jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Otto Lindfors
Source: Doria (University of Helsinki) (article, 2021-01-01)
Field: Chemistry — Spectroscopy

Modeling molecular trajectories using long short-term memory with stacked state pooling Atomistic molecular dynamics can be used for simulating large molecular systems with great accuracy. The downside to this is that simulations are computationally expensive and thus take a long time to perform. Many times, one is only interested in a subset, or a summarizing statistic, of the information available in the results of the simulations. This raises the question whether it is necessary to run full molecular dynamics simulations when one is interested in only partial outputs or if similar results can be achieved with a cleverly designed machine learning algorithm. In this thesis, an attempt to answer this question is made by proposing a deep learning model for solving general many-body problems. The model generates long sequences of particle positions by predicting one time step at a time, using its outputs as new inputs. It is demonstrated that this model is capable of running simple molecular dynamics. Suggestions for further experiments to measure the capability of the model to generalize to complex many-body problems are presented. Term (time) ; Pooling ; Computer science ; State (computer science) ; Artificial intelligence ; Algorithm ; Physics Mass Spectrometry Techniques and Applications ; Gaussian Processes and Bayesian Inference ; Neural Networks and Applications
Authors: Binghong Chen | Chengtao Li | Hanjun Dai | Rampi Ramprasad | Song Le
Source:  (article, 2021-05-04)
Field: Materials Science — Materials Chemistry

PolyRetro: Few-shot Polymer Retrosynthesis via Domain Adaptation Polymers appear everywhere in our daily lives -- fabrics, plastics, rubbers, etc. -- and we could hardly live without them. To make polymers, chemists develop processes that combine smaller building blocks~(monomers) to form long chains or complex networks~(polymers). These processes are called polymerizations and will usually take lots of human efforts to develop. Although machine learning models for small molecules have generated lots of promising results, the prediction problem for polymerization is new and suffers from the scarcity of polymerization datasets available in the field. Furthermore, the problem is made even more challenging by the large size of the polymers and the additional recursive constraints, which are not present in the small molecule problem. In this paper, we make an initial step towards this challenge and propose a learning-based search framework that can automatically identify a sequence of reactions that lead to the polymerization of a target polymer with minimal polymerization data involved. Our method transfers models trained on small molecule datasets for retrosynthesis to check the validity of polymerization reaction. Furthermore, our method also incorporates a template prior learned on a limited amount of polymer data into the framework to adapt the model from small molecule to the polymer domain. We demonstrate that our method is able to propose high-quality polymerization plans for a dataset of 52 real-world polymers, of which a significant portion successfully recovers the currently-in-used polymerization processes in the real world. Polymerization ; Retrosynthetic analysis ; Polymer ; Computer science ; Domain (mathematical analysis) ; Chemistry ; Mathematics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Dong Hyeon Mok | Seoin Back
Source: ChemRxiv (preprint, 2021-05-10)
Field: Materials Science — Materials Chemistry

Atomic Structure-free Representation of Active Motifs for Expedited Catalyst Discovery For CO* and H* binding energy prediction, we develop new representation of catalyst surface which split surface into three types of site, first nearest neighbor of adsorbates and second nearest neighbor in same layer and sublayer. From this representation and machine learning regression model, we achieve reasonable accuracy (0.120 eV for CO* and 0.105 eV for H*) with quick training (~200 sec using CPU). Because our representation does not require density functional calculation and atomic structure modelling, it can predict binding energies of possible active motifs without time-consuming steps. Representation (politics) ; k-nearest neighbors algorithm ; Catalysis ; Computer science ; Active site ; Chemistry ; Artificial intelligence Machine Learning in Materials Science ; Surface Chemistry and Catalysis ; Catalysis and Oxidation Reactions
Authors: Višnja Stepanić | Davor Oršolić | Vesna Pehar | Tomislav Šmuc
Source:  (article, 2021-01-01)
Field: Chemistry — Analytical Chemistry

