jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Soumyajyoti Biswas | David Fernández Castellanos | Michael Zaiser
Source: Scientific Reports (preprint, 2020-10-09)
Field: Materials Science — Materials Chemistry

Prediction of creep failure time using machine learning A subcritical load on a disordered material can induce creep damage. The creep rate in this case exhibits three temporal regimes viz. an initial decelerating regime followed by a steady-state regime and a stage of accelerating creep that ultimately leads to catastrophic breakdown. Due to the statistical regularities in the creep rate, the time evolution of creep rate has often been used to predict residual lifetime until catastrophic breakdown. However, in disordered samples, these efforts met with limited success. Nevertheless, it is clear that as the failure is approached, the damage become increasingly spatially correlated, and the spatio-temporal patterns of acoustic emission, which serve as a proxy for damage accumulation activity, are likely to mirror such correlations. However, due to the high dimensionality of the data and the complex nature of the correlations it is not straightforward to identify the said correlations and thereby the precursory signals of failure. Here we use supervised machine learning to estimate the remaining time to failure of samples of disordered materials. The machine learning algorithm uses as input the temporal signal provided by a mesoscale elastoplastic model for the evolution of creep damage in disordered solids. Machine learning algorithms are well-suited for assessing the proximity to failure from the time series of the acoustic emissions of sheared samples. We show that materials are relatively more predictable for higher disorder while are relatively less predictable for larger system sizes. We find that machine learning predictions, in the vast majority of cases, perform substantially better than other prediction approaches proposed in the literature. Creep ; Residual ; Computer science ; Catastrophic failure ; Mesoscale meteorology ; Curse of dimensionality ; Materials science ; Artificial intelligence ; Machine learning ; Statistical physics ; Algorithm ; Physics ; Composite material Material Properties and Failure Mechanisms ; Landslides and related hazards ; Rock Mechanics and Modeling
Authors: Jonas Verhellen | Jeriek Van den Abeele
Source: ChemRxiv (preprint, 2020-07-05)
Field: Materials Science — Materials Chemistry

Illuminating Elite Patches of Chemical Space In the past few years, there has been considerable activity in both academic and industrial research to develop innovative machine learning approaches to locate novel, high-performing molecules in chemical space. Here we describe a new and fundamentally different type of approach that provides a holistic overview of how high-performing molecules are distributed throughout a search space. Based on an open-source, graph-based implementation [Jensen, Chem. Sci., 2019, 12, 3567-3572] of a traditional genetic algorithm for molecular optimisation, and influenced by state-of-the-art concepts from soft robot design [Mouret et al., IEEE Trans. Evolut. Comput., 2016, 22, 623-630], we provide an algorithm that (i) produces a large diversity of high-performing, yet qualitatively different molecules, (ii) illuminates the distribution of optimal solutions, and (iii) improves search efficiency compared to both machine learning and traditional genetic algorithm approaches. Computer science ; Space (punctuation) ; Chemical space ; Artificial intelligence ; Genetic algorithm ; Graph ; Theoretical computer science ; Machine learning ; Biology ; Bioinformatics ; Drug discovery Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Computational Drug Discovery Methods
Authors: Youngho Jung | Junho Jeong | Zhongnan Qu | Bin Cui | Ankita Khanda | S. Parkin | Joyce K. S. Poon
Source: arXiv (Cornell University) (preprint, 2020-10-23)
Field: Materials Science — Polymers and Plastics

Observation of photoelectric nonvolatile memory and oscillations in VO2 at room temperature Vanadium dioxide (VO2) is a phase change material that can reversibly change between high and low resistivity states through electronic and structural phase transitions. Thus far, VO2 memory devices have essentially been volatile at room temperature, and nonvolatile memory has required non-ambient surroundings (e.g., elevated temperatures, electrolytes) and long write times. Here, we report the first observation of optically addressable nonvolatile memory in VO2 at room temperature with a readout by voltage oscillations. The read and write times had to be kept shorter than about 150 μs. The writing of the memory and onset of the voltage oscillations had a minimum optical power threshold. This discovery demonstrates the potential of VO2 for new computing devices and architectures, such as artificial neurons and oscillatory neural networks. Photoelectric effect ; Non-volatile memory ; Materials science ; Environmental science ; Optoelectronics Transition Metal Oxide Nanomaterials ; Gas Sensing Nanomaterials and Sensors ; Photoacoustic and Ultrasonic Imaging
Authors: Tien-Lam Pham | Nguyễn Văn Duy | Nguyễn Tiến Cường
Source: VNU Journal of Science Mathematics - Physics (article, 2020-06-26)
Field: Materials Science — Materials Chemistry

