;a&gt;Atomic and molecular resolved atomic force microscopy (AFM) images &lt;/a&gt;offer unique insights into materials properties such as local ordering, molecular orientation and topological defects, which can be used to pinpoint physical and chemical interactions occurring at the surface. Utilizing machine learning for extracting underlying physical parameters increases the throughput of AFM data processing and eliminates inconsistencies intrinsic to manual image analysis thus enabling the creation of reliable frameworks for qualitative and quantitative evaluation of experimental data. Here, we present a robust and scalable approach to the segmentation of AFM images based on flexible pre-selected classification criteria. Usage of supervised learning and feature extraction allows to retain the consideration of specific problem-dependent features (such as types of periodical structure observed in the images and the associated numerical parameters: spacing, orientation, etc.). We highlight the applicability of this approach for segmentation of molecular resolved AFM images based on crystal orientation of observed domains, automated selection of boundaries and collection of relevant statistics. Overall, we outline a general strategy for machine learning-enabled analysis of nanoscale systems exhibiting periodic order that could be applied to any analytical imaging technique. Segmentation ; Orientation (vector space) ; Computer science ; Artificial intelligence ; Scalability ; Texture (cosmology) ; Atomic force microscopy ; Image processing ; Pattern recognition (psychology) ; Feature extraction ; Image segmentation ; Nanoscopic scale ; Machine learning ; Image (mathematics) ; Materials science ; Nanotechnology ; Mathematics ; Geometry Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Electron and X-Ray Spectroscopy Techniques
Authors: Chi Hao Liow | Seungbum Hong | Moony Na | Kihoon Bang | Hyuck Mo Lee | Hye Ryung Byon | Yoonsu Shim | Jong Min Yuk | Arthur Baucour | Jonghwa Shin | EunAe Cho | Yongju Lee | Hyeonmuk Kang | Seongwoo Cho | Gun Park | Jiwon Yeom
Source: ECS Meeting Abstracts (article, 2020-11-23)
Field: Materials Science — Materials Chemistry

Data-Driven Synthesis Optimization of Cathode Materials Machine intelligence-based optimizing synthesis parameters offers exciting opportunities in various domain of materials chemistry where large and comprehensive dataset are available. Aiming to construct a “design-to-device” pipeline for battery application with small discrepancy, we adapt machine learning model based on actual experimental data from curated literatures to predict the discharge capacity of Nickel-Manganese-Cobalt (NCM) cathode material. However, missing data is an inherent problem from the results based on literature. Herein we introduce imputation techniques to fill the missing data based. To this end, we show that our model have remarkable predictive capability with the average coefficient of determinant, R 2 = 0.84 for training sets. In addition, we also validate our model prediction by the experiments, which yields in less than 8% error. The present work highlights a promising data driven materials synthesis approach, which underpins nonlinear structure-properties relationship for broad range of materials development. Computer science ; Pipeline (software) ; Missing data ; Imputation (statistics) ; Nonlinear system ; Data mining ; Machine learning ; Artificial intelligence Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Electron and X-Ray Spectroscopy Techniques
Authors: Richard L. Apodaca
Source: Front Matter (article, 2020-09-14)
Field: Materials Science — Materials Chemistry

Getting Started with DeepChem Like many fields, chemistry is in the midst of a machine learning transformation. Chemistry also has some peculiarities that make getting started with machine learning a challenge. What would be helpful is a workbench that makes it possible to conduct simple but illustrative studies with minimal ceremony. DeepChem is a batteries-included suite that seeks to fill this need. Suite ; Workbench ; Simple (philosophy) ; Feature (linguistics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Scientific Computing and Data Management
Authors: Kominami
Source: The Chemical Society of Japan (article, 2020-02-21)
Field: Chemistry — Inorganic Chemistry

Machine-Learning-Assisted Exploration of Synthesis Condition of Novel MOFs Containing In-S Bonds Bond ; Computer science ; Chemistry ; Business Metal-Organic Frameworks: Synthesis and Applications ; Polyoxometalates: Synthesis and Applications ; Organometallic Compounds Synthesis and Characterization
Authors: Muneera Alsaeed | Haikel Alhichri
Source:  (article, 2020-10-13)
Field: Chemistry — Analytical Chemistry

A Convolutional Neural Network Solution for Spectroscopic Data Regression This work deals with the problem of predicting chemical information from different materials using spectroscopic data. This is part of a field of study called chemometrics, which combines chemistry with informatics. In pattern recognition, this kind of problem is known as multivariate regression. In this work, we propose a convolutional neural network (CNN) that combines global and local features of the spectroscopic signal. The motivation behind this method is that convolutional layers in CNN provide localized features only because the filters have a limited width (such as 3×3 or 5×5). However, global features are also important in learning the regression function. The proposed CNN is composed of two branches one branch learns global features from the signal while the second branch learns local features using convolutional layers. The two branches are combined at the end of the deep network using a concatenation operation. The preliminary results presented on two chemometric datasets show clearly the potential of the proposed deep learning method. Concatenation (mathematics) ; Convolutional neural network ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Deep learning ; Multivariate statistics ; Field (mathematics) ; Regression ; Chemometrics ; Machine learning ; Algorithm ; Mathematics ; Statistics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Jianxing Huang | Linfeng Zhang | Handong Wang | Jinbao Zhao | Jun Cheng | E Weinan
Source: arXiv (Cornell University) (article, 2020-06-05)
Field: Materials Science — Materials Chemistry