Development of phytotoxic natural molecules as complementary herbicidal agents is supported by machine learning study Natural (archaeology) ; Computer science ; Artificial intelligence ; Chemistry ; Biology Spectroscopy and Chemometric Analyses ; Agriculture and Biological Studies ; Allelopathy and phytotoxic interactions
Authors: Steven P. Bennett | Filip Szczypiński | Lukas Turcani | Michael E. Briggs | Rebecca L. Greenaway | Kim E. Jelfs
Source: ChemRxiv (preprint, 2021-03-31)
Field: Materials Science — Materials Chemistry

Materials Precursor Score: Modelling Chemists' Intuition for the Synthetic Accessibility of Porous Organic Cages &lt;div&gt;Computation is increasingly being used to try to accelerate the discovery of new materials. One specific example of this is porous molecular materials, specifically porous organic cages, where the porosity of the materials predominantly comes from the internal cavities of the molecules themselves. The computational discovery of novel structures with useful properties is currently hindered by the difficulty in transitioning from a computational prediction to synthetic realisation. Attempts at experimental validation are often time-consuming, expensive and, frequently, the key bottleneck of material discovery. In this work, we developed a computational screening workflow for porous molecules that includes consideration of the synthetic difficulty of material precursors, aimed at easing the transition between computational prediction and experimental realisation. We trained a machine learning model by first collecting data on 12,553 molecules categorised either as `easy-to-synthesise' or `difficult-to-synthesise' by expert chemists with years of experience in organic synthesis. We used an approach to address the class imbalance present in our dataset, producing a binary classifier able to categorise easy-to-synthesise molecules with few false positives. We then used our model during computational screening for porous organic molecules to bias towards precursors whose easier synthesis requirements would make them promising candidates for experimental realisation and material development. We found that even by limiting precursors to those that are easier-to-synthesise, we are still able to identify cages with favourable, and even some rare, properties. &lt;/div&gt; Workflow ; Computer science ; Limiting ; Bottleneck ; Realisation ; Artificial intelligence ; Organic molecules ; Biochemical engineering ; Computational model ; Machine learning ; Nanotechnology ; Molecule ; Chemistry ; Materials science ; Engineering ; Organic chemistry ; Mechanical engineering ; Database ; Physics Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Supramolecular Chemistry and Complexes
Authors: Ryo Nagai | Ryosuke Akashi | Osamu Sugino
Source: arXiv (Cornell University) (preprint, 2021-11-30)
Field: Materials Science — Materials Chemistry

Machine-Learning-Based Exchange-Correlation Functional with Physical Asymptotic Constraints Density functional theory is the standard theory for computing the electronic structure of materials, which is based on a functional that maps the electron density to the energy. However, a rigorous form of the functional is not known and has been heuristically constructed by interpolating asymptotic constraints known for extreme situations, such as isolated atoms and uniform electron gas. Recent studies have demonstrated that the functional can be effectively approximated using machine learning (ML) approaches. However, most ML models do not satisfy asymptotic constraints. In this study, by applying a novel ML model architecture, we demonstrate a neural network-based exchange-correlation functional satisfying physical asymptotic constraints. Calculations reveal that the trained functional is applicable to various materials with an accuracy higher than that of existing functionals, even for materials whose electronic properties are different from the properties of materials in the training dataset. Our proposed approach thus improves the accuracy and generalization performance of the ML-based functional by combining the advantages of ML and analytical modeling. Density functional theory ; Generalization ; Energy functional ; Hybrid functional ; Orbital-free density functional theory ; Computer science ; Artificial neural network ; Applied mathematics ; Artificial intelligence ; Mathematical optimization ; Mathematics ; Computational chemistry ; Mathematical analysis ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Dong Hyeon Mok | Seoin Back
Source: ChemRxiv (preprint, 2021-05-11)
Field: Materials Science — Materials Chemistry

Atomic Structure-free Representation of Active Motifs for Expedited Catalyst Discovery For CO* and H* binding energy prediction, we develop new representation of catalyst surface which split surface into three types of site, first nearest neighbor of adsorbates and second nearest neighbor in same layer and sublayer. From this representation and machine learning regression model, we achieve reasonable accuracy (0.120 eV for CO* and 0.105 eV for H*) with quick training (~200 sec using CPU). Because our representation does not require density functional calculation and atomic structure modelling, it can predict binding energies of possible active motifs without time-consuming steps. Representation (politics) ; k-nearest neighbors algorithm ; Catalysis ; Computer science ; Chemistry ; Materials science ; Artificial intelligence Machine Learning in Materials Science ; Surface Chemistry and Catalysis ; Catalysis and Oxidation Reactions
Authors: Michael Kilgour | Lena Simine
Source: ChemRxiv (preprint, 2021-05-31)
Field: Materials Science — Materials Chemistry