Machine Learning Representation for Atomic Forces and Energies We present machine learning models for fast estimating atomic forces. In our method, the total energy of a system is approximated as the summation of atomic energy which is the interaction energy with its surrounding chemical environment within a certain cutoff radius. Atomic energy is decomposed into two-body terms which are expressed as a linear combination of basis functions. For the force exerted on an atom, we employ a linear combination of a set of basis functions for representing pairwise force. We use least-square linear regression regularized by the l2-norm, known as Ridge regression, to estimate model parameters. We demonstrate that our model can accurately reproduce atomic forces and energies from density-functional-theory (DFT) calculations for crystalline and amorphous silicon. The machine learning force model is then applied to calculate the phonon dispersion of crystalline silicon. The result shows reasonable agreement with DFT calculations. Cutoff ; Density functional theory ; Atom (system on chip) ; Statistical physics ; Chemistry ; Physics ; Quantum mechanics ; Computer science Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Surface and Thin Film Phenomena
Authors: Tanner Kirk
Source: OakTrust (Texas A&M University Libraries) (article, 2020-12-07)
Field: Materials Science — Ceramics and Composites

Computational Design of Compositionally Graded Alloys In this work, a new computational methodology is presented for the design of compositionally graded alloys. Compositionally graded alloys are a class of functionally graded materials, or materials which exhibit spatially varying properties. While the introduction of additive manufacturing has accelerated interest in these materials, there are many challenges that impede their development like the formation of deleterious phases and material compositions that are incompatible with manufacturing processes. Previous design methods have attempted to design gradients that avoid these issues, but such methods have been limited to the analysis and interpretation of two-dimensional diagrams and are therefore hindered by the limits of human visualization and ideation.\nThe proposed methodology is made possible by the novel formulation of gradient design as a path planning problem. This formulation allows the use of path planning algorithms to optimize gradient paths in composition space. Such algorithms can optimize gradients with any number of constituent elements to meet specified design requirements or objectives. To make the gradient design problem tractable for such algorithms, surrogate modeling techniques are employed to represent design constraints and objectives. Constraints, like deleterious phase formation, can be predicted by CALPHAD software and then modeled by a machine learning classifier. Similarly, regression models can be trained to evaluate cost functions in an efficient manner.\nSeveral unique problem formulations are demonstrated to showcase the advantages of the methodology in gradient design. Among these are constraints to avoid deleterious phase regions and other regions of the state space with poor predicted manufacturability. Common cost functions in the path planning community, like path length and obstacle clearance, are shown to be useful for some problems, while including constraint violation as penalty term is demonstrated to satisfy constraints that might otherwise be unachievable. Lastly, a novel cost function is proposed to design gradients with monotonic properties, which can achieve nearly any bounded property distribution on a gradient part. All proposed problem formulations are demonstrated in the design of authentic compositionally graded alloys and experiments are used to validate predicted results. Computer science ; Materials science Advanced ceramic materials synthesis ; Aluminum Alloys Composites Properties ; Powder Metallurgy Techniques and Materials
Authors: Yu Kitamura | Emi Terado | Zechen Zhang | Hirofumi Yoshikawa | Tomoko Inose | Hiroshi Uji‐i | Masaharu Tanimizu | Akihiro Inokuchi | Yoshinobu Kamakura | Daisuke Tanaka
Source: ChemRxiv (preprint, 2020-12-29)
Field: Chemistry — Inorganic Chemistry

