jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jun Zhang | Yao-Kun Lei | Yi Yang | Yi Qin Gao
Source: ChemRxiv (preprint, 2019-08-19)
Field: Materials Science — Materials Chemistry

Deep Learning for Variational Multi-Scale Molecular Modeling Molecular simulations are widely applied in the study of chemical and bio-physical systems. However, the&lt;br&gt;accessible timescales of atomistic simulations are limited, and extracting equilibrium properties of systems&lt;br&gt;containing rare events remains challenging. Two distinct strategies are usually adopted in this regard: either&lt;br&gt;sticking to the atomistic level and performing enhanced sampling, or trading details for speed by leveraging&lt;br&gt;coarse-grained models. Although both strategies are promising, either of them, if adopted individually,&lt;br&gt;exhibits severe limitations. In this paper we propose a machine-learning approach to ally both strategies so&lt;br&gt;that simulations on different scales can benefit mutually from their cross-talks: Accurate coarse-grained (CG)&lt;br&gt;models can be inferred from the fine-grained (FG) simulations through deep generative learning; In turn, FG&lt;br&gt;simulations can be boosted by the guidance of CG models via deep reinforcement learning. Our method&lt;br&gt;defines a variational and adaptive training objective which allows end-to-end training of parametric&lt;br&gt;molecular models using deep neural networks. Through multiple experiments, we show that our method is&lt;br&gt;efficient and flexible, and performs well on challenging chemical and bio-molecular systems. &lt;br&gt; Reinforcement learning ; Deep learning ; Molecular dynamics ; Computer science ; Artificial intelligence ; Artificial neural network ; Deep neural networks ; Parametric statistics ; Scale (ratio) ; Chemistry ; Algorithm ; Biological system ; Physics ; Mathematics ; Computational chemistry ; Biology Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Topic Modeling
Authors: Hitomi MITSUNO | Koji Okuwaki | Masato Ito | Yuji Mochizuki
Source: Journal of Computer Chemistry Japan (article, 2019-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Development of Educational Scratch Program for Machine Learning for Students in Chemistry Course We have been developing a Scratch-based educational program for machine learning in the chemistry context. This system has an interface with the DeepChem library set of Python. The solubility prediction of several molecules with SMILES notation was demonstrated as a preliminary application. Scratch ; Python (programming language) ; Computer science ; Notation ; Interface (matter) ; Context (archaeology) ; Programming language ; Operating system Various Chemistry Research Topics
Authors: Pankaj Rajak | Rajiv K. Kalia | Aiichiro Nakano | Priya Vashishta
Source: MRS Advances (article, 2019-01-02)
Field: Materials Science — Materials Chemistry

Neural Network Analysis of Dynamic Fracture in a Layered Material Materials science ; Fracture (geology) ; Composite material ; Artificial neural network ; Artificial intelligence ; Computer science Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Nuclear Materials and Properties
Authors: Anton V. Sinitskiy | Vijay S. Pande
Source: arXiv (Cornell University) (preprint, 2019-08-01)
Field: Materials Science — Materials Chemistry

