jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: O. V. Uvarova | С. И. Уваров
Source: Izvestiya Vysshikh Uchebnykh Zavedenii Materialy Elektronnoi Tekhniki = Materials of Electronics Engineering (article, 2021-02-25)
Field: Materials Science — Materials Chemistry

Machine-learning based interatomic potential for studying of crystal structures properties In the process of modeling multilayer semiconductor nanostructures, it is important to quickly obtain accurate values the characteristics of the structure under consideration. One of these characteristics is the value of the interaction energy of atoms within the structure. The energy value is also important for obtaining other quantities, such as bulk modulus of the structure, shear modulus etc. The paper considers a machine learning based method for obtaining the interaction energy of two atoms. A model built on the basis of the Gaussian Approximation Potential (GAP) is trained on a previously prepared sample and allows predicting the energy values of atom pairs for test data. The values of the coordinates of the interacting atoms, the distance between the atoms, the value of the lattice constant of the structure, an indication of the type of interacting atoms, and also the value describing the environment of the atoms were used as features. The coordinates of the atoms, the distance between the atoms, the lattice constant of the structure, an indication of the type of interacting atoms, the value describing the environment of the atoms were used as features. The computational experiment was carried out with structures of Si, Ge and C. There were estimated the rate of obtaining the energy of interacting atoms and the accuracy of the obtained value. The characteristics of speed and accuracy were compared with the characteristics that were achieved using the many-particle interatomic potential — the Tersoff potential. Interatomic potential ; Atom (system on chip) ; Lattice constant ; Lattice (music) ; Crystal structure ; Gaussian ; Lattice energy ; Shear modulus ; Atomic physics ; Materials science ; Molecular physics ; Chemistry ; Molecular dynamics ; Thermodynamics ; Physics ; Crystallography ; Computational chemistry ; Quantum mechanics ; Computer science Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Semiconductor materials and interfaces
Authors: J. Göttert
Source: Modern Concepts in Material Science (article, 2021-01-19)
Field: Materials Science — Materials Chemistry

Automated Modelling of Material Chemistry Applying High-Throughput Formulation and Machine Learning Chemical material development belongs to the fields chemical synthesis of raw materials and formulation of products like plastics, adhesives, coatings, composites, etc. The improvement of material behavior is based on wide range of chemical structures fulfilling the conditions of the application. Raw material ; Process engineering ; Adhesive ; Throughput ; Biochemical engineering ; Materials science ; Computer science ; Nanotechnology ; Mechanical engineering ; Manufacturing engineering ; Chemistry ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Process Optimization and Integration ; Advanced Data Processing Techniques
Authors: Ankur Gupta | Krishnan Raghavachari
Source: ChemRxiv (preprint, 2021-05-20)
Field: Materials Science — Materials Chemistry

3D Dense Convolutional Neural Networks Utilizing Molecular Topological Features for Accurate Atomization Energy Predictions &lt;p&gt;Deep learning methods provide a novel way to establish a correlation between two quantities. In this context, computer vision techniques like 3D-Convolutional Neural Networks (3D-CNN) become a natural choice to associate a molecular property with its structure due to the inherent three-dimensional nature of a molecule. However, traditional 3D input data structures are intrinsically sparse in nature, which tend to induce instabilities during the learning process, which in turn may lead to under-fitted results. To address this deficiency, in this project, we propose to use quantum-chemically derived molecular topological features, namely, Localized Orbital Locator (LOL) and Electron Localization Function (ELF), as molecular descriptors, which provide a relatively denser input representation in three-dimensional space. Such topological features provide a detailed picture of the atomic configuration and inter-atomic interactions in the molecule and are thus ideal for predicting properties that are highly dependent on molecular geometry. Herein, we demonstrate the efficacy of our proposed model by applying it to the task of predicting atomization energies for the QM9-G4MP2 dataset, which contains ~134-k molecules. Furthermore, we incorporated the Δ-ML approach into our model, allowing us to reach beyond benchmark accuracy levels (~1.0 kJ mol&lt;sup&gt;−1&lt;/sup&gt;).&lt;sup&gt; &lt;/sup&gt;We consistently obtain impressive MAEs of the order 0.1 kcal mol&lt;sup&gt;−1&lt;/sup&gt; (~ 0.42 kJ mol&lt;sup&gt;−1&lt;/sup&gt;) &lt;i&gt;versus&lt;/i&gt; G4(MP2) theory using relatively modest models, which could potentially be improved further using additional compute resources.&lt;/p&gt; Context (archaeology) ; Convolutional neural network ; Physics ; Molecule ; Topology (electrical circuits) ; Artificial neural network ; Benchmark (surveying) ; Energy (signal processing) ; Computer science ; Function (biology) ; Molecular orbital ; Representation (politics) ; Algorithm ; Chemistry ; Artificial intelligence ; Mathematics ; Quantum mechanics ; Combinatorics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Hongyu Chen | Shigeru Yamaguchi | Yuya Morita | Hiroyasu Nakao | Xiangning Zhai | Y. Shimizu | Harunobu Mitsunuma | Motomu Kanai
Source: ChemRxiv (preprint, 2021-05-13)
Field: Chemistry — Inorganic Chemistry