Inside the Black Box: A Physical Basis for the Effectiveness of Deep Generative Models of Amorphous Materials We have recently demonstrated an effective protocol for the simulation of amorphous molecular configurations using the PixelCNN generative model (J. Phys. Chem. Lett. 2020, 11, 20, 8532). The morphological sampling of amorphous materials via such an autoregressive generation protocol sidesteps the high computational costs associated with simulating amorphous materials at scale, enabling practically unlimited structural sampling based on only small-scale experimental or computational training samples. An important question raised but not rigorously addressed in that report was whether this machine learning approach could be considered a physical simulation in the conventional sense. Here we answer this question by detailing the inner workings of the underlying algorithm that we refer to as the Morphological Autoregression Protocol or MAP. Amorphous solid ; Generative grammar ; Computer science ; Protocol (science) ; Autoregressive model ; Scale (ratio) ; Sampling (signal processing) ; Artificial intelligence ; Mathematics ; Econometrics ; Medicine ; Chemistry ; Cartography ; Geography ; Computer vision Machine Learning in Materials Science ; Protein Structure and Dynamics ; Material Dynamics and Properties
Authors: Jakob Ropers | Marco M. Mosca | Olga Anosova | Vitaliy Kurlin | Andrew I. Cooper
Source: arXiv (Cornell University) (preprint, 2021-08-11)
Field: Materials Science — Materials Chemistry

Fast predictions of lattice energies by continuous isometry invariants of crystal structures Crystal Structure Prediction (CSP) aims to discover solid crystalline materials by optimizing periodic arrangements of atoms, ions or molecules. CSP takes weeks of supercomputer time because of slow energy minimizations for millions of simulated crystals. The lattice energy is a key physical property, which determines thermodynamic stability of a crystal but has no simple analytic expression. Past machine learning approaches to predict the lattice energy used slow crystal descriptors depending on manually chosen parameters. The new area of Periodic Geometry offers much faster isometry invariants that are also continuous under perturbations of atoms. Our experiments on simulated crystals confirm that a small distance between the new invariants guarantees a small difference of energies. We compare several kernel methods for invariant-based predictions of energy and achieve the mean absolute error of less than 5kJ/mole or 0.05eV/atom on a dataset of 5679 crystals. Lattice energy ; Crystal structure ; Lattice (music) ; Crystal (programming language) ; Atom (system on chip) ; Ion ; Crystal structure prediction ; Isometry (Riemannian geometry) ; Physics ; Crystallography ; Materials science ; Statistical physics ; Mathematics ; Chemistry ; Computer science ; Mathematical analysis ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Y. Kang | In‐Ho Lee | Myung Joon Han | K. J. Chang
Source: Physical Review Materials (article, 2021-10-13)
Field: Materials Science — Materials Chemistry

<i>Ab initio</i> prediction of nontrivial topological band and superconductivity in stable metallic Si allotropes at ambient pressure Silicon is a semiconductor and widely used as the key element for modern electronic devices. Various metallic superconducting phases have been reported, but most retain their crystal structure at high pressures. Thus, it remains a challenge to search for potential superconducting Si allotropes. In this article, the authors propose novel metallic Si allotropes that meet the conditions for dynamic, mechanical, and thermal stability at ambient pressure through machine learning and first-principles electronic structure calculations. The new allotropes are superconductors and even exhibit a nontrivial band topology, providing a promising platform for realizing a topological superconducting state in all-Si systems. Superconductivity ; Materials science ; Topology (electrical circuits) ; Semiconductor ; Ambient pressure ; Condensed matter physics ; Metal ; Ab initio ; Silicon ; Electronic band structure ; Electronic structure ; Nanotechnology ; Optoelectronics ; Thermodynamics ; Physics ; Quantum mechanics ; Electrical engineering ; Metallurgy Graphene research and applications ; Boron and Carbon Nanomaterials Research ; Machine Learning in Materials Science
Authors: Rohit Modee | Sheena Agarwal | Ashwini Verma | Kavita Joshi | U. Deva Priyakumar
Source: ChemRxiv (preprint, 2021-05-26)
Field: Materials Science — Materials Chemistry