Physical machine learning outperforms "human learning" in Quantum\n Chemistry Two types of approaches to modeling molecular systems have demonstrated high\npractical efficiency. Density functional theory (DFT), the most widely used\nquantum chemical method, is a physical approach predicting energies and\nelectron densities of molecules. Recently, numerous papers on machine learning\n(ML) of molecular properties have also been published. ML models greatly\noutperform DFT in terms of computational costs, and may even reach comparable\naccuracy, but they are missing physicality - a direct link to Quantum Physics -\nwhich limits their applicability. Here, we propose an approach that combines\nthe strong sides of DFT and ML, namely, physicality and low computational cost.\nBy generalizing the famous Hohenberg-Kohn theorems, we derive general equations\nfor exact electron densities and energies that can naturally guide applications\nof ML in Quantum Chemistry. Based on these equations, we build a deep neural\nnetwork that can compute electron densities and energies of a wide range of\norganic molecules not only much faster, but also closer to exact physical\nvalues than current versions of DFT. In particular, we reached a mean absolute\nerror in energies of molecules with up to eight non-hydrogen atoms as low as\n0.9 kcal/mol relative to CCSD(T) values, noticeably lower than those of DFT\n(down to ~3 kcal/mol on the same set of molecules) and ML (down to ~1.5\nkcal/mol) methods. A simultaneous improvement in the accuracy of predictions of\nelectron densities and energies suggests that the proposed approach describes\nthe physics of molecules better than DFT functionals developed by "human\nlearning" earlier. Thus, physics-based ML offers exciting opportunities for\nmodeling, with high-theory-level quantum chemical accuracy, of much larger\nmolecular systems than currently possible.\n Density functional theory ; Range (aeronautics) ; Molecule ; Quantum ; Statistical physics ; Artificial neural network ; Quantum chemistry ; Electron ; Physics ; Quantum mechanics ; Computational chemistry ; Computer science ; Chemistry ; Machine learning ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Text Analysis Techniques
Authors: Kai Yang | Xinyi Xu | Benjamin Yang | B. Cook | Herbert Ramos | Mathieu Bauchy
Source: arXiv (Cornell University) (preprint, 2019-01-27)
Field: Materials Science — Ceramics and Composites

Prediction of Silicate Glasses' Stiffness by High-Throughput Molecular Dynamics Simulations and Machine Learning The development by machine learning of models predicting materials' properties usually requires the use of a large number of consistent data for training. However, quality experimental datasets are not always available or self-consistent. Here, as an alternative route, we combine machine learning with high-throughput molecular dynamics simulations to predict the Young's modulus of silicate glasses. We demonstrate that this combined approach offers excellent predictions over the entire compositional domain. By comparing the performance of select machine learning algorithms, we discuss the nature of the balance between accuracy, simplicity, and interpretability in machine learning. Interpretability ; Throughput ; Computer science ; Machine learning ; Artificial intelligence ; Silicate glass ; Simplicity ; Silicate ; Materials science ; Engineering ; Physics Glass properties and applications ; Geochemistry and Geologic Mapping
Authors: Patrick Avery | Xiaoyu Wang | Corey Oses | Eric Gossett | Davide Μ. Proserpio | Cormac Toher | Stefano Curtarolo | Eva Zurek
Source: npj Computational Materials (preprint, 2019-09-03)
Field: Materials Science — Materials Chemistry

Predicting superhard materials via a machine learning informed evolutionary structure search Abstract The computational prediction of superhard materials would enable the in silico design of compounds that could be used in a wide variety of technological applications. Herein, good agreement was found between experimental Vickers hardnesses, H v , of a wide range of materials and those calculated by three macroscopic hardness models that employ the shear and/or bulk moduli obtained from: (i) first principles via AFLOW-AEL (AFLOW Automatic Elastic Library), and (ii) a machine learning (ML) model trained on materials within the AFLOW repository. Because $$H_{\mathrm{v}}^{{\mathrm{ML}}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msubsup> <mml:mrow> <mml:mi>H</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>v</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>ML</mml:mi> </mml:mrow> </mml:msubsup> </mml:math> values can be quickly estimated, they can be used in conjunction with an evolutionary search to predict stable, superhard materials. This methodology is implemented in the X tal O pt evolutionary algorithm. Each crystal is minimized to the nearest local minimum, and its Vickers hardness is computed via a linear relationship with the shear modulus discovered by Teter. Both the energy/enthalpy and $$H_{{\mathrm{v,Teter}}}^{{\mathrm{ML}}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msubsup> <mml:mrow> <mml:mi>H</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>v,Teter</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>ML</mml:mi> </mml:mrow> </mml:msubsup> </mml:math> are employed to determine a structure’s fitness. This implementation is applied towards the carbon system, and 43 new superhard phases are found. A topological analysis reveals that phases estimated to be slightly harder than diamond contain a substantial fraction of diamond and/or lonsdaleite. Vickers hardness test ; Diamond ; Materials science ; Shear modulus ; Elastic modulus ; Moduli ; Shear (geology) ; Energy (signal processing) ; Machine learning ; Thermodynamics ; Algorithm ; Computer science ; Physics ; Mathematics ; Microstructure ; Composite material ; Statistics Boron and Carbon Nanomaterials Research ; Machine Learning in Materials Science ; Metal and Thin Film Mechanics
Authors: Tobias Sikosek
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2019-11-14)
Field: Materials Science — Materials Chemistry