Data-Driven Catalyst Optimization for Stereodivergent Asymmetric Synthesis of α-Allyl Carboxylic Acids by Iridium/boron Hybrid Catalysis Asymmetric catalysis enabling divergent control of multiple stereocenters remains challenging in synthetic organic chemistry. While machine learning-based optimization of molecular catalysis is an emerging approach, data-driven catalyst design to achieve stereodivergent asymmetric synthesis producing multiple reaction outcomes, such as constitutional selectivity, diastereoselectivity, and enantioselectivity, is unprecedented. Here, we report the straightforward identification of asymmetric two-component iridium/boron hybrid catalyst systems for α-C-allylation of carboxylic acids. Structural optimization of the chiral ligands for iridium catalysts was driven by molecular field-based regression analysis with a dataset containing overall 32 molecular structures. The catalyst systems enabled selective access to all the possible isomers of chiral carboxylic acids bearing contiguous stereocenters. This stereodivergent asymmetric catalysis is applicable to late-stage structural modifications of drugs and their derivatives. Stereocenter ; Catalysis ; Combinatorial chemistry ; Iridium ; Chemistry ; Enantioselective synthesis ; Asymmetric hydrogenation ; Selectivity ; Organic chemistry Asymmetric Hydrogenation and Catalysis ; Advanced Synthetic Organic Chemistry ; Machine Learning in Materials Science
Authors: Chenru Duan | Shuxin Chen | Michael G. Taylor | Fang Liu | Heather J. Kulik
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2021-06-25)
Field: Materials Science — Materials Chemistry

Machine learning to tame divergent density functional approximations: a new path to consensus materials design principles Machine learning models for the manuscript entitled "Machine learning to tame divergent density functional approximations: a new path to consensus materials design principles". Preprint at https://arxiv.org/pdf/2106.13109.pdf Path (computing) ; Computer science ; Artificial intelligence ; Machine learning ; Theoretical computer science ; Computer network Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Inorganic Chemistry and Materials
Authors: Lixin Sun | Simon Batzner | Wei Che | Jin Soo Lim | Yu Xie | Steven B. Torrisi | Jonathan Vandermause | Boris Kozinsky
Source: Bulletin of the American Physical Society (article, 2021-03-15)
Field: Materials Science — Materials Chemistry

Two-tier machine learning acceleration of molecular dynamics with enhanced sampling: surface reactions and restructuring on metal catalysts Restructuring ; Acceleration ; Catalysis ; Metal ; Molecular dynamics ; Dynamics (music) ; Materials science ; Sampling (signal processing) ; Chemical physics ; Chemical engineering ; Computer science ; Business ; Chemistry ; Computational chemistry ; Metallurgy ; Physics ; Engineering ; Classical mechanics ; Organic chemistry ; Acoustics Machine Learning in Materials Science ; Machine Learning and ELM ; Neural Networks and Applications
Authors: Kin Long Kelvin Lee | Michael McCarthy
Source: Proceedings of the 2021 International Symposium on Molecular Spectroscopy (article, 2021-06-25)
Field: Chemistry — Physical and Theoretical Chemistry