DART: Deep Learning Enabled Topological Interaction Model for Energy Prediction of Metal Clusters and its Application in Identifying Unique Low Energy Isomers &lt;div&gt;&lt;div&gt;&lt;div&gt;&lt;p&gt;Recently, Machine Learning (ML) has proven to yield fast and accurate predictions of chemical properties to accelerate the discovery of novel molecules and materials. The majority of the work is on organic molecules, and much more work needs to be done for inorganic molecules, especially clusters. In the present work, we introduce a simple Topological Atomic Descriptor called TAD, which encodes chemical environment information of each atom in the cluster. TAD is a simple and interpretable descriptor where each value represents the atom count in three shells. We also introduce the DART, Deep Learning Enabled Topological Interaction model, which uses TAD as a feature vector to predict energies of metal clusters, in our case Gallium clusters with size ranging from 31 to 70 atoms. DART model is designed based on the principle that energy is a function of atomic interactions and allows us to model these complex atomic interactions to predict the energy. We further introduce a new dataset called GNC_31-70, which comprises structures and DFT optimized energies of Gallium clusters with sizes ranging from 31 to 70 atoms. We show how DART can be used to accelerate the identification of ground-state structures without geometry optimization. Albeit using topological descriptor, DART achieves MAE of 3.59 kcal/mol (0.15 eV) on testset. We also show that our model can distinguish core and surface atoms in the Ga-70 cluster, which the model has never encountered earlier. Finally, we demonstrate the transferability of DART model by predicting energies for about 6k unseen configurations picked up from Molecular Dynamics (MD) data for three cluster sizes (46, 57, and 60) within seconds. The DART model was able to reduce the load on DFT optimizations while identifying unique low energy structures from MD data.&lt;/p&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt; Cluster (spacecraft) ; Atom (system on chip) ; Topology (electrical circuits) ; Energy (signal processing) ; Yield (engineering) ; Molecule ; Physics ; Simple (philosophy) ; Chemistry ; Computer science ; Atomic physics ; Mathematics ; Quantum mechanics ; Combinatorics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; History and advancements in chemistry
Authors: Jiaji Su
Source: 2021 2nd Asia-Pacific Conference on Image Processing, Electronics and Computers (article, 2021-04-14)
Field: Materials Science — Materials Chemistry

