jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Marco Tulio Angulo | Claude H. Moog | Yang‐Yu Liu
Source: Nature Communications (article, 2019-03-05)
Field: Chemistry — Organic Chemistry

A theoretical framework for controlling complex microbial communities Abstract Microbes form complex communities that perform critical roles for the integrity of their environment or the well-being of their hosts. Controlling these microbial communities can help us restore natural ecosystems and maintain healthy human microbiota. However, the lack of an efficient and systematic control framework has limited our ability to manipulate these microbial communities. Here we fill this gap by developing a control framework based on the new notion of structural accessibility. Our framework uses the ecological network of the community to identify minimum sets of its driver species, manipulation of which allows controlling the whole community. We numerically validate our control framework on large communities, and then we demonstrate its application for controlling the gut microbiota of gnotobiotic mice infected with Clostridium difficile and the core microbiota of the sea sponge Ircinia oros . Our results provide a systematic pipeline to efficiently drive complex microbial communities towards desired states. Pipeline (software) ; Ecosystem ; Microbial population biology ; Computer science ; Ecology ; Complex network ; Metagenomics ; Control (management) ; Biology ; Environmental resource management ; Computational biology ; Environmental science ; Artificial intelligence ; Bacteria Chemical synthesis and alkaloids ; Evolutionary Game Theory and Cooperation ; Microbial Community Ecology and Physiology
Authors: Jatin Kumar | Qianxiao Li | Karen Yuanting Tang | Tonio Buonassisi | Anibal L. Gonzalez-Oyarce | Jun Ye
Source: npj Computational Materials (article, 2019-07-12)
Field: Materials Science — Materials Chemistry

Machine learning enables polymer cloud-point engineering via inverse design Abstract Inverse design is an outstanding challenge in disordered systems with multiple length scales such as polymers, particularly when designing polymers with desired phase behavior. Here we demonstrate high-accuracy tuning of poly(2-oxazoline) cloud point via machine learning. With a design space of four repeating units and a range of molecular masses, we achieve an accuracy of 4 °C root mean squared error (RMSE) in a temperature range of 24–90 °C, employing gradient boosting with decision trees. The RMSE is &gt;3x better than linear and polynomial regression. We perform inverse design via particle-swarm optimization, predicting and synthesizing 17 polymers with constrained design at 4 target cloud points from 37 to 80 °C. Our approach challenges the status quo in polymer design with a machine learning algorithm, that is capable of fast and systematic discovery of new polymers. Mean squared error ; Particle swarm optimization ; Computer science ; Inverse ; Point cloud ; Polymer ; Algorithm ; Machine learning ; Artificial intelligence ; Mathematical optimization ; Materials science ; Mathematics ; Statistics Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Advanced Polymer Synthesis and Characterization
Authors: Emanuele Boattini | Marjolein Dijkstra | Laura Filion
Source: The Journal of Chemical Physics (article, 2019-10-15)
Field: Materials Science — Materials Chemistry

Unsupervised learning for local structure detection in colloidal systems We introduce a simple, fast, and easy to implement unsupervised learning algorithm for detecting different local environments on a single-particle level in colloidal systems. In this algorithm, we use a vector of standard bond-orientational order parameters to describe the local environment of each particle. We then use a neural-network-based autoencoder combined with Gaussian mixture models in order to autonomously group together similar environments. We test the performance of the method on snapshots of a wide variety of colloidal systems obtained via computer simulations, ranging from simple isotropically interacting systems to binary mixtures, and even anisotropic hard cubes. Additionally, we look at a variety of common self-assembled situations such as fluid-crystal and crystal-crystal coexistences, grain boundaries, and nucleation. In all cases, we are able to identify the relevant local environments to a similar precision as "standard," manually tuned, and system-specific, order parameters. In addition to classifying such environments, we also use the trained autoencoder in order to determine the most relevant bond orientational order parameters in the systems analyzed. Autoencoder ; Unsupervised learning ; Binary number ; Pattern recognition (psychology) ; Local structure ; Variety (cybernetics) ; Local binary patterns ; Gaussian Machine Learning in Materials Science ; Material Dynamics and Properties ; Block Copolymer Self-Assembly
Authors: Hao Yang | Hao Chi | Wenfeng Zeng | Wenjing Zhou | Si‐Min He
Source: Bioinformatics (article, 2019-05-10)
Field: Chemistry — Spectroscopy