IDENTIFYING UNKNOWN MOLECULES WITH PROBABILISTIC DEEP LEARNING AND ROTATIONAL SPECTROSCOPY A major bottleneck in the analysis of broadband chirped-pulse microwave spectra is the identification of unknown molecules. Often, a set of molecular frequencies are fit to an effective Hamiltonian, whereby a set of spectroscopic parameters are used to infer the molecular carrier. These constants are reproduced with electronic structure calculations through a trial and error process, accompanied by chemical intuition involving the precursors used, and the reaction conditions (e.g. electrical discharges). As the size of the molecules increase, the combinatorics of many hundreds to thousands of possible isomers becomes an intractable problem for chemical intuition alone.\n\nSince spectroscopic parameters are only weakly informative, we turn to statistical inference to complement conventional spectroscopic analysis. In this talk, I will discuss a new framework for identifying unknown molecules by performing inference on spectroscopic parameters with probabilistic deep learning. Using a series of decoder architectures, we are able to infer the approximate molecular composition/formula and what functional groups are present using only the rotational constants and derived quantities $\\kappa$ (the asymmetry parameter) and $\\Delta$ (the inertial defect), and approximate magnitudes and projections of the dipole moments. Probabilistic logic ; Spectroscopy ; Computer science ; Artificial intelligence ; Molecule ; Physics ; Astronomy ; Quantum mechanics Various Chemistry Research Topics ; Molecular spectroscopy and chirality
Authors: K. Satō | Kenji Tsuruta
Source: Materials science forum (article, 2021-01-05)
Field: Materials Science — Materials Chemistry

Optimization of Molecular Characteristics via Machine Learning Based on Continuous Representation of Molecules We demonstrate an automatic materials design method using continuous representation of molecule and its atomic arrangement via a neural network algorithm. This method is applied to optimizing and predicting the HOMO-LUMO gap within the molecules composed of carbon, oxygen, nitrogen, fluorine, and hydrogen. Adopting the Quantum Machine 9 (QM9) dataset as a training dataset for the molecules, we first established a continuous representation of molecules in a latent space, then predicted molecules that have target values of the HOMO-LUMO gap. In the gap maximization calculation, the CF 4 with the largest gap value in the QM9 dataset was automatically found despite there is no a priori data for the gap. In the case of a target gap value of 0.10 hartree, we found a new molecule whose gap value is closer to 0.10 hartree than any other molecules in the QM9 dataset. Molecule ; Representation (politics) ; HOMO/LUMO ; Maximization ; Band gap ; Materials science ; Artificial neural network ; Value (mathematics) ; A priori and a posteriori ; Computer science ; Chemical physics ; Computational chemistry ; Artificial intelligence ; Physics ; Mathematics ; Machine learning ; Quantum mechanics ; Chemistry ; Mathematical optimization Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Rama K. Vasudevan | Stephen Jesse | Maxim Ziatdinov | Kyle P. Kelley | Hiroshi Funakubo | Ayana Ghosh | Sergei Kalinin
Source: Bulletin of the American Physical Society (article, 2021-03-16)
Field: Materials Science — Materials Chemistry

Machine Learning and Reinforcement Learning forAutomated Experimentation and Materials Synthesis Reinforcement learning ; Reinforcement ; Computer science ; Artificial intelligence ; Materials science ; Composite material Machine Learning in Materials Science
Authors: Cecilia Clementi
Source: ePrints Soton (University of Southampton) (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