Automatic machine-learning potential generation scheme and simulation protocol for the LiGePS-type superionic conductors It has been a challenge to accurately simulate Li-ion diffusion processes in battery materials at room temperature using {\it ab initio} molecular dynamics (AIMD) due to its high computational cost. This situation has changed drastically in recent years due to the advances in machine learning-based interatomic potentials. Here we implement the Deep Potential Generator scheme to \textit{automatically} generate interatomic potentials for LiGePS-type solid-state electrolyte materials. This increases our ability to simulate such materials by several orders of magnitude without sacrificing {\it ab initio} accuracy. Important technical aspects like the statistical error and size effects are carefully investigated. We further establish a reliable protocol for accurate computation of Li-ion diffusion processes at experimental conditions, by investigating important technical aspects like the statistical error and size effects. Such a protocol and the automated workflow allow us to screen materials for their relevant properties with much-improved efficiency. By using the protocol and automated workflow developed here, we obtain the diffusivity data and activation energies of Li-ion diffusion that agree well with the experiment. Our work paves the way for future investigation of Li-ion diffusion mechanisms and optimization of Li-ion conductivity of solid-state electrolyte materials. Diffusion ; Ab initio ; Computer science ; Fast ion conductor ; Ion ; Workflow ; Protocol (science) ; Thermal diffusivity ; Electrolyte ; Battery (electricity) ; Materials science ; Chemistry ; Thermodynamics ; Physics ; Power (physics) ; Physical chemistry Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Materials and Technologies
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. Computer science ; Graph database ; Graph ; Transition state ; Quantum chemical ; Relational database ; Theoretical computer science ; Chemistry ; Data mining ; Database ; Molecule ; Catalysis ; Organic chemistry Machine Learning in Materials Science ; Data Quality and Management ; Graph Theory and Algorithms
Authors: Richard L. Apodaca
Source: Front Matter (article, 2020-03-09)
Field: Materials Science — Materials Chemistry

A Brief Introduction to Graph Convolutional Networks The extensive use of graphs in chemistry to model both reactions and molecules creates challenges for machine learning. Most of the previous and ongoing research in the field solves the problem by translating molecular graphs into forms that fit cleanly within existing paradigms and tools: images; text; and binary fingerprints. Graph representations themselves seem out-of-place at first. Graph ; Binary number ; Graph theory ; Binary relation ; Field (mathematics) ; Graph product Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Graph Theory and Algorithms
Authors: David Zagaceta | Howard Yanxon | Qiang Zhu
Source: Journal of Applied Physics (article, 2020-08-18)
Field: Materials Science — Materials Chemistry

Publisher’s Note: “Spectral neural network potentials for binary alloys” [J. Appl. Phys 128, 045113 (2020)] First Page Binary number ; Artificial neural network ; Statistical physics ; Physics ; Materials science ; Computer science ; Mathematics ; Artificial intelligence ; Arithmetic Machine Learning in Materials Science
Authors: Pablo M. Piaggi | Roberto Car
Source: Bulletin of the American Physical Society (article, 2020-03-04)
Field: Materials Science — Materials Chemistry

A molecular dynamics study of water crystallization using deep neural network potentials of ab-initio quality Crystallization ; Ab initio ; Artificial neural network ; Quality (philosophy) ; Molecular dynamics ; Dynamics (music) ; Materials science ; Computational chemistry ; Chemical physics ; Computer science ; Biological system ; Artificial intelligence ; Chemistry ; Thermodynamics ; Physics ; Organic chemistry ; Quantum mechanics ; Biology Crystallization and Solubility Studies ; Neural Networks and Applications ; Spectroscopy and Chemometric Analyses
Authors: Jacqueline F. Hamilton
Source: White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (article, 2020-04-14)
Field: Chemistry — Spectroscopy

A new approach combining molecular fingerprints and machine learning to estimate relative ionization efficiency in electrospray ionization Electrospray ionization ; Ionization ; Computer science ; Ambient ionization ; Artificial intelligence ; Chemistry ; Machine learning ; Mass spectrometry ; Chromatography ; Chemical ionization ; Ion Mass Spectrometry Techniques and Applications ; Advanced Chemical Sensor Technologies ; Analytical Chemistry and Chromatography
Authors: Anas Karuth | Amirhadi Alesadi | Wenjie Xia | Bakhtiyor Rasulev
Source:  (article, 2020-05-08)
Field: Materials Science — Materials Chemistry