Application and Development of Carbon Nanotubes in Manufacturing Decades after the concept of carbon nanotubes was proposed and became a hot topic of research, there is a new focus on the application of such materials with excellent chemical and physical properties in manufacturing. After investigating academic reports on nanoscale manufacturing in the past few years, this paper discusses and summarizes various potential applications of carbon nanotubes, both electrical and non-electrical. Moreover, with the increasing computer computing power and the improvement of machine learning algorithms, study of CNT based on AI becomes a new and effective way to learn more about this material. However, there are still some unknown properties of carbon nanotubes, which may influence the quality control of products in the process of manufacturing, and the harm to human body and the natural environment is also an obstacle to its practical application and manufacturing. Carbon nanotube ; Nanotechnology ; Obstacle ; Computer science ; Process (computing) ; Materials science ; Manufacturing engineering ; Engineering Carbon Nanotubes in Composites ; Nanotechnology research and applications ; Neuroscience and Neural Engineering
Authors: Gang Bai | Lin Cui | Duansheng Liu | Jie Xu | Wei Li | Cun‐Fa Gao
Source: Acta Physica Sinica (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Phase diagram and dielectric properties of orientation-dependent PbZr&lt;sub&gt;0.52&lt;/sub&gt;Ti&lt;sub&gt;0.48&lt;/sub&gt;O&lt;sub&gt;3&lt;/sub&gt; epitaxial films Exploring phase transition behaviors and constructing phase diagrams are of importance for theoretically and experimentally studying ferroelectric physics and materials. Because of the rapid development of computers and artificial intelligence, especially machine learning methods combined with other computational methods such as first principle calculation, it is possible to predict and choose appropriate materials that meet the target requirements from a large number of material data, which greatly saves the cost of experiments. In this work, we use neural network method and phenomenological theoretical calculations to accurately predict the phase structures that may appear in the phase diagrams of different orientated Pb(Zr&lt;sub&gt;0.52&lt;/sub&gt;Ti&lt;sub&gt;0.48&lt;/sub&gt;)O&lt;sub&gt;3&lt;/sub&gt; ferroelectric films, and establish the temperature-strain phase diagrams of (001), (110) and (111) oriented thin film, and calculate the polarization and dielectric properties of different oriented films at room temperature. By analyzing the changes of prediction accuracy and loss with the number of iterations, it is found that the deep neural network method has the advantages of high accuracy and speed in the construction of the film temperature-strain phase diagram and the prediction of the types of phases. Through the analysis of the room temperature polarization and dielectric properties, it is found that the (111)-oriented PbZr&lt;sub&gt;0.52&lt;/sub&gt;Ti&lt;sub&gt;0.48&lt;/sub&gt;O&lt;sub&gt;3&lt;/sub&gt; film has the largest out-of-plane polarization and the smallest out-of-plane dielectric coefficient, and they are insensitive to misfit strain. This work provides guidelines for designing micro-nano devices that require the stable dielectric coefficient and polarization performance in the special working environment and operation. Phase diagram ; Dielectric ; Materials science ; Ferroelectricity ; Polarization (electrochemistry) ; Phase transition ; Phase (matter) ; Condensed matter physics ; Physics ; Optoelectronics ; Physical chemistry ; Chemistry Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials ; Magnetic Properties and Applications
Authors: Leixin Miao | Yingdong Guan | Jinliang Ning | Weiwei Xie | Jianwei Sun | Zhiqiang Mao | Nasim Alem
Source: Microscopy and Microanalysis (article, 2021-07-30)
Field: Materials Science — Materials Chemistry

Decoding defect ordering from ADF-STEM images of van der Waals CrGa<sub>2</sub>Te<sub>7</sub> ferromagnetic crystals using the unsupervised machine learning algorithm An abstract is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the ‘Save PDF’ action button. van der Waals force ; Decoding methods ; Ferromagnetism ; Algorithm ; Action (physics) ; Computer science ; Artificial intelligence ; Materials science ; Condensed matter physics ; Physics ; Quantum mechanics ; Molecule 2D Materials and Applications ; Machine Learning in Materials Science ; Quantum Dots Synthesis And Properties
Authors: Julio Coll
Source: ChemRxiv (preprint, 2021-09-06)
Field: Chemistry — Organic Chemistry

Star-shaped Triazine-derivatives: would they crossbind SARS-CoV-2 spike helices? This work describes synthesizable water-soluble Triazine-derivatives computationally crossbinding the S spike helices of Severe Acute Respiratory Syndrome coronavirus (SARS)-CoV-2. The "spring-loaded switch-folding” (S-SLSF) α-helices included in the S homotrimer top-to-bottom cavity and implicated in viral-host membrane fusion were targeted by star-shaped Trihydroxyl-Triphenyl-Triazines (TTT) leads at subnanomolar binding-scores. Exploration of in silico leads among millions of molecular candidates, included several similar searches, core-replacement, fragment extensions, or convolutional neural network deep-screening combined with hundreds of water-soluble lead-derivatives identified by manual iterations and commercially available building-blocks for chemical synthesis. The lead-derivatives are briefly discussed for in vitro validation and possibilities of fusion inhibition substituting mutations. In silico ; Triazine ; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ; Computational biology ; Folding (DSP implementation) ; Helix (gastropod) ; Chemistry ; Coronavirus disease 2019 (COVID-19) ; Biophysics ; Coronavirus ; Spike (software development) ; Combinatorial chemistry ; Stereochemistry ; Biology ; Computer science ; Biochemistry ; Medicine ; Gene Synthesis and Characterization of Heterocyclic Compounds ; Click Chemistry and Applications ; Bacteriophages and microbial interactions