Failure-experiment-supported optimization of poorly reproducible synthetic conditions for novel lanthanide metal-organic frameworks with two-dimensional secondary building units A series of novel metal organic frameworks with lanthanide double-layer-based inorganic subnetworks (KGF-3) was synthesized assisted by machine learning. Pure KGF-3 was difficult to isolate in the initial screening experiments. The synthetic conditions were successfully optimized by extracting the dominant factors for KGF-3 synthesis using two machine-learning techniques. Cluster analysis was used to classify the obtained PXRD patterns of the products and to decide automatically whether the experiments were successful or had failed. Decision tree analysis was used to visualize the experimental results, with the factors that mainly affected the synthetic reproducibility being extracted. The water adsorption isotherm revealed that KGF-3 possesses unique hydrophilic pores, and impedance measurements demonstrated good proton conductivities (σ = 5.2 × 10&lt;sup&gt;−4&lt;/sup&gt; S cm&lt;sup&gt;−1&lt;/sup&gt; for KGF-3(Y)) at a high temperature (363 K) and high relative humidity (95%).&lt;br&gt; Lanthanide ; Materials science ; Metal-organic framework ; Adsorption ; Metal ; Relative humidity ; Reproducibility ; Analytical Chemistry (journal) ; Chemistry ; Chromatography ; Thermodynamics ; Organic chemistry ; Metallurgy ; Physics Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Organic and Molecular Conductors Research
Authors: Chengqiang Lu | Qi Liu | Qiming Sun | Chang‐Yu Hsieh | Shengyu Zhang | Liang Shi | Chee‐Kong Lee
Source: Figshare (preprint, 2020-03-11)
Field: Materials Science — Materials Chemistry

Deep Learning for Optoelectronic Properties of Organic\nSemiconductors Atomistic\nmodeling of the optoelectronic properties of organic\nsemiconductors (OSCs) requires a large number of excited-state electronic-structure\ncalculations, a computationally daunting task for many OSC applications.\nIn this work, we advocate the use of deep learning to address this\nchallenge and demonstrate that state-of-the-art deep neural networks\n(DNNs) are capable of accurately predicting various electronic properties\nof an important class of OSCs, i.e., oligothiophenes (OTs), including\ntheir HOMO and LUMO energies, excited-state energies and associated\ntransition dipole moments. Among the tested DNNs, SchNet shows the\nbest performance for OTs of different sizes, achieving average prediction\nerrors in the range of 20–80 meV. We show that SchNet also\nconsistently outperforms shallow feed-forward neural networks, especially\nin difficult cases with large molecules or limited training data.\nWe further show that SchNet could predict the transition dipole moment\naccurately, a task previously known to be difficult for feed-forward\nneural networks, and we ascribe the relatively large errors in transition\ndipole prediction seen for some OT configurations to the charge-transfer\ncharacter of their excited states. Finally, we demonstrate the effectiveness\nof SchNet by modeling the UV–vis absorption spectra of OTs\nin dichloromethane, and a good agreement is observed between the calculated\nand experimental spectra. Dipole ; Artificial neural network ; Excited state ; Computer science ; Deep learning ; Artificial intelligence ; Convolutional neural network ; Electronic structure ; Organic semiconductor ; Materials science ; Machine learning ; Physics ; Optoelectronics ; Condensed matter physics ; Atomic physics ; Quantum mechanics Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Conducting polymers and applications
Authors: Kaiqi Yang | Yifan Cao | Youtian Zhang | Ming Tang | Daniel Åberg | Babak Sadigh | Fei Zhou
Source: arXiv (Cornell University) (preprint, 2020-08-17)
Field: Materials Science — Materials Chemistry

Self-Supervised Learning and Prediction of Microstructure Evolution with Recurrent Neural Networks Microstructural evolution is a key aspect of understanding and exploiting the structure-property-performance relation of materials. Modeling microstructure evolution usually relies on coarse-grained simulations with evolution principles described by partial differential equations (PDEs). Here we demonstrate that convolutional recurrent neural networks can learn the underlying physical rules and replace PDE-based simulations in the prediction of microstructure phenomena. Neural nets are trained by self-supervised learning with image sequences from simulations of several common processes, including plane wave propagation, grain growth, spinodal decomposition and dendritic crystal growth. The trained networks can accurately predict both short-term local dynamics and long-term statistical properties of microstructures and is capable of extrapolating beyond the training datasets in spatiotemporal domains and configurational and parametric spaces. Such a data-driven approach offers significant advantages over PDE-based simulations in time stepping efficiency and offers a useful alternative especially when the material parameters or governing PDEs are not well determined. Computer science ; Artificial intelligence ; Microstructure ; Artificial neural network ; Term (time) ; Parametric statistics ; Convolutional neural network ; Relation (database) ; Algorithm ; Machine learning ; Statistical physics ; Mathematics ; Materials science ; Physics ; Data mining Machine Learning in Materials Science ; Neural Networks and Applications ; Theoretical and Computational Physics
Authors: Julien Baderot | Bertrand Darbon | Clément Nicolas | Matthew Bryan | Sergio Martínez | J. Foucher
Source:  (article, 2020-03-23)
Field: Materials Science — Surfaces, Coatings and Films

Machine Learning assistant technology to facilitate Fin and 3D memory measurements on SEM and TEM images We present a machine learning-based metrology pipeline for electron microscope imagery in the semiconductor industry. The pipeline is targeted to reduce the time spent by Process Engineers during research and development, by automating measurements of features according to their instructions in the form of a “measurement recipe”. Specifically, we present the principles and functionality of tools to measure Fin and 3D Memory structures based on edge finding algorithms, including through direct modelling of the SEM acquisition process to better capture blurred-appearing features. Pipeline (software) ; Recipe ; Process (computing) ; Computer science ; Metrology ; Measure (data warehouse) ; Enhanced Data Rates for GSM Evolution ; Fin ; Semiconductor industry ; Pipeline transport ; Artificial intelligence ; Engineering drawing ; Engineering ; Manufacturing engineering ; Mechanical engineering ; Data mining ; Operating system ; Optics Electron and X-Ray Spectroscopy Techniques ; Advancements in Photolithography Techniques ; Industrial Vision Systems and Defect Detection
Authors: Shae-Lynn Lahey | Từ Nguyễn Thiên Phúc | Christopher N. Rowley
Source: ChemRxiv (preprint, 2020-08-10)
Field: Chemistry — Spectroscopy

Benchmarking Force Field and the ANI Neural Network Potentials for the Torsional Potential Energy Surface of Biaryl Drug Fragments Many drug molecules contain biaryl fragments, resulting in a torsional barrier corresponding to rotation around the bond linking the aryls. The potential energy surfaces of these torsions vary significantly due to steric and electronic effects, ultimately affecting the relative stability of the molecular conformations in the protein-bound and solution states. Simulations of protein--ligand binding require accurate computational models to represent the intramolecular interactions to provide accurate predictions of the structure and dynamics of binding. In this paper, we compare four force fields (Generalized AMBER Force Field (GAFF), Open Force Field (OpenFF), CHARMM General Force Field (CGenFF), Optimized Potentials for Liquid Simulations (OPLS)) and two neural network potentials (ANI-2x, ANI-1ccx) in their ability to predict the torsional potential energy surfaces of 88 biaryls extracted from drug fragments. The mean of the absolute deviation over the full PES (MADF) and the mean absolute deviation of the torsion rotational barrier height (MADB) relative to high-level ab initio reference data was used as a measure of accuracy. In comparison to high-level ab-initio data, ANI-1ccx was most accurate for predicting the barrier height (MADF: 0.5~kcal/mol, MADB:~0.8~kcal/mol), followed closely by ANI-2x (MADF: 0.5~kcal/mol, MADB:~1.0~kcal/mol), then CGenFF (MADF: 0.8~kcal/mol, MADB: 1.3~kcal/mol), OpenFF (MADF: 1.5~kcal/mol, MADB: 1.4~kcal/mol), GAFF (MADF: 1.2~kcal/mol, MADB: 2.6~kcal/mol), and finally OPLS (MADF: 1.5~kcal/mol, MADB: 2.8~kcal/mol). Significantly, the NNPs are systematically more accurate and more reliable than any of the force fields. As a practical example, the neural network potential/molecular mechanics (NNP/MM) method was used to simulate the isomerization of ozanimod, a drug used for multiple sclerosis. Multi-nanosecond molecular dynamics (MD) simulations in an explicit aqueous solvent were performed, as well as umbrella sampling and adaptive biasing force enhanced sampling techniques. These theories predicted a rate of isomerization of $4.30 \times 10^{-1}$ ns$^{-1}$, which is consistent with direct MD simulations. Force field (fiction) ; Chemistry ; Steric effects ; Ab initio ; Intramolecular force ; Computational chemistry ; Potential energy surface ; Binding energy ; Molecule ; Potential energy ; Molecular dynamics ; Crystallography ; Thermodynamics ; Stereochemistry ; Physics ; Atomic physics ; Organic chemistry Molecular spectroscopy and chirality ; Protein Structure and Dynamics ; Chemical Synthesis and Analysis
Authors: Emil Annevelink | Rachel C. Kurchin | Eric S. Muckley | Lance Kavalsky | Vinay I. Hegde | Valentin Sulzer | Shang Zhu | Jiankun Pu | David Fariña | Matthew S. Johnson | Dhairya Gandhi | Adarsh Dave | Hongyi Lin | Alan Edelman | Bharath Ramsundar | James E. Saal | Christopher Rackauckas | Viral N. Shah | Bryce Meredig | Venkatasubramanian Viswanathan
Source: arXiv (Cornell University) (preprint, 2020-11-03)
Field: Materials Science — Materials Chemistry

AutoMat: Accelerated Computational Electrochemical systems Discovery Large-scale electrification is vital to addressing the climate crisis, but several scientific and technological challenges remain to fully electrify both the chemical industry and transportation. In both of these areas, new electrochemical materials will be critical, but their development currently relies heavily on human-time-intensive experimental trial and error and computationally expensive first-principles, meso-scale and continuum simulations. We present an automated workflow, AutoMat, that accelerates these computational steps by introducing both automated input generation and management of simulations across scales from first principles to continuum device modeling. Furthermore, we show how to seamlessly integrate multi-fidelity predictions such as machine learning surrogates or automated robotic experiments "in-the-loop". The automated framework is implemented with design space search techniques to dramatically accelerate the overall materials discovery pipeline by implicitly learning design features that optimize device performance across several metrics. We discuss the benefits of AutoMat using examples in electrocatalysis and energy storage and highlight lessons learned. Workflow ; Computer science ; Fidelity ; Pipeline (software) ; Systems engineering ; Electrification ; Scale (ratio) ; Data science ; Artificial intelligence ; Engineering Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: Zhongyang Zhu | Guangmin Sun | Anqi Liu | Cunfu He
Source: IEEE Transactions on Instrumentation and Measurement (article, 2020-01-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

An Evolutionary Computation-Based Method for Nondestructive Tensile Force Measurements in Two Materials Using Hysteresis Loop In traditional methods, features extracted from the hysteresis loop are manually analyzed to establish a model for predicting tensile force in one ferromagnetic material. However, the hysteresis loop is sensitive to material type, thus increasing the difficulty in predicting tensile forces in two materials. Moreover, manual feature extraction is time consuming. It is necessary to complete automatic feature extraction and elimination of the influence of material type on the hysteresis loop in order to automatically establish a prediction model for tensile force prediction in two materials. In this article, an evolutionary computation-based method was proposed to perform the above-mentioned two tasks simultaneously. The proposed method involves three steps. First, hysteresis loops of two materials under different tensile forces were measured. Second, the measured hysteresis loops were transformed into corresponding transformed ascending hysteresis curves through a transformation operation of the curve of hysteresis loop change (CHLC) for eliminating the influence of material type on the hysteresis loop. Third, an improved bat algorithm was proposed to eliminate further the influence of material type on the transformed ascending hysteresis curve. The improved bat algorithm iteratively gave the position of a material type-insensitive feature on the transformed ascending hysteresis curve. The material type-insensitive feature-based neural network was used to predict tensile forces in two materials. Experimental results verified the proposed method. The motivation of obtaining a general tensile force prediction model, the applicability of the proposed method in more than two materials, and the influence of CHLC transformation operation on the improved bat algorithm and neural network are also discussed. Hysteresis ; Ultimate tensile strength ; Materials science ; Loop (graph theory) ; Computation ; Control theory (sociology) ; Transformation (genetics) ; Magnetic hysteresis ; Algorithm ; Computer science ; Composite material ; Artificial intelligence ; Mathematics ; Physics ; Magnetization ; Condensed matter physics ; Chemistry Magnetic Properties and Applications ; Neural Networks and Applications ; Evolutionary Algorithms and Applications