Protein structure featurization via standard image classification neural networks ABSTRACT Many applications in the biomedical domain involve the detailed molecular and functional characterization of macro-molecules such as proteins. Where possible, this involves the knowledge of detailed 3D coordinates of every atom within a protein. At the same time, machine learning has become the basis of much innovation within this domain in recent years. There are, however, a few challenges in applying machine learning to 3D protein structures, such as variability in size and high dimensionality of the data. It would therefore be beneficial to be able to map every protein structure to a smaller fixed-dimensional representation that is directly learned from the structure without manual curation. In addition, it would be valuable for biomedical researchers if such approaches would require little method development and instead draw from cutting-edge research such as image classification via deep neural networks. Here, such an approach is outlined that first re-formats protein structures as 2D color images and then applies off-the-shelf neural networks for image classification. It is shown that such neural networks can be trained to effectively encode the CATH protein classification database and that feature vectors extracted from such networks, once trained, can be transferred to a completely new task that is likely to benefit from molecular protein information, namely that of small molecule binding. Computer science ; ENCODE ; Artificial intelligence ; Artificial neural network ; Domain (mathematical analysis) ; Curse of dimensionality ; Task (project management) ; Feature (linguistics) ; Pattern recognition (psychology) ; Representation (politics) ; Machine learning ; Deep learning ; Biology ; Mathematics Enzyme Structure and Function ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Oliver T. Unke | Markus Meuwly
Source: arXiv (Cornell University) (preprint, 2019-09-17)
Field: Materials Science — Materials Chemistry

Machine Learning Potential Energy Surfaces Machine Learning techniques can be used to represent high-dimensional potential energy surfaces for reactive chemical systems. Two such methods are based on a reproducing kernel Hilbert space representation or on deep neural networks. They can achieve a sub-1 kcal/mol accuracy with respect to reference data and can be used in studies of chemical dynamics. Their construction and a few typical examples are briefly summarized in the present contribution. Reproducing kernel Hilbert space ; Computer science ; Representation (politics) ; Artificial intelligence ; Kernel (algebra) ; Energy (signal processing) ; Hilbert space ; Artificial neural network ; Kernel method ; Machine learning ; Chemical space ; Mathematics ; Support vector machine ; Chemistry ; Pure mathematics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Protein Structure and Dynamics
Authors: Yuan Qi | Alejandro Santana‐Bonilla | Martijn A. Zwijnenburg | Kim E. Jelfs
Source: ChemRxiv (preprint, 2019-09-30)
Field: Materials Science — Materials Chemistry

Molecular Generation Targeting Desired Electronic Properties via Deep Generative Models &lt;p&gt;The chemical space for novel electronic donor-acceptor oligomers with targeted properties was explored using deep generative models and transfer learning. A General Recurrent Neural Network model was trained from the ChEMBL database to generate chemically valid SMILES strings. The parameters of the General Recurrent Neural Network were fine-tuned via transfer learning using the electronic donor-acceptor database from the Computational Material Repository to generate novel donor-acceptor oligomers. Six different transfer learning models were developed with different subsets of the donor-acceptor database as training sets. We concluded that electronic properties such as HOMO-LUMO gaps and dipole moments of the training sets can be learned using the SMILES representation with deep generative models, and that the chemical space of the training sets can be efficiently explored. This approach identified approximately 1700 new molecules that have promising electronic properties (HOMO-LUMO gap &lt;2 eV and dipole moment &lt;2 Debye), 6-times more than in the original database. Amongst the molecular transformations, the deep generative model has learned how to produce novel molecules by trading off between selected atomic substitutions (such as halogenation or methylation) and molecular features such as the spatial extension of the oligomer. The method can be extended as a plausible source of new chemical combinations to effectively explore the chemical space for targeted properties.&lt;/p&gt; Chemical space ; Artificial neural network ; Dipole ; Computer science ; Acceptor ; HOMO/LUMO ; Deep learning ; Artificial intelligence ; Representation (politics) ; Chemistry ; Computational chemistry ; Molecule ; Physics ; Quantum mechanics ; Drug discovery ; Organic chemistry Machine Learning in Materials Science ; Chemistry and Chemical Engineering ; Computational Drug Discovery Methods
Authors: J.C. Armitage | Leszek J. Spalek | Małgorzata Nguyen | Mark Nikolka | Ian E. Jacobs | Lorena Marañón | Iyad Nasrallah | Guillaume Schweicher | Ivan Dimov | Dimitrios Simatos | Iain McCulloch | Christian B. Nielsen | G. J. Conduit | Henning Sirringhaus
Source: arXiv (Cornell University) (preprint, 2019-10-21)
Field: Materials Science — Materials Chemistry