pNovo 3: precise <i>de novo</i> peptide sequencing using a learning-to-rank framework Supplementary data are available at Bioinformatics online. Computer science ; Benchmark (surveying) ; Deep learning ; Artificial intelligence ; Protein methods ; Rank (graph theory) ; Software ; Shotgun proteomics ; Deep sequencing ; Computational biology ; DNA sequencing ; Sequence assembly ; Machine learning ; Data mining ; Proteomics ; Peptide sequence ; Biology ; Genome ; Mathematics ; Genetics ; Gene Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Machine Learning in Bioinformatics
Authors: Estefanía Garijo del Río | Jens Jørgen Mortensen | Karsten W. Jacobsen
Source: Physical review. B./Physical review. B (article, 2019-09-05)
Field: Materials Science — Materials Chemistry

Local Bayesian optimizer for atomic structures A local optimization method based on Bayesian Gaussian processes is developed and applied to atomic structures. The method is applied to a variety of systems including molecules, clusters, bulk materials, and molecules at surfaces. The approach is seen to compare favorably to standard optimization algorithms like the conjugate gradient or Broyden-Fletcher-Goldfarb-Shanno in all cases. The method relies on prediction of surrogate potential energy surfaces, which are fast to optimize, and which are gradually improved as the calculation proceeds. The method includes a few hyperparameters, the optimization of which may lead to further improvements of the computational speed. Bayesian optimization ; Hyperparameter ; Computer science ; Conjugate gradient method ; Bayesian probability ; Gaussian ; Algorithm ; Mathematical optimization ; Variety (cybernetics) ; Mathematics ; Artificial intelligence ; Computational chemistry ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Nicolas Sbirrazzuoli
Source: Molecules (article, 2019-04-30)
Field: Materials Science — Materials Chemistry

Advanced Isoconversional Kinetic Analysis for the Elucidation of Complex Reaction Mechanisms: A New Method for the Identification of Rate-Limiting Steps Two complex cure mechanisms were simulated. Isoconversional kinetic analysis was applied to the resulting data. The study highlighted correlations between the reaction rate, activation energy dependency, rate constants for the chemically controlled part of the reaction and the diffusion-controlled part, activation energy and pre-exponential factors of the individual steps and change in rate-limiting steps. It was shown how some parameters computed using Friedman's method can help to identify change in the rate-limiting steps of the overall polymerization mechanism as measured by thermoanalytical techniques. It was concluded that the assumption of the validity of a single-step equation when restricted to a given <i>α</i> value holds for complex reactions. The method is not limited to chemical reactions, but can be applied to any complex chemical or physical transformation. Activation energy ; Thermodynamics ; Limiting ; Kinetic energy ; Chemistry ; Reaction mechanism ; Reaction rate constant ; Reaction rate ; Diffusion ; Rate-determining step ; Polymerization ; Rate equation ; Biological system ; Physical chemistry ; Kinetics ; Organic chemistry ; Polymer ; Physics Thermal and Kinetic Analysis ; Epoxy Resin Curing Processes ; Injection Molding Process and Properties
Authors: Mahalaxmi Samal | Sreeramulu Valligatla | Nabil A. Saad | M. Veeramohan Rao | D. Narayana Rao | Rojalin Sahu | Bishnu P. Biswal
Source: Chemical Communications (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

A thiazolo[5,4-<i>d</i>]thiazole-bridged porphyrin organic framework as a promising nonlinear optical material Porphyrin-based porous organic frameworks are an important group of materials gaining interest due to their structural diversity and distinct opto-electronic properties. However, these materials are seldom explored for nonlinear optical (NLO) applications. In this work, we investigate a thiazolo[5,4-d]thiazole-bridged porous, porphyrin framework (Por-TzTz-POF) with promising NLO properties. The planar TzTz moiety coupled with integrated porphyrin units enables efficient π-conjugation and charge distribution in the Por-TzTz-POF resulting in a high nonlinear absorption coefficient (β = 1100 cm GW<sup>-1</sup>) with figure of merit (FoM) σ<sub>1</sub>/σ<sub>0</sub> = 5571, in contrast to analogous molecules and material counterparts e.g. metal-organic frameworks (MOFs; β = ∼0.3-0.5 cm GW<sup>-1</sup>), molecular porphyrins (β = ∼100-400 cm GW<sup>-1</sup>), graphene (β = 900 cm GW<sup>-1</sup>), and covalent organic frameworks (Por-COF-HH; β = 1040 cm GW<sup>-1</sup> and FoM = 3534). Porphyrin ; Thiazole ; Nonlinear optical ; Combinatorial chemistry ; Chemistry ; Nonlinear system ; Materials science ; Photochemistry ; Organic chemistry ; Physics Porphyrin and Phthalocyanine Chemistry ; Luminescence and Fluorescent Materials ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Mümin Mehmet Koç | Naim Aslan | Alexander P. Kao | Asa H. Barber
Source: Microscopy Research and Technique (review, 2019-02-20)
Field: Materials Science — Biomaterials

Evaluation of X‐ray tomography contrast agents: A review of production, protocols, and biological applications X-ray computed tomography is a strong tool that finds many applications both in medical applications and in the investigation of biological and nonbiological samples. In the clinics, X-ray tomography is widely used for diagnostic purposes whose three-dimensional imaging in high resolution helps physicians to obtain detailed image of investigated regions. Researchers in biological sciences and engineering use X-ray tomography because it is a nondestructive method to assess the structure of their samples. In both medical and biological applications, visualization of soft tissues and structures requires special treatment, in which special contrast agents are used. In this detailed report, molecule-based and nanoparticle-based contrast agents used in biological applications to enhance the image quality were compiled and reported. Special contrast agent applications and protocols to enhance the contrast for the biological applications and works to develop nanoparticle contrast agents to enhance the contrast for targeted drug delivery and general imaging applications were also assessed and listed. Contrast (vision) ; Tomography ; Computed tomography ; Computer science ; Medical imaging ; Medical physics ; Drug delivery ; Image contrast ; Biomedical engineering ; Medicine ; Artificial intelligence ; Nanotechnology ; Radiology ; Materials science Nanoparticle-Based Drug Delivery ; Lanthanide and Transition Metal Complexes ; Nanoplatforms for cancer theranostics
Authors: George S. Fanourgakis | Konstantinos Gkagkas | Emmanuel Tylianakis | Emmanuel Klontzas | George E. Froudakis
Source: The Journal of Physical Chemistry A (article, 2019-06-07)
Field: Chemistry — Inorganic Chemistry

A Robust Machine Learning Algorithm for the Prediction of Methane Adsorption in Nanoporous Materials In the present study, we propose a new set of descriptors that, along with a few structural features of nanoporous materials, can be used by machine learning algorithms for accurate predictions of the gas uptake capacities of these materials. All new descriptors closely resemble the helium atom void fraction of the material framework. However, instead of a helium atom, a particle with an appropriately defined van der Waals radius is used. The set of void fractions of a small number of these particles is found to be sufficient to characterize uniquely the structure of each material and to account for the most important topological features. We assess the accuracy of our approach by examining the predictions of the random forest algorithm in the relative small dataset of the computation-ready, experimental (CoRE) MOFs (∼4700 structures) that have been experimentally synthesized and whose geometrical/structural features have been accurately calculated before. We first performed grand canonical Monte Carlo simulations to accurately determine their methane uptake capacities at two different temperatures (280 and 298 K) and three different pressures (1, 5.8, and 65 bar). Despite the high chemical and structural diversity of the CoRE MOFs, it was found that the use of the proposed descriptors significantly improves the accuracy of the machine learning algorithm, particularly at low pressures, compared to the predictions made based solely on the rest structural features. More importantly, the algorithm can be easily adapted for other types of nanoporous materials beyond MOFs. Convergence of the predictions was reached even for small training set sizes compared to what was found in previous works using the hypothetical MOF database. Nanoporous ; Algorithm ; van der Waals force ; Computation ; Monte Carlo method ; Convergence (economics) ; Materials science ; Computer science ; Adsorption ; Set (abstract data type) ; Statistical physics ; Mathematics ; Physics ; Molecule ; Chemistry ; Nanotechnology ; Physical chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis
Authors: Altug Akay | Henry Hess
Source: IEEE Journal of Biomedical and Health Informatics (review, 2019-01-23)
Field: Materials Science — Materials Chemistry

Deep Learning: Current and Emerging Applications in Medicine and Technology Machine learning is enabling researchers to analyze and understand increasingly complex physical and biological phenomena in traditional fields such as biology, medicine, and engineering and emerging fields like synthetic biology, automated chemical synthesis, and biomanufacturing. These fields require new paradigms toward understanding increasingly complex data and converting such data into medical products and services for patients. The move toward deep learning and complex modeling is an attempt to bridge the gap between acquiring massive quantities of complex data, and converting such data into practical insights. Here, we provide an overview of the field of machine learning, its current applications and needs in traditional and emerging fields, and discuss an illustrative attempt at using deep learning to understand swarm behavior of molecular shuttles. Deep learning ; Computer science ; Data science ; Artificial intelligence ; Big data ; Field (mathematics) ; Biomanufacturing ; Bridge (graph theory) ; Data mining ; Medicine ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics
Authors: Wei Hu | Sheng Ye | Yujin Zhang | Tianduo Li | Guozhen Zhang | Yi Luo | Shaul Mukamel | Jun Jiang
Source: The Journal of Physical Chemistry Letters (article, 2019-09-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning Protocol for Surface-Enhanced Raman Spectroscopy Surface-enhanced Raman spectroscopy (SERS) is a powerful technique that can capture the electronic-vibrational "fingerprint" of molecules on surfaces. Ab initio prediction of Raman response is a long-standing challenge because of the diversified interfacial structures. Here we show that a cost-effective machine learning (ML) random forest method can predict SERS signals of a <i>trans</i>-1,2-bis (4-pyridyl) ethylene (BPE) molecule adsorbed on a gold substrate. Using geometric descriptors extracted from quantum chemistry simulations of thousands of ab initio molecular dynamics conformations, the ML protocol predicts vibrational frequencies and Raman intensities. The resulting spectra agree with density functional theory calculations and experiment. Predicted SERS responses of the molecule on different surfaces, or under external fields of electric fields and solvent environment, demonstrate the good transferability of the protocol. Raman spectroscopy ; Ab initio ; Transferability ; Molecule ; Density functional theory ; Surface-enhanced Raman spectroscopy ; Chemical physics ; Chemistry ; Substrate (aquarium) ; Ab initio quantum chemistry methods ; Raman scattering ; Computational chemistry ; Molecular physics ; Materials science ; Computer science ; Physics ; Machine learning ; Optics ; Organic chemistry Gold and Silver Nanoparticles Synthesis and Applications ; Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies
Authors: Ruihao Yuan | Yuan Tian | Dezhen Xue | Deqing Xue | Yumei Zhou | Xiangdong Ding | Jun Sun | Turab Lookman
Source: Advanced Science (article, 2019-09-02)
Field: Materials Science — Materials Chemistry

Accelerated Search for BaTiO<sub>3</sub>‐Based Ceramics with Large Energy Storage at Low Fields Using Machine Learning and Experimental Design The problem that is considered is that of maximizing the energy storage density of Pb-free BaTiO<sub>3</sub>-based dielectrics at low electric fields. It is demonstrated that how varying the size of the combinatorial search space influences the efficiency of material discovery by comparing the performance of two machine learning based approaches where different levels of physical insights are involved. It is started with physics intuition to provide guiding principles to find better performers lying in the crossover region in the composition-temperature phase diagram between the ferroelectric phase and relaxor ferroelectric phase. Such an approach is limiting for multidopant solid solutions and motivates the use of two data-driven machine learning and design strategies with a feedback loop to experiments. Strategy I considers learning and property prediction on all the compounds, and strategy II learns to preselect compounds in the crossover region on which prediction is carried out. By performing only two active learning loops via strategy II, the compound (Ba<sub>0.86</sub>Ca<sub>0.14</sub>)(Ti<sub>0.79</sub>Zr<sub>0.11</sub>Hf<sub>0.10</sub>)O<sub>3</sub> is synthesized with the largest energy storage density ≈73 mJ cm<sup>-3</sup> at a field of 20 kV cm<sup>-1</sup>, and an insight into the relative performance of the strategies using varying levels of knowledge is provided. Ferroelectricity ; Crossover ; Dielectric ; Intuition ; Phase diagram ; Limiting ; Computer science ; Ceramic ; Materials science ; Energy storage ; Machine learning ; Artificial intelligence ; Phase (matter) ; Physics ; Mechanical engineering ; Thermodynamics ; Optoelectronics ; Engineering Ferroelectric and Piezoelectric Materials ; Ferroelectric and Negative Capacitance Devices ; Machine Learning in Materials Science
Authors: Amir Reza Fahami | Tobias Günter | Dmitry E. Doronkin | Maria Casapu | Deniz Zengel | Thanh Huyen Vuong | Michaël Simon | Frank Breher | A.V. Kucherov | Angelika Brückner | Jan‐Dierk Grunwaldt
Source: Reaction Chemistry & Engineering (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

The dynamic nature of Cu sites in Cu-SSZ-13 and the origin of the seagull NO<sub>x</sub> conversion profile during NH<sub>3</sub>-SCR Systematic catalytic tests and <italic>in situ</italic>/<italic>operando</italic> spectroscopy uncovered structure–performance relationships determining the seagull profile of the NO<sub>x</sub> conversion for Cu-SSZ-13 catalysts. Catalysis ; Copper ; Chemistry ; Materials science ; Crystallography ; Metallurgy ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Luminescence Properties of Advanced Materials
Authors: Haidong Lu | Yueze Tan | James P. V. McConville | Zahra Ahmadi | Bo Wang | Michele Conroy | Kalani Moore | U. Bangert | Jeffrey E. Shield | Long‐Qing Chen | J. M. Gregg | Alexei Gruverman
Source: Advanced Materials (article, 2019-10-07)
Field: Materials Science — Materials Chemistry

Electrical Tunability of Domain Wall Conductivity in LiNbO<sub>3</sub> Thin Films Domain wall nanoelectronics is a rapidly evolving field, which explores the diverse electronic properties of the ferroelectric domain walls for application in low-dimensional electronic systems. One of the most prominent features of the ferroelectric domain walls is their electrical conductivity. Here, using a combination of scanning probe and scanning transmission electron microscopy, the mechanism of the tunable conducting behavior of the domain walls in the sub-micrometer thick films of the technologically important ferroelectric LiNbO<sub>3</sub> is explored. It is found that the electric bias generates stable domains with strongly inclined domain boundaries with the inclination angle reaching 20° with respect to the polar axis. The head-to-head domain boundaries exhibit high conductance, which can be modulated by application of the sub-coercive voltage. Electron microscopy visualization of the electrically written domains and piezoresponse force microscopy imaging of the very same domains reveals that the gradual and reversible transition between the conducting and insulating states of the domain walls results from the electrically induced wall bending near the sample surface. The observed modulation of the wall conductance is corroborated by the phase-field modeling. The results open a possibility for exploiting the conducting domain walls as the electrically controllable functional elements in the multilevel logic nanoelectronics devices. Materials science ; Thin film ; Electrical resistivity and conductivity ; Conductivity ; Composite material ; Optoelectronics ; Nanotechnology ; Physical chemistry Ferroelectric and Piezoelectric Materials ; Semiconductor materials and devices ; Electronic and Structural Properties of Oxides