jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sheng-Che Yen | Yu‐Lin Chen | Yen‐Hsun Su
Source: APL Materials (article, 2020-09-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Materials genome evolution of surface plasmon resonance characteristics of Au nanoparticles decorated ZnO nanorods The effect of surface plasmon resonance (SPR) from noble metal nanostructures such as gold nanoparticles (Au NPs) has been proposed to promote the generation of energetic hot electrons as well as boosting resonant energy transfer, thereby resulting in significantly enhancing solar-light harvesting and energy conversion efficiency. Herein, Au NPs decorated zinc oxide nanorods with plasmonic metal–semiconductor heterostructures have been synthesized through UV/Ozone treatment. Absorption, light-to-plasmon conversion efficiency, plasmon-to-hot electron conversion efficiency, and quality (Q)-factor of Au@ZnO nanocomposites are further characterized in order to understand the related SPR effect from various aspects. Simultaneously, the use of machine learning (ML) as an artificial intelligence data-driven method to derive an alternative predictive model for evaluating the relationship between synthesis and properties of materials has been adopted. In this regard, we collect only a limited supply of experimental dataset as training data to establish the predictive model with an artificial neural network incorporating genetic algorithm. According to the results from experimental datasets and the proposed predictive model, our analysis has revealed that the conversion efficiency and Q-factor associated with the SPR effect from Au@ZnO nanocomposites can be efficiently evaluated through ML, which has potential application in plasmon-sensitized solar cells and plasmonic lasers in the future. Materials science ; Surface plasmon resonance ; Plasmon ; Nanorod ; Nanoparticle ; Energy conversion efficiency ; Surface plasmon ; Nanotechnology ; Optoelectronics ; Plasmonic solar cell ; Localized surface plasmon ; Polymer solar cell Gold and Silver Nanoparticles Synthesis and Applications ; Quantum Dots Synthesis And Properties ; Chalcogenide Semiconductor Thin Films
Authors: Chee Kong Lee | Chengqiang Lu | Yue Yu | Qiming Sun | Chang‐Yu Hsieh | Shengyu Zhang | Qi Liu | Liang Shi
Source: ChemRxiv (preprint, 2020-11-16)
Field: Materials Science — Materials Chemistry

Transfer Learning with Graph Neural Networks for Optoelectronic Properties of Conjugated Oligomers Despite the remarkable progress of machine learning (ML) techniques in chemistry, modeling the optoelectronic properties of long conjugated oligomers and polymers with ML remains challenging due to the difficulty in obtaining sufficient training data. Here we use transfer learning to address the data scarcity issue by pre-training graph neural networks using data from short oligomers. With only a few hundred training data, we are able to achieve an average error of about 0.1 eV for excited state energy of oligothiophenes against TDDFT calculations. We show that the success of our transfer learning approach relies on the relative locality of low-lying electronic excitations in long conjugated oligomers. Finally, we demonstrate the transferability of our approach by modeling the lowest-lying excited-state energies of poly(3-hexylthiopnene) (P3HT) in its single-crystal and solution phases using the transfer learning models trained with data of gas-phase oligothiophenes. The transfer learning predicted excited-state energy distributions agree quantitatively with TDDFT calculations and capture some important qualitative features observed in experimental absorption spectra. Excited state ; Time-dependent density functional theory ; Transfer of learning ; Artificial neural network ; Transferability ; Computer science ; Conjugated system ; Materials science ; Artificial intelligence ; Chemical physics ; Machine learning ; Chemistry ; Polymer ; Physics ; Atomic physics Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Conducting polymers and applications
Authors: Aleks Reinhardt | Chris J. Pickard | Bingqing Cheng
Source: Apollo (University of Cambridge) (article, 2020-05-26)
Field: Materials Science — Materials Chemistry