Predicting the Glass Transition Temperature of Amorphous Polymers via Integration of Cheminformatics and Molecular Dynamics Simulations Glass transition temperature Tg is one of the most important thermophysical properties of amorphous polymers. The substantial change in polymer dynamics at glass-transition temperature causes a major change in physical properties, including mechanical modulus, density, specific heat, damping characteristics, dielectric properties of the polymer[1-5]. The cheminformatics approach based on machine learning algorithms is often applied to predict the quantitative relationships between key molecular descriptors and the glass transition temperature Tg of investigated polymers. In this work, we discussing an innovative modeling framework by integrating cheminformatics and coarse-grained molecular dynamics simulations to predict Tg of diverse set of more than hundred polymers[6-10]. This synergistic approach provides valuable insights into the roles of key molecular features (i.e., cohesive interactions, chain stiffness, and topology) influencing the of polymers, paving the way to establish a materials-by-design framework for polymeric materials [11-12]. By harnessing the power of this unprecedented computational efficiency provided by this novel framework, we can successfully predict properties of not only the polymeric materials, but also other classes of organic and inorganic materials Glass transition ; Cheminformatics ; Molecular dynamics ; Polymer ; Amorphous solid ; Materials science ; Work (physics) ; Chemical physics ; Nanotechnology ; Thermodynamics ; Chemistry ; Computational chemistry ; Composite material ; Physics ; Organic chemistry Machine Learning in Materials Science ; Polymer crystallization and properties ; Material Dynamics and Properties
Authors: Song Wang
Source: eScholarship (California Digital Library) (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Computational Insights into Porous Materials for Gas Separation Membrane-based materials are an important branch in the field of gas separation. There are increasing number of membrane materials which are synthesized in recent years. Among them, the novel ultrathin membranes with uniform pores like nanoporous graphene attracted attention of many researchers, because their excellent gas separation ability based on molecular sieving effect. Several factors might influence the performance of ultrathin membranes, including pore size, pore shape, pore density, etc. However, the theoretical understanding of these factors has not been clearly addressed. In this dissertation, we firstly investigated the optimal pore size by first-principles density functional theory and simulated the pore-size effect for post-combustion carbon capture by grand canonical Monte Carlo. Next, we used molecular dynamics simulations to study the effect of pore density on the nanoporous graphene membrane. Then, we proposed a bilayer design of nanoporous graphene membrane with continuously tunable effective pore size for gas separations, such as CO2/CH4, N2/CH4, O2/N2. Meanwhile, we studied the effect of entropic selectivity by tracking the trajectories of gas-permeance events. After that, we proposed other design of graphene/ionic-liquid composites with tunable slit pore size. Finally, to satisfy the requirement of exploring huge material database for gas separation, we applied machine learning to predict the selectivity of porous carbon materials. We further used convolutional neural networks to search the optimal porous carbon material from an ultimate input feature. The same approach was also used for prediction of hydrogen locations in copper clusters, which are potential materials for hydrogen storage. The works in this dissertation aims to find and design ultrathin nanoporous materials for gas separation by different computational approaches. Permeance ; Nanoporous ; Gas separation ; Graphene ; Materials science ; Membrane ; Porosity ; Nanotechnology ; Porous medium ; Chemical engineering ; Composite material ; Chemistry Catalytic Processes in Materials Science ; Phase Equilibria and Thermodynamics ; Membrane Separation and Gas Transport
Authors: Andrew E. Sifain | Samuel H. Yalkowsky | Betsy M. Rice | Brian M. Barnes
Source: ChemRxiv (preprint, 2020-03-09)
Field: Materials Science — Materials Chemistry

Machine Learning Transition Temperatures from 2D Structure A priori knowledge of melting and boiling could expedite the discovery of pharmaceutical, energetic, and energy harvesting materials. The tools of data science are becoming increasingly important for exploring chemical datasets and predicting material properties. A fundamental part of data-driven modeling is molecular featurization. Herein, we propose a molecular representation with group-constitutive and geometrical descriptors that map to enthalpy and entropy--two thermodynamic quantities that drive thermal phase transitions. The descriptors are inspired by the linear regression-based quantitative structure-property relationship of Yalkowsky and coworkers known as the Unified Physicochemical Property Estimation Relationships (UPPER). Combined with nonlinear machine learning (specifically, eXtreme Gradient Boosting or XGBoost), these concise and easy-to-compute descriptors provide an appealing framework for predicting transition enthalpies, entropies, and temperatures in a diverse chemical space. An application to energetic materials shows that UPPER plus XGBoost is predictive, despite a relatively modest energetics reference dataset. We also report results on public datasets of melting points (i.e., OCHEM, Enamine, Bradley, and Bergstrom). The newly proposed representation is determined purely from SMILES string, thus showing promise toward fast and accurate screening of thermodynamic properties. Computer science ; Chemical space ; Entropy (arrow of time) ; Phase transition ; Property (philosophy) ; Enthalpy ; Representation (politics) ; Nonlinear system ; Artificial intelligence ; Machine learning ; Statistical physics ; Thermodynamics ; Chemistry ; Physics ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics