jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Øyvind Sunde Sortland | Mohammed M’Hamdi | Moez Jomâa
Source: Duo Research Archive (University of Oslo) (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Assessment of feature engineering and long short-term memory for structure loss identification from process data in monocrystalline silicon growth by the Czochralski method Structure loss impairs the quality of monocrystalline ingots and represents a major productivity loss for crystal growers. If structure loss could be predicted in advance it could help reduce the lost production time or preventive measures could be initiated. For this reason, feature engineering and machine learning by long short-term memory (LSTM) is used to assess if structure loss could be predicted from sensor data collected during growth of ingots. The method is not able to predict structure loss in advance, and the predictions may likely be based on the length of the signal, which is shortened for structure loss ingots as their growth is interrupted, and not actual features in the sensor readings. Monocrystalline silicon ; Silicon ; Identification (biology) ; Process (computing) ; Feature (linguistics) ; Term (time) ; Computer science ; Materials science ; Optoelectronics ; Operating system ; Biology ; Physics Solidification and crystal growth phenomena
Authors: Ruud van Deursen | Peter Ertl | Igor V. Tetko | Guillaume Godin
Source: arXiv (Cornell University) (preprint, 2019-09-10)
Field: Materials Science — Materials Chemistry

GEN: Highly Efficient SMILES Explorer Using Autodidactic Generative Examination Networks Recurrent neural networks have been widely used to generate millions of de novo molecules in a known chemical space. These deep generative models are typically setup with LSTM or GRU units and trained with canonical SMILEs. In this study, we introduce a new robust architecture, Generative Examination Networks GEN, based on bidirectional RNNs with concatenated sub-models to learn and generate molecular SMILES with a trained target space. GENs autonomously learn the target space in a few epochs while being subjected to an independent online examination mechanism to measure the quality of the generated set. Here we have used online statistical quality control (SQC) on the percentage of valid molecules SMILES as an examination measure to select the earliest available stable model weights. Very high levels of valid SMILES (95-98%) can be generated using multiple parallel encoding layers in combination with SMILES augmentation using unrestricted SMILES randomization. Our architecture combines an excellent novelty rate (85-90%) while generating SMILES with a strong conservation of the property space (95-99%). Our flexible examination mechanism is open to other quality criteria. Computer science ; Artificial intelligence ; Novelty ; Chemical space ; Generative grammar ; Generative model ; Recurrent neural network ; Measure (data warehouse) ; Set (abstract data type) ; Space (punctuation) ; Machine learning ; Artificial neural network ; Natural language processing ; Data mining ; Bioinformatics Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Stefanos Papanikolaou | Michail Tzimas
Source:  (preprint, 2019-12-18)
Field: Materials Science — Materials Chemistry

Effects of Rate, Size, and Prior Deformation in Microcrystal Plasticity Crystal plasticity of sub-micron finite volumes is characterized by the flow of emergent dislocation defects, giving rise to size effects in mechanical properties and avalanche phenomena. In this chapter, we present a minimal model for discrete edge dislocations in a finite volume, that has been benchmarked against experimental data and displays known phenomenological trends. We discuss how this model can explain seemingly disconnected effects of rate, size and prior deformation on microcrystal plasticity. We demonstrate the statistical features of dislocation ensembles for both stress and displacement controlled loading conditions and explore in detail the connection between loading rate and displacement bursts. Finally, we present model studies of machine learning algorithms in microcrystal plasticity that both improve understanding and clarify the range of such methods' usefulness. In this way, we elucidate the role of prior deformation history on micro and nano-sized specimens and we use this understanding to predict the mechanical response of thin films through microstructural observations of pre-existing dislocation configurations. Plasticity ; Deformation (meteorology) ; Dislocation ; Materials science ; Displacement (psychology) ; Mechanics ; Flow stress ; Crystal plasticity ; Range (aeronautics) ; Dislocation creep ; Deformation mechanism ; Stress (linguistics) ; Statistical physics ; Strain rate ; Composite material ; Physics ; Microstructure ; Psychology Microstructure and mechanical properties ; Machine Learning in Materials Science ; Force Microscopy Techniques and Applications
Authors: Jun Zhang | Yi Yang | Frank Noé
Source: ChemRxiv (preprint, 2019-04-02)
Field: Materials Science — Materials Chemistry

Targeted Adversarial Learning Optimized Sampling Boosting transitions of rare events is critical to modern-day simulations of complex dynamic systems. We present a novel approach to modify the potential energy surface in order to drive the system to a user-defined target distribution where the free energy barrier is lowered. The new approach, called targeted adversarial learning optimized sampling (TALOS), cross-fertilizes statistical mechanics and deep learning. By casting the enhanced sampling problem as a competing game between a real sampling engine and a virtual discriminator, TALOS enables unsupervised construction of bias potential on an arbitrary dimensional space and seeks for an optimal transport plan that transforms the system into target. Through multiple experiments we show that on-the-fly training of TALOS benefits from the state-of-art optimization techniques in deep learning, thus is efficient, robust and interpretable. TALOS can also simultaneously learn to extract good reaction coordinate from a high-dimensional space where bias potential is being constructed. Additionally, TALOS is shown to be closely related to reinforcement learning, giving rise to a new framework of manipulating Hamiltonian in order to fulfill user-specified tasks via deep learning. Computer science ; Adversarial system ; Reinforcement learning ; Artificial intelligence ; Deep learning ; Boosting (machine learning) ; Sampling (signal processing) ; Machine learning ; Detector Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference
Authors: Arun Mannodi‐Kanakkithodi | Michael Y. Toriyama | Fatih G. Sen | Michael J. Davis | Robert F. Klie | Maria K. Y. Chan
Source: arXiv (Cornell University) (preprint, 2019-06-05)
Field: Materials Science — Materials Chemistry

Machine-learned impurity level prediction for semiconductors: the\n example of Cd-based chalcogenides The ability to predict the likelihood of impurity incorporation and their\nelectronic energy levels in semiconductors is crucial for controlling its\nconductivity, and thus the semiconductor's performance in solar cells,\nphotodiodes, and optoelectronics. The difficulty and expense of experimental\nand computational determination of impurity levels makes a data-driven machine\nlearning approach appropriate. In this work, we show that a density functional\ntheory-generated dataset of impurities in Cd-based chalcogenides CdTe, CdSe,\nand CdS can lead to accurate and generalizable predictive models of defect\nproperties. By converting any semiconductor + impurity system into a set of\nnumerical descriptors, regression models are developed for the impurity\nformation enthalpy and charge transition levels. These regression models can\nsubsequently predict impurity properties in mixed anion CdX compounds (where X\nis a combination of Te, Se and S) fairly accurately, proving that although\ntrained only on the end points, they are applicable to intermediate\ncompositions. We make machine-learned predictions of the Fermi-level dependent\nformation energies of hundreds of possible impurities in 5 chalcogenide\ncompounds, and suggest a list of impurities which can shift the equilibrium\nFermi level in the semiconductor as determined by the dominant intrinsic\ndefects. These dominating impurities as predicted by machine learning compare\nwell with DFT predictions, revealing the power of machine-learned models in the\nquick screening of impurities likely to affect the optoelectronic behavior of\nsemiconductors.\n Impurity ; Semiconductor ; Chalcogenide ; Materials science ; Machine learning ; Condensed matter physics ; Optoelectronics ; Chemistry ; Computer science ; Physics Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films ; Advanced Semiconductor Detectors and Materials
Authors: Tran-Thai Dang | Tien-Lam Pham | Hiori Kino | Takashi Miyake | Hieu‐Chi Dam
Source: arXiv (Cornell University) (preprint, 2019-03-23)
Field: Materials Science — Materials Chemistry

Measuring the Similarity between Materials with an Emphasis on the Materials Distinctiveness In this study, we establish a basis for selecting similarity measures when applying machine learning techniques to solve materials science problems. This selection is considered with an emphasis on the distinctiveness between materials that reflect their nature well. We perform a case study with a dataset of rare-earth transition metal crystalline compounds represented using the Orbital Field Matrix descriptor and the Coulomb Matrix descriptor. We perform predictions of the formation energies using k-nearest neighbors regression, ridge regression, and kernel ridge regression. Through detailed analyses of the yield prediction accuracy, we examine the relationship between the characteristics of the material representation and similarity measures, and the complexity of the energy function they can capture. Empirical experiments and theoretical analysis reveal that similarity measures and kernels that minimize the loss of materials distinctiveness improve the prediction performance. Optimal distinctiveness theory ; Similarity (geometry) ; Ridge ; Kernel (algebra) ; Computer science ; Artificial intelligence ; Field (mathematics) ; Regression ; Representation (politics) ; Machine learning ; Matrix (chemical analysis) ; Data mining ; Pattern recognition (psychology) ; Mathematics ; Statistics ; Materials science ; Geology ; Paleontology ; Psychology Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Chia-Hao Liu | Yunzhe Tao | Daniel Hsu | Qiang Du | Simon J. L. Billinge
Source: Acta Crystallographica Section A Foundations and Advances (article, 2019-07-20)
Field: Materials Science — Materials Chemistry

Explore the symmetry encoding in atomic pair distribution function (PDF) with convolutional neural network (CNN) Discovery of new materials depends critically on knowing the material structure which is generally done using x-ray diffraction. The first step in structure solution is to "index" the diffraction patterns to determine the space-group symmetry. However, the indexing approach fails when the material is nanostructured. Atomic pair distribution function (PDF) is powerful in this case of probing local structure, but the space group information is encoded in a yet discovered way. In this work, we utilize a convolutional neural network (CNN) model to approximate the unknown encoding of the space group information in PDF. The CNN model is trained on more than 100,000 data and it yields a 91% of accuracy with the top-6 estimates against testing set and it demonstrates robust results on predicting space group of experimental PDFs, from both crystalline and nanocrystalline samples. Convolutional neural network ; Encoding (memory) ; Symmetry (geometry) ; Function (biology) ; Computer science ; Artificial neural network ; Physics ; Biological system ; Pattern recognition (psychology) ; Artificial intelligence ; Mathematics ; Biology ; Geometry ; Genetics X-ray Diffraction in Crystallography
Authors: Alessandro Greco | Vladimir Starostin | Christos Karapanagiotis | Alexander Hinderhofer | Alexander Gerlach | Linus Pithan | Sascha Liehr | Frank Schreiber | Stefan Kowarik
Source: Journal of Applied Crystallography (preprint, 2019-11-08)
Field: Materials Science — Materials Chemistry

Fast fitting of reflectivity data of growing thin films using neural networks X-ray reflectivity (XRR) is a powerful and popular scattering technique that can give valuable insight into the growth behavior of thin films. This study shows how a simple artificial neural network model can be used to determine the thickness, roughness and density of thin films of different organic semiconductors [diindenoperylene, copper(II) phthalocyanine and α-sexithiophene] on silica from their XRR data with millisecond computation time and with minimal user input or <i>a priori</i> knowledge. For a large experimental data set of 372 XRR curves, it is shown that a simple fully connected model can provide good results with a mean absolute percentage error of 8-18% when compared with the results obtained by a genetic least mean squares fit using the classical Parratt formalism. Furthermore, current drawbacks and prospects for improvement are discussed. X-ray reflectivity ; Millisecond ; Thin film ; Artificial neural network ; Materials science ; Computation ; Surface finish ; Formalism (music) ; Reflectivity ; Scattering ; Computer science ; Log-normal distribution ; Algorithm ; Optics ; Computational physics ; Biological system ; Optoelectronics ; Physics ; Artificial intelligence ; Nanotechnology ; Mathematics ; Statistics ; Composite material Machine Learning in Materials Science ; CCD and CMOS Imaging Sensors ; Advanced Memory and Neural Computing
Authors: Pokuri, Balaji | Ghosal, Sambuddha | Kokate, Apurva | Ganapathysubramanian, Baskar | Sarkar, Soumik
Source: Iowa State University Digital Repository (Iowa State University) (article, 2019-10-01)
Field: Materials Science — Materials Chemistry

Interpretable deep learning for guided microstructure-property explorations in photovoltaics The microstructure determines the photovoltaic performance of a thin film organic semiconductor film. The relationship between microstructure and performance is usually highly non-linear and expensive to evaluate, thus making microstructure optimization challenging. Here, we show a data-driven approach for mapping the microstructure to photovoltaic performance using deep convolutional neural networks. We characterize this approach in terms of two critical metrics, its generalizability (has it learnt a reasonable map?), and its intepretability (can it produce meaningful microstructure characteristics that influence its prediction?). A surrogate model that exhibits these two features of generalizability and intepretability is particularly useful for subsequent design exploration. We illustrate this by using the surrogate model for both manual exploration (that verifies known domain insight) as well as automated microstructure optimization. We envision such approaches to be widely applicable to a wide variety of microstructure-sensitive design problems. Generalizability theory ; Microstructure ; Deep learning ; Convolutional neural network ; Photovoltaic system ; Domain (mathematical analysis) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Organic Electronics and Photovoltaics
Authors: Alberto Fabrizio | Andrea Grisafi | Benjamin Meyer | Michele Ceriotti | Clémence Corminbœuf
Source: ChemRxiv (preprint, 2019-06-04)
Field: Materials Science — Materials Chemistry

Insights into Non-Covalent Interactions with a Machine-Learned Electron Density Chemists continuously harvest the power of non-covalent interactions to control phenomena in both the micro- and macroscopic worlds. From the quantum chemical perspective, the strategies essentially rely upon an in-depth understanding of the physical origin of these interactions, the quantification of their magnitude and their visualization in real-space. The total electron density rho(r) represents the simplest yet most comprehensive piece of information available for fully characterizing bonding patterns and non-covalent interactions. The charge density of a molecule can be computed by solving the Schrodinger equation, but this approach becomes rapidly demanding if the electron density has to be evaluated for thousands of different molecules or for very large chemical systems, such as peptides and proteins. Here we present a transferable and scalable machine-learning model capable of predicting the total electron density directly from the atomic coordinates. The regression model is used to access qualitative and quantitative insights beyond the underlying rho(r) in a diverse ensemble of sidechain-sidechain dimers extracted from the BioFragment database (BFDb). The transferability of the model to more complex chemical systems is demonstrated by predicting and analyzing the electron density of a collection of 8 polypeptides. Electron density ; Transferability ; Covalent bond ; Computer science ; Molecule ; Electron ; Scalability ; Chemical physics ; Chemistry ; Physics ; Statistical physics ; Quantum mechanics ; Machine learning ; Database Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Anna Szczegielniak | Mariana Pinto da Costa | Márton Asztalos
Source: European Neuropsychopharmacology (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

A decade of research by the European Federation of Psychiatric Trainees (EFPT) - a review. Property (philosophy) ; Field (mathematics) ; Construct (python library) ; Simple (philosophy) ; Computer science ; Feature (linguistics) ; Process (computing) ; Artificial intelligence ; Association rule learning ; Data science ; Data mining ; Machine learning ; Mathematics ; Epistemology Machine Learning in Materials Science
Authors: Dalmeet Singh Chawla
Source: C&EN Global Enterprise (article, 2019-04-22)
Field: Chemistry — Physical and Theoretical Chemistry

Machine writes chemistry book Springer Nature, the world’s second-biggest academic publisher, has published the first scholarly book authored entirely by machines. The book, which is free to read and download, consists of four chapters summarizing studies about lithium-ion batteries. It is based on 150 papers published between 2016 and 2018 on SpringerLink, the publisher’s database of more than 1,200 scholarly journals. The algorithm sifts through studies analyzing keywords using similarity-based clustering, a computational technique often used in the fields of machine learning, pattern recognition, and image analysis. It groups together text on similar topics, producing succinct paraphrased summaries central to the topic of interest. According to Christian Chiarcos, a computational linguist at Goethe University Frankfurt who created the algorithm that wrote the book, the only thing users need to provide beforehand is the number of topical chapters and sections they want the generated book to have. “The book did a fairly good job in Computer science ; Chemistry Various Chemistry Research Topics ; Genetics, Bioinformatics, and Biomedical Research
Authors: Marco Bernasconi
Source: International Journal of Nanoscience (article, 2019-04-02)
Field: Materials Science — Materials Chemistry

Atomistic Simulations of Phase Change Materials for Electronic Memories We review our results on large-scale atomistic simulations of the phase change compound GeTe of interest for applications in nonvolatile electronic memories. The simulations are based on an interatomic potential with an accuracy close to that of the density functional theory (DFT). The potential was generated by fitting a DFT database by means of an artificial neural network method. This methodological advance allowed us to perform molecular dynamics simulations with several thousand atoms for several ns that provided useful insights on several properties of interest for the operation of phase change memories, including the crystallization kinetics, the dynamics of the supercooled liquid, the structural relaxation in the glass and the properties of nanowires. Supercooling ; Materials science ; Molecular dynamics ; Relaxation (psychology) ; Phase (matter) ; Chemical physics ; Density functional theory ; Crystallization ; Nanowire ; Phase change ; Nanotechnology ; Statistical physics ; Thermodynamics ; Computational chemistry ; Physics Phase-change materials and chalcogenides ; Chalcogenide Semiconductor Thin Films ; Machine Learning in Materials Science
Authors: Mark P. Oxley
Source: Microscopy and Microanalysis (article, 2019-08-01)
Field: Materials Science — Surfaces, Coatings and Films

Spatial and Temporal Incoherence in Scanning Diffraction and Differential Phase Contrast Recent developments in segmented detectors and pixelated detectors have led to a resurgence in differential phase contrast (DPC) and scanning diffraction or 4D STEM These new detectors, coupled with aberration correction, allow the use of -size probes and promise a wealth of information at atomic resolution. DPC can be used to measure local fields and the large data sets acquired from using pixelated detectors offer possibilities for novel machine learning approaches However, much of this work overlooks the influence of spatial incoherence (due to a finite electron source size) and temporal incoherence (due to chromatic aberrations and energy variations of the beam). In this work we address these issues for both monolayer and bulk crystalline materials. Contrast (vision) ; Differential (mechanical device) ; Diffraction ; Phase contrast microscopy ; Phase (matter) ; Optics ; Physics ; Materials science ; Thermodynamics Electron and X-Ray Spectroscopy Techniques ; Advancements in Photolithography Techniques ; Advanced X-ray Imaging Techniques