Fragment Graphical Variational AutoEncoding for Screening Molecules with Small Data In the majority of molecular optimization tasks, predictive machine learning (ML) models are limited due to the unavailability and cost of generating big experimental datasets on the specific task. To circumvent this limitation, ML models are trained on big theoretical datasets or experimental indicators of molecular suitability that are either publicly available or inexpensive to acquire. These approaches produce a set of candidate molecules which have to be ranked using limited experimental data or expert knowledge. Under the assumption that structure is related to functionality, here we use a molecular fragment-based graphical autoencoder to generate unique structural fingerprints to efficiently search through the candidate set. We demonstrate that fragment-based graphical autoencoding reduces the error in predicting physical characteristics such as the solubility and partition coefficient in the small data regime compared to other extended circular fingerprints and string based approaches. We further demonstrate that this approach is capable of providing insight into real world molecular optimization problems, such as searching for stabilization additives in organic semiconductors by accurately predicting 92% of test molecules given 69 training examples. This task is a model example of black box molecular optimization as there is minimal theoretical and experimental knowledge to accurately predict the suitability of the additives. Unavailability ; Computer science ; Set (abstract data type) ; Data mining ; Task (project management) ; Test set ; Fragment (logic) ; Experimental data ; Data set ; Machine learning ; Artificial intelligence ; Algorithm ; Mathematics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Frederik Cloppenburg | Stefan Schlichter | Thomas Gries
Source: RWTH Publications (RWTH Aachen) (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Wirtschaftliche und technische Modellierung und Selbstoptimierung von Vliesstoffkrempeln The carding process is the most relevant process for the production of nonwovens from fibers of limited length (staple fibers). The quality parameters of nonwovens are complexly influenced by a large number of properties of the raw material, the production settings and the environmental conditions. In particular, the setting of the card has an enormous influence on the quality of the nonwoven. The aim of this work is to optimize the cards under technical and economic aspects using machine learning methods. For this purpose, the influences on the resulting nonwoven quality and the production costs are determined and a measuring system is developed which records all variables during the production process. The effect of the influencing variables on the target variables of product quality and production costs is modelled with the help of machine learning methods. Based on the developed models a simulation is developed, which simulates the expected product quality and the expected energy consumption for different set-tings. For each setting point the specific production costs are calculated which are expected at the given settings. An optimization algorithm based on the ε-constraint method is developed, which systematically browses through the simulation results and selects the setting where the required quality is achieved and the production costs are the lowest. The developments will be validated on two production lines. The investment is evaluated monetarily using a dynamic payback period calculation. Depending on the degree of previously realized production optimization, the payback period lies between 3 and 14 months. The measurement system is then further developed to classify the subjectively perceived optical uniformity of a carded web using cost-effective camera technology and neural networks. It is shown that the detection of the aesthetical nonwoven quality is successful with low-cost camera technology and an artificial neural network. Environmental science Metal Alloys Wear and Properties ; Engineering and Materials Science Studies ; Metallurgy and Material Forming
Authors: Alain C. Vaucher | Federico Zipoli | Joppe Geluykens | Vishnu H Nair | Philippe Schwaller | Teodoro Laino
Source: ChemRxiv (preprint, 2019-12-26)
Field: Materials Science — Materials Chemistry

Automated Extraction of Chemical Synthesis Actions from Experimental Procedures &lt;div&gt; &lt;div&gt; &lt;div&gt; &lt;p&gt;Experimental procedures for chemical synthesis are commonly reported in prose in patents or in the scientific literature. The automatic extraction of the details necessary to reproduce and validate a synthesis in a chemical laboratory is quite often a tedious task, requiring extensive human intervention. We present a method to convert unstructured experimental procedures written in English to structured synthetic steps (action sequences) reflecting all the operations needed to successfully conduct the corresponding chemical reactions. To achieve this, we design a set of synthesis actions with predefined properties and a deep-learning sequence to sequence model based on the transformer architecture to convert experimental procedures to action sequences. The model is pretrained on vast amounts of data generated automatically with a custom rule-based natural language processing approach and refined on a smaller set of manually annotated samples. Predictions on our test set resulted in a perfect (100%) match of the action sequence for 60.8% of sentences, a 90% match for 71.3% of sentences, and a 75% match for 82.4% of sentences. &lt;br&gt;&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;This is a pre-print of an article published in Nature Communications. The final authenticated version is available online at: https://doi.org/10.1038/s41467-020-17266-6&lt;br&gt;&lt;/p&gt; &lt;/div&gt; &lt;/div&gt; &lt;/div&gt; Computer science ; Transformer ; Sequence (biology) ; Set (abstract data type) ; Artificial intelligence ; Natural language processing ; Chemistry ; Programming language ; Biochemistry ; Engineering Machine Learning in Materials Science ; Advanced Text Analysis Techniques ; Software Engineering Research
Authors: Piero Gasparotto | Davide Bochicchio | Michele Ceriotti | Giovanni M. Pavan
Source: ChemRxiv (preprint, 2019-06-14)
Field: Materials Science — Materials Chemistry

Identifying and Tracking Defects in Dynamic Supramolecular Polymers A central paradigm of self-assembly is to create ordered structures starting from molecular&lt;br&gt;monomers that spontaneously recognize and interact with each other via noncovalent interactions.&lt;br&gt;In the recent years, great efforts have been directed toward reaching the perfection in the&lt;br&gt;design of a variety of supramolecular polymers and materials with different architectures. The&lt;br&gt;resulting structures are often thought of as ideally perfect, defect-free supramolecular fibers,&lt;br&gt;micelles, vesicles, etc., having an intrinsic dynamic character, which are typically studied at the&lt;br&gt;level of statistical ensembles to assess their average properties. However, molecular simulations&lt;br&gt;recently demonstrated that local defects that may be present or may form in these assemblies, and which are poorly captured by conventional approaches, are key to controlling their dynamic&lt;br&gt;behavior and properties. The study of these defects poses considerable challenges, as the&lt;br&gt;flexible/dynamic nature of these soft systems makes it difficult to identify what effectively constitutes&lt;br&gt;a defect, and to characterize its stability and evolution. Here, we demonstrate the power&lt;br&gt;of unsupervised machine learning techniques to systematically identify and compare defects in&lt;br&gt;supramolecular polymer variants in different conditions, using as a benchmark 5°A-resolution&lt;br&gt;coarse-grained molecular simulations of a family of supramolecular polymers. We shot that this&lt;br&gt;approach allows a complete data-driven characterization of the internal structure and dynamics&lt;br&gt;of these complex assemblies and of the dynamic pathways for defects formation and resorption.&lt;br&gt;This provides a useful, generally applicable approach to unambiguously identify defects in&lt;br&gt;these dynamic self-assembled materials and to classify them based on their structure, stability&lt;br&gt;and dynamics.&lt;br&gt; Supramolecular chemistry ; Polymer ; Supramolecular polymers ; Monomer ; Nanotechnology ; Chemistry ; Micelle ; Materials science ; Computer science ; Molecule ; Aqueous solution ; Organic chemistry Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Polymer composites and self-healing