AI3SD Video: Designing molecular models by machine learning and experimental data The last years have seen an immense increase in high-throughput and high-resolution technologies for experimental observation as well as high-performance techniques to simulate molecular systems at a microscopic level, resulting in vast and ever-increasing amounts of high-dimensional data. However, experiments provide only a partial view of macromolecular processes and are limited in their temporal and spatial resolution. On the other hand, atomistic simulations are still not able to sample the conformation space of large complexes, thus leaving significant gaps in our ability to study molecular processes at a biologically relevant scale. We present our efforts to bridge these gaps, by exploiting the available data and using state-of-the-art machine-learning methods to design optimal coarse models for complex macromolecular systems. We show that it is possible to define simplified molecular models to reproduce the essential information contained both in microscopic simulation and experimental measurements. Computer science ; Molecular machine ; Experimental data ; Bridge (graph theory) ; Resolution (logic) ; High resolution ; Scale (ratio) ; Throughput ; Artificial intelligence ; Biological system ; Machine learning ; Data mining ; Nanotechnology ; Materials science ; Physics ; Mathematics ; Biology Machine Learning in Materials Science ; Protein Structure and Dynamics ; Hydrocarbon exploration and reservoir analysis
Authors: Katsuhiro Endo | Kenji Yasuoka
Source: The Proceedings of Conference of Kanto Branch (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Speeding up Monte Carlo molecular simulations using machine learning Monte Carlo molecular simulation is a powerful and commonly used method for investigating molecular behavior. State updating algorithm greatly affects the efficiency of the simulations. In this work, we propose a novel method to construct highly efficient state updating algorithm using machine learning. Our proposed method contains the following three components: (1) Continuous Normalizing Molecular Flow(CNMF) method, which create various probability distributions which suitable for proposal distribution of Metropolis-Hastings algorithm, (2) a self-learning molecular flow MetropolisHastings (SL-MFMH) algorithm which is unbiased sampler with combining CNMF method, (3) a reverse process selflearning optimization (RPSO) method, which optimizes the efficiency of the SL-MFMH method. We performed experiments to evaluate the efficiency improvement of our method. The results show that the proposed method has about 10 times better performance than the original MH algorithm. We also found that the average displacement of the atoms does not decrease with decreasing acceptance rate using our method, which differs from the conventional method. Monte Carlo method ; Computer science ; Algorithm ; Process (computing) ; Construct (python library) ; Molecular dynamics ; Mathematics ; Chemistry ; Statistics Machine Learning in Materials Science ; Mass Spectrometry Techniques and Applications ; Fuel Cells and Related Materials
Authors: Chuqiao Shi | Michael C. Cao | Sarah M. Rehn | Sang‐Hoon Bae | Jeehwan Kim | Matthew R. Jones | David A. Muller | Yimo Han
Source: arXiv (Cornell University) (preprint, 2021-11-11)
Field: Materials Science — Materials Chemistry

Uncovering Material Deformations via Machine Learning Combined with Four-Dimensional Scanning Transmission Electron Microscopy Understanding lattice deformations is crucial in determining the properties of nanomaterials, which can become more prominent in future applications ranging from energy harvesting to electronic devices. However, it remains challenging to reveal unexpected deformations that crucially affect material properties across a large sample area. Here, we demonstrate a rapid and semi-automated unsupervised machine learning approach to uncover lattice deformations in materials. Our method utilizes divisive hierarchical clustering to automatically unveil multi-scale deformations in the entire sample flake from the diffraction data using four-dimensional scanning transmission electron microscopy (4D-STEM). Our approach overcomes the current barriers of large 4D data analysis and enables extraction of essential features even without a priori knowledge of the sample. Using this purely data-driven analysis, we have uncovered different types of material deformations, such as strain, lattice distortion, bending contour, etc., which can significantly impact the band structure and subsequent performance of nanomaterials-based devices. We envision that this data-driven procedure will provide insight into the intrinsic structures and accelerate the discovery of novel materials. Lattice (music) ; Nanomaterials ; Computer science ; Cluster analysis ; Materials science ; A priori and a posteriori ; Nanotechnology ; Bending ; Artificial intelligence ; Biological system ; Physics ; Acoustics ; Composite material Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Electronic and Structural Properties of Oxides
Authors: Nicolai Ree | Andreas H. Göller | Jan H. Jensen
Source:  (preprint, 2021-09-16)
Field: Materials Science — Materials Chemistry

RegioML: Predicting the regioselectivity of electrophilic aromatic substitution reactions using machine learning We present RegioML, an atom-based machine learning model for predicting the regioselectivities of electrophilic aromatic substitution reactions. The model relies on CM5 atomic charges computed using semiempirical tight binding (GFN1-xTB) combined with the ensemble decision tree variant light gradient boosting machine (LightGBM). The model is trained and tested on 21,201 bromination reactions with 101K reaction centers, which is split into a training, test, and out-of-sample datasets with 58K, 15K, and 27K reaction centers, respectively. The accuracy is 93% for the test set and 90% for the out-of-sample set, while the precision (the percentage of positive predictions that are correct) is 88% and 80%, respectively. The test-set performance is very similar to the graph-based WLN method developed by Struble et al. (React. Chem. Eng. 2020, 5, 896) though the comparison is complicated by the possibility that some of the test and out-of-sample molecules are used to train WLN. RegioML out-performs our physics-based RegioSQM20 method (J. Cheminform. 2021, 13:10) where the precision is only 75%. Even for the out-of-sample dataset, RegioML slightly outperforms RegioSQM20. The good performance of RegioML and WLN is in large part due to the large datasets available for this type of reaction. However, for reactions where there is little experimental data, physics-based approaches like RegioSQM20 can be used to generate synthetic data for model training. We demonstrate this by showing that the performance of RegioSQM20 can be reproduced by a ML-model trained on RegioSQM20-generated data. Computer science ; Gradient boosting ; Regioselectivity ; Test set ; Graph ; Artificial intelligence ; Chemistry ; Machine learning ; Computational chemistry ; Organic chemistry ; Theoretical computer science ; Random forest Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: S. Mohadeseh Taheri‐Mousavi | Seyed Sina Moeini-Ardakani | Ryan W. Penny | Ju Li | A. John Hart
Source: arXiv (Cornell University) (preprint, 2021-03-03)
Field: Materials Science — Materials Chemistry

On-lattice voxelated convolutional neural networks for prediction of phase diagrams and diffusion barriers in cubic alloys Cluster expansion approximates an on-lattice potential with polynomial regression. We show that using a convolutional neural network (CNN) instead leads to more accurate prediction due to the depth of the network. We construct our CNN potential directly on cubic lattice sites, representing voxels in a 3D image, and refer to our method as the voxelated CNN (VCNN). The convolutional layers automatically integrate interaction terms in the regressor; thus, no explicit definition of clusters is required. As a model system, we combine our VCNN potential with Monte Carlo simulations on a Ni$_{1-x}$Al$_x$ ($x$ &lt; 30%) and predict a disordered-to-ordered phase transition with less than 1 meV/atom error. We also predict the energetic landscape of vacancy diffusion. Classification of formation energy with respect to short-range-ordering of Al alloys around a vacancy reveals that the ordering decreases the probability of Ni diffusion. As the width of our input layer does not depend on the atomic composition, VCNNs can be applied to study alloys with arbitrary numbers of elements and empty lattice sites, without additional computational costs. Vacancy defect ; Lattice (music) ; Convolutional neural network ; Monte Carlo method ; Cluster expansion ; Statistical physics ; Cluster (spacecraft) ; Voxel ; Phase diagram ; Condensed matter physics ; Algorithm ; Materials science ; Computer science ; Phase (matter) ; Mathematics ; Physics ; Artificial intelligence ; Thermodynamics ; Quantum mechanics ; Statistics Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; High Temperature Alloys and Creep
Authors: Jörn Appeldorn | Simon Lemcke | Thomas Speck | Arash Nikoubashman
Source:  (preprint, 2021-08-31)
Field: Materials Science — Materials Chemistry

Employing artificial neural networks to find reaction coordinates and pathways for self-assembly Capturing the autonomous self-assembly of molecular building blocks in computer simulations is a persistent challenge, requiring to model complex interactions and to access long time scales. Advanced sampling methods allow to bridge these time scales but typically require to construct accurate low-dimensional representations of the transition pathways. In this work, we demonstrate for the self-assembly of two single-stranded DNA fragments into a ring-like structure how autoencoder architectures based on unsupervised neural networks can be employed to reliably expose transition pathways and to provide a suitable low-dimensional representation. The assembly occurs as a two-step process through two distinct half-bound states, which are correctly identified by the neural net. We exploit this latent space representation to construct a Markov state model for predicting the four molecular conformations and transition rates. Our work opens up new avenues for the computational modeling of multi-step and hierarchical self-assembly, which has proven challenging so far. Computer science ; Autoencoder ; Construct (python library) ; Artificial neural network ; Representation (politics) ; Exploit ; Artificial intelligence ; Markov chain ; Theoretical computer science ; Machine learning Machine Learning in Materials Science ; Surface Chemistry and Catalysis ; Protein Structure and Dynamics