Predicting the phase diagram of titanium dioxide with random search and pattern recognition. Predicting phase stabilities of crystal polymorphs is central to computational materials science and chemistry. Such predictions are challenging because they first require searching for potential energy minima and then performing arduous free-energy calculations to account for entropic effects at finite temperatures. Here, we develop a framework that facilitates such predictions by exploiting all the information obtained from random searches of crystal structures. This framework combines automated clustering, classification and visualisation of crystal structures with machine-learning estimation of their enthalpy and entropy. We demonstrate the framework on the technologically important system of TiO2, which has many polymorphs, without relying on prior knowledge of known phases. We find a number of new phases and predict the phase diagram and metastabilities of crystal polymorphs at 1600 K, benchmarking the results against full free-energy calculations. Phase diagram ; Titanium dioxide ; Crystal (programming language) ; Phase (matter) ; Crystal structure prediction ; Materials science ; Chemical physics ; Crystal structure ; Statistical physics ; Chemistry ; Computer science ; Crystallography ; Physics ; Metallurgy ; Organic chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallization and Solubility Studies
Authors: Vipul Mann | Venkat Venkatasubramanian
Source: ChemRxiv (preprint, 2020-09-22)
Field: Materials Science — Materials Chemistry

Predicting Chemical Reaction Outcomes: A Grammar Ontology-based Transformer Framework &lt;p&gt;Discovering and designing novel materials is a challenging problem as it often requires searching a combinatorially large space of potential candidates. Evaluation of all candidates experimentally is typically infeasible as it requires great amounts of effort, time, expertise, and money. The ability to predict reaction outcomes without performing extensive experiments is, therefore, important. Towards that goal, we report an approach that uses context-free grammar (CFG) based representations of molecules in a neural machine translation framework. We formulate the reaction-prediction task as a machine translation problem that involves discovering the transformations from the source sequence (comprising the reactants and agents) to the target sequence (comprising the major product) in the reaction. The grammar ontology-based representation of molecules hierarchically incorporates rich molecular structure information that, in principle, should be valuable for modeling chemical reactions. We achieve an accuracy of 80.1% on a standard reaction dataset using a model characterized by only a fraction of the number of training parameters in other sequence-to-sequence models based works in this area. Moreover, 99% of the predictions made on the same reaction dataset were found to be syntactically valid. We conclude that CFGs-based ontological representations could be an efficient way of incorporating structural information, ensuring chemically valid predictions, and overcoming overfitting in complex machine learning architectures employed in reaction prediction tasks.&lt;br&gt;&lt;/p&gt; Overfitting ; Computer science ; Artificial intelligence ; Grammar ; Natural language processing ; Sequence (biology) ; Machine learning ; Representation (politics) ; Machine translation ; Ontology ; Theoretical computer science ; Chemistry ; Artificial neural network Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Biomedical Text Mining and Ontologies
Authors: Huy Q. Ta | Alicja Bachmatiuk | Rafael G. Mendes | David Perello | Liang Zhao | Barbara Trzebicka | Thomas Gemming | Slava V. Rotkin | Mark H. Rümmeli
Source: Advanced Materials (article, 2020-09-23)
Field: Materials Science — Materials Chemistry

Large‐Area Single‐Crystal Graphene via Self‐Organization at the Macroscale In 1665 Christiaan Huygens first noticed how two pendulums, regardless of their initial state, would synchronize. It is now known that the universe is full of complex self-organizing systems, from neural networks to correlated materials. Here, graphene flakes, nucleated over a polycrystalline graphene film, synchronize during growth so as to ultimately yield a common crystal orientation at the macroscale. Strain and diffusion gradients are argued as the probable causes for the long-range cross-talk between flakes and the formation of a single-grain graphene layer. The work demonstrates that graphene synthesis can be advanced to control the nucleated crystal shape, registry, and relative alignment between graphene crystals for large area, that is, a single-crystal bilayer, and (AB-stacked) few-layer graphene can been grown at the wafer scale. Graphene ; Materials science ; Wafer ; Bilayer graphene ; Crystallite ; Crystal (programming language) ; Nanotechnology ; Bilayer ; Layer (electronics) ; Single crystal ; Condensed matter physics ; Crystallography ; Membrane ; Physics Graphene research and applications ; Advanced Memory and Neural Computing ; Carbon Nanotubes in Composites
Authors: Aditya Venkatraman | David Montes de Oca Zapiain | Surya R. Kalidindi
Source: JOM (article, 2020-10-13)
Field: Materials Science — Materials Chemistry

Reduced-Order Models for Ranking Damage Initiation in Dual-Phase Composites Using Bayesian Neural Networks Ranking (information retrieval) ; Artificial neural network ; Computer science ; Bayesian inference ; Scalability ; Dual (grammatical number) ; Resilience (materials science) ; Machine learning ; Artificial intelligence ; Bayesian probability ; Inference ; Bayesian network ; Materials science ; Composite material Machine Learning in Materials Science ; Probabilistic and Robust Engineering Design ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Zhuoran Qiao | Feizhi Ding | Matthew Welborn | Peter J. Bygrave | Daniel G. A. Smith | Animashree Anandkumar | Frederick R. Manby | Thomas F. Miller
Source: arXiv (Cornell University) (preprint, 2020-11-05)
Field: Materials Science — Materials Chemistry

Multi-task learning for electronic structure to predict and explore molecular potential energy surfaces We refine the OrbNet model to accurately predict energy, forces, and other response properties for molecules using a graph neural-network architecture based on features from low-cost approximated quantum operators in the symmetry-adapted atomic orbital basis. The model is end-to-end differentiable due to the derivation of analytic gradients for all electronic structure terms, and is shown to be transferable across chemical space due to the use of domain-specific features. The learning efficiency is improved by incorporating physically motivated constraints on the electronic structure through multi-task learning. The model outperforms existing methods on energy prediction tasks for the QM9 dataset and for molecular geometry optimizations on conformer datasets, at a computational cost that is thousand-fold or more reduced compared to conventional quantum-chemistry calculations (such as density functional theory) that offer similar accuracy. Differentiable function ; Computer science ; Electronic structure ; Task (project management) ; Quantum chemistry ; Density functional theory ; Symmetry (geometry) ; Quantum chemical ; Graph ; Domain (mathematical analysis) ; Quantum ; Artificial intelligence ; Artificial neural network ; Space (punctuation) ; Basis (linear algebra) ; Theoretical computer science ; Molecule ; Computational chemistry ; Chemistry ; Quantum mechanics ; Physics ; Mathematics ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: K.-J. Boehm | Y. Ayzman | Robert C. Blake | Antonio García | K. Sequoia | Shiv Sundram | W. Sweet
Source: Fusion Science & Technology (article, 2020-08-12)
Field: Materials Science — Materials Chemistry

Machine Learning Algorithms for Automated NIF Capsule Mandrel Selection Small shells, approximately 2 mm in diameter, made from Poly(α-methylstyrene) (PAMS) are used as mandrels in the production of glow discharge polymer capsules located at the center of inertial confinement fusion experiments. The visual inspection process of microscope images of these shell mandrels, including detection of micron-sized defects on the shell surface as well as the handling and sorting, is a very labor-intensive, repetitive, and highly subjective process that stands to benefit greatly from automation.As part of an effort to decrease the number of labor hours spent in capsule handling, inspection, and metrology, the development of robotic systems was presented in a paper by Carlson et al., “Automation in Target Fabrication” [Fusion Sci. Technol., Vol. 70, p. 274 (2016)]. The current work expands the automated image acquisition systems developed previously and adds the use of convolutional neural networks to select capsules best suited for use in the downstream production process. Through the use of these machine learning algorithms, the selection process becomes robust, repeatable, and operator independent. As an added benefit the system developed as part of this work is able to provide defect statistics on entire shell batches and feed this information upstream to the production team. Mandrel ; Automation ; Computer science ; Machine vision ; Process (computing) ; Algorithm ; Sorting ; Metrology ; Mechanical engineering ; Convolutional neural network ; Artificial intelligence ; Materials science ; Optics ; Engineering ; Physics Fusion materials and technologies ; Laser-Plasma Interactions and Diagnostics ; Nuclear Physics and Applications
Authors: Young-Jae Choi | Seung-Hoon Jhi
Source: The Journal of Physical Chemistry B (article, 2020-09-10)
Field: Materials Science — Materials Chemistry

Efficient Training of Machine Learning Potentials by a Randomized Atomic-System Generator Machine learning potentials provide an efficient and comprehensive tool to simulate large-scale systems inaccessible by conventional first-principles methods still in a similar level of accuracy. One critical issue in constructing machine learning potentials is to build training data sets cost-effectively that can represent the potential energy surface in a wide range of configurations. We develop a scheme named randomized atomic-system generator (RAG) to produce the training sets that widely cover the potential energy surface by combining the random sampling and structural optimization. We apply the scheme to construct the machine learning potentials for simulation of chalcogen-based phase change materials. Constructed machine learning potentials successfully simulate the dynamics of melting and crystallization processes of binary GeTe at a level comparable to first-principles simulations. The visual analysis shows that the RAG-generated training set represents the crystallization process including the amorphous phases. From the velocity autocorrelation function obtained from the molecular dynamics simulations, we calculate the phonon density of states to analyze the vibrational properties during crystallization. Computer science ; Generator (circuit theory) ; Artificial intelligence ; Autocorrelation ; Machine learning ; Energy (signal processing) ; Molecular dynamics ; Potential energy surface ; Algorithm ; Statistical physics ; Physics ; Mathematics ; Chemistry ; Thermodynamics ; Computational chemistry ; Power (physics) ; Ab initio Phase-change materials and chalcogenides ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Pavel Polishchuk
Source: Research Square (preprint, 2020-04-06)
Field: Materials Science — Materials Chemistry

CReM: chemically reasonable mutations framework for structure generation Abstract Structure generators are widely used in de novo design studies and their performance substantially influences an outcome. Approaches based on the deep learning models and conventional atom-based approaches may result in invalid structures and fail to address their synthetic feasibility issues. On the other hand, conventional reaction-based approaches result in synthetically feasible compounds but novelty and diversity of generated compounds may be limited. Fragment-based approaches can provide both better novelty and diversity of generated compounds but the issue of synthetic complexity of generated structure was not explicitly addressed before. Here we developed a new framework of fragment-based structure generation that, by design, results in the chemically valid structures and provides flexible control over diversity, novelty, synthetic complexity and chemotypes of generated compounds. The framework was implemented as an open-source Python module and can be used to create custom workflows for the exploration of chemical space. Novelty ; Computer science ; Chemical space ; Python (programming language) ; Workflow ; Fragment (logic) ; Synthetic biology ; Artificial intelligence ; Theoretical computer science ; Computational biology ; Algorithm ; Biology ; Drug discovery ; Bioinformatics ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Ari Frankel | Kousuke Tachida | Reese E. Jones
Source: Machine Learning Science and Technology (preprint, 2020-05-13)
Field: Materials Science — Materials Chemistry

Prediction of the evolution of the stress field of polycrystals undergoing elastic-plastic deformation with a hybrid neural network model Abstract Crystal plasticity theory is often employed to predict the mesoscopic states of polycrystalline metals, and is well-known to be costly to simulate. Using a neural network with convolutional layers encoding correlations in time and space, we were able to predict the evolution of the dominant component of the stress field given only the initial microstructure and external loading. In comparison to our recent work, we were able to predict not only the spatial average of the stress response but the evolution of the field itself. We show that the stress fields and their rates are in good agreement with the two dimensional crystal plasticity data and have no visible artifacts. Furthermore the distribution of stress throughout the elastic to fully plastic transition match the truth provided by held out crystal plasticity data. Lastly we demonstrate the efficacy of the trained model in material characterization and optimization tasks. Mesoscopic physics ; Plasticity ; Materials science ; Crystal plasticity ; Convolutional neural network ; Field (mathematics) ; Stress (linguistics) ; Stress field ; Deformation (meteorology) ; Characterization (materials science) ; Crystal (programming language) ; Statistical physics ; Computer science ; Biological system ; Finite element method ; Artificial intelligence ; Structural engineering ; Condensed matter physics ; Composite material ; Physics ; Mathematics ; Nanotechnology ; Engineering Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and mechanical properties
Authors: Henri Salmenjoki | Lasse Laurson | Mikko J. Alava
Source: Materials Theory (article, 2020-10-09)
Field: Materials Science — Materials Chemistry

Probing the transition from dislocation jamming to pinning by machine learning Abstract Collective motion of dislocations is governed by the obstacles they encounter. In pure crystals, dislocations form complex structures as they become jammed by their anisotropic shear stress fields. On the other hand, introducing disorder to the crystal causes dislocations to pin to these impeding elements and, thus, leads to a competition between dislocation-dislocation and dislocation-disorder interactions. Previous studies have shown that, depending on the dominating interaction, the mechanical response and the way the crystal yields change.Here we employ three-dimensional discrete dislocation dynamics simulations with varying density of fully coherent precipitates to study this phase transition − from jamming to pinning − using unsupervised machine learning. By constructing descriptors characterizing the evolving dislocation configurations during constant loading, a confusion algorithm is shown to be able to distinguish the systems into two separate phases. These phases agree well with the observed changes in the relaxation rate during the loading. Our results also give insights on the structure of the dislocation networks in the two phases. Dislocation ; Jamming ; Anisotropy ; Crystal (programming language) ; Shear (geology) ; Relaxation (psychology) ; Stress (linguistics) ; Phase transition Microstructure and mechanical properties ; Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications
Authors: Abel Kolawole Oyebamiji | Fadare Olatomide A | Banjo Semire
Source: Heliyon (article, 2020-03-01)
Field: Chemistry — Organic Chemistry

Anti-gastric cancer activity of 1,2,3-triazolo[4,5-d]pyrimidine hybrids (1,2,3-TPH): QSAR and molecular docking approaches Gastric cancer as a dreaded disease which occurs in the digestive system of human being remain a threat to the medical world. Bioactivity of series of designed and synthesized molecular compounds containing triazole and pyrimidine moieties were subjected to quantum chemical calculations using B3LYP/6-31+G∗. The calculated molecular descriptors such as the E<sub>HOMO</sub> (eV), E<sub>LUMO</sub> (eV), band gap (eV), chemical hardness (η), global nucleophilicity, dipole moment (Debye), chemical potential, log P, molecular weight (amu) and Ovality. The descriptors that describe anti-gastric cancer activity of the studied compounds were used for QSAR analysis using SPSS and Gretl software packages for multiple linear regression (MLR), XLSTAT for partial least square (PLS) and MATLAB for artificial neural network (ANN). The methods (MLR, PLS, and ANN) were predictive. Nevertheless, ANN performed better than MLR and PLS. More so, molecular docking study was executed on the studied compounds and gastric cancer cell line (<b>PDB ID:</b>4oum); the docking studies showed that 2-(1-(2-(3-benzyl-5-(benzylthio)-3H-[1,2,3]triazolo[4,5-d]pyrimidin-7-yl)hydrazono)ethyl)phenol (<b>A22</b>) having the lowest binding affinity (-8.40 kcal/mol); this was correlated to the observed inhibitory activity of the compound against gastric cancer. Thus, it showed better inhibition than other studied compounds. The amino acid residues that were involved in stabilizing <b>A22</b> in the active site of the <b>4oum</b> are: VAL-9, ALA-10, THR-49, ASN-48, PRO-47 and TYR-46. Also, a good relationship was observed between the calculated binding affinity and the observed inhibition concentration (IC<sub>50</sub>). Quantitative structure–activity relationship ; Chemistry ; Protein Data Bank (RCSB PDB) ; Pyrimidine ; Docking (animal) ; Molecular descriptor ; Computational chemistry ; Stereochemistry ; HOMO/LUMO ; Pyridine ; Molecule ; Organic chemistry Synthesis and biological activity ; Synthesis and Characterization of Heterocyclic Compounds ; Cancer Mechanisms and Therapy
Authors: Sandip Sawarkar | Abhijit Chatterjee
Source: Computational Materials Science (article, 2020-06-24)
Field: Materials Science — Materials Chemistry

Decision tree driven construction of rate constant models: Identifying the “top-N” environment atoms that influence surface diffusion barriers in Ag, Cu, Ni, Pd and Pt Reaction rate constant ; Constant (computer programming) ; Diffusion ; Atom (system on chip) ; Artificial neural network ; Cluster (spacecraft) ; Computer science ; Statistical physics ; Biological system ; Chemistry ; Thermodynamics ; Artificial intelligence ; Physics ; Kinetics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions