jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dimitri Abrahamsson | June-Soo Park | Randolph R. Singh | Marina Sirota | Tracey J. Woodruff
Source: Journal of Chemical Information and Modeling (article, 2020-05-07)
Field: Chemistry — Spectroscopy

Applications of Machine Learning to In Silico Quantification of Chemicals without Analytical Standards Non-targeted analysis provides a comprehensive approach to analyze environmental and biological samples for nearly all chemicals present. One of the main shortcomings of current analytical methods and workflows is that they are unable to provide any quantitative information constituting an important obstacle in understanding environmental fate and human exposure. Herein, we present an in silico quantification method using mahine-learning for chemicals analyzed using electrospray ionization (ESI). We considered three data sets from different instrumental setups: (i) capillary electrophoresis electrospray ionization-mass spectrometry (CE-MS) in positive ionization mode (ESI+), (ii) liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF/MS) in ESI+ and (iii) LC-QTOF/MS in negative ionization mode (ESI-). We developed and applied two different machine-learning algorithms: a random forest (RF) and an artificial neural network (ANN) to predict the relative response factors (RRFs) of different chemicals based on their physicochemical properties. Chemical concentrations can then be calculated by dividing the measured abundance of a chemical, as peak area or peak height, by its corresponding RRF. We evaluated our models and tested their predictive power using 5-fold cross-validation (CV) and <i>y</i> randomization. Both the RF and the ANN models showed great promise in predicting RRFs. However, the accuracy of the predictions was dependent on the data set composition and the experimental setup. For the CE-MS ESI+ data set, the best model predicted measured RRFs with a mean absolute error (MAE) of 0.19 log units and a cross-validation coefficient of determination (<i>Q</i><sup>2</sup>) of 0.84 for the testing set. For the LC-QTOF/MS ESI+ data set, the best model predicted measured RRFs with an MAE of 0.32 and a <i>Q</i><sup>2</sup> of 0.40. For the LC-QTOF/MS ESI- data set, the best model predicted measured RRFs with a MAE of 0.50 and a <i>Q</i><sup>2</sup> of 0.20. Our findings suggest that machine-learning algorithms can be used for predicting concentrations of nontargeted chemicals with reasonable uncertainties, especially in ESI+, while the application on ESI- remains a more challenging problem. In silico ; Computer science ; Artificial intelligence ; Machine learning ; Biochemical engineering ; Chemistry ; Engineering Analytical Chemistry and Chromatography ; Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies
Authors: Marcos del Cueto | Xueyao Zhou | Linsen Zhou | Yaolong Zhang | Bin Jiang | Hua Guo
Source: The Journal of Physical Chemistry C (article, 2020-02-10)
Field: Materials Science — Materials Chemistry

New Perspectives on CO<sub>2</sub>–Pt(111) Interaction with a High-Dimensional Neural Network Potential Energy Surface CO oxidation on transition metal surfaces is not only a prototype for studying surface chemistry but also of critical importance in applications such as pollution control and fuel cells. The reverse process, the dissociation of CO2, is also key in the sequestration of this greenhouse gas. However, our understanding of the dynamics involved in these processes is still incomplete. Theoretical studies of surface dynamics have so far been largely hindered by the high computational costs of on-the-fly calculations. To overcome this bottleneck, we report here a high-dimensional potential energy surface (PES) for both the dissociative chemisorption and recombinative desorption of CO2 on Pt(111), by using a machine learning method. Trained with a large number of density functional points in a large configuration space, the multipurpose neural network PES accurately reproduces the geometry and energy of the stationary points along the CO2 reaction path on Pt(111), as well as the dynamical results obtained using the ab initio molecular dynamics method. In addition, we propose a new perspective on the chemical shape of the surface, which reveals the site specificity of the chemical barrier. This approach opens the door to accurate studies of these relevant reactions on surfaces, with a low computational cost, granting access to a more in-depth description of the chemical processes taking place in these systems. Bottleneck ; Chemisorption ; Potential energy surface ; Dissociation (chemistry) ; Chemical physics ; Reaction dynamics ; Artificial neural network ; Desorption ; Computer science ; Ab initio ; Density functional theory ; Molecular dynamics ; Chemical reaction ; Chemistry ; Materials science ; Nanotechnology ; Biological system ; Artificial intelligence ; Molecule ; Computational chemistry ; Physical chemistry ; Adsorption Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalytic Processes in Materials Science
Authors: Volker L. Deringer | Chris J. Pickard | Davide Μ. Proserpio
Source: Angewandte Chemie International Edition (article, 2020-06-04)
Field: Materials Science — Materials Chemistry

Hierarchically Structured Allotropes of Phosphorus from Data‐Driven Exploration The discovery of materials is increasingly guided by quantum-mechanical crystal-structure prediction, but the structural complexity in bulk and nanoscale materials remains a bottleneck. Here we demonstrate how data-driven approaches can vastly accelerate the search for complex structures, combining a machine-learning (ML) model for the potential-energy surface with efficient, fragment-based searching. We use the characteristic building units observed in Hittorf's and fibrous phosphorus to seed stochastic ("random") structure searches over hundreds of thousands of runs. Our study identifies a family of hierarchically structured allotropes based on a P8 cage as principal building unit, including one-dimensional (1D) single and double helix structures, nanowires, and two-dimensional (2D) phosphorene allotropes with square-lattice and kagome topologies. These findings yield new insight into the intriguingly diverse structural chemistry of phosphorus, and they provide an example for how ML methods may, in the long run, be expected to accelerate the discovery of hierarchical nanostructures. Bottleneck ; Phosphorene ; Black phosphorus ; Nanotechnology ; Nanoscopic scale ; Materials science ; Nanostructure ; Computer science ; Bridging (networking) ; Square lattice ; Nanowire ; Chemical physics ; Statistical physics ; Chemistry ; Physics Machine Learning in Materials Science ; 2D Materials and Applications ; Surface Chemistry and Catalysis
Authors: Karthik Nayani | Yu Yang | Huaizhe Yu | Purvil Jani | Manos Mavrikakis | Nicholas L. Abbott
Source: Liquid Crystals Today (article, 2020-04-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Areas of opportunity related to design of chemical and biological sensors based on liquid crystals The societal impact of liquid crystals (LCs) in electrooptical displays arrived after decades of research involving molecular-level design of LCs and their alignment layers, and elucidation of LC electrooptical phenomena at device scales. The anisotropic optical, mechanical and dielectric properties of LCs used in displays also make LCs remarkable amplifiers of their interactions with chemical and biological species, thus opening up the possibility that LCs may play an influential role in a data-driven society that depends on information coming from sensors. In this article, we describe ongoing efforts to design LC systems tailored for chemical and biological sensing, efforts that mirror the challenges and opportunities in LC design and alignment tackled several decades ago during development of LC electrooptical displays. Now, however, traditional design approaches based on structure–property relationships are being supplemented by data-driven methods such as machine learning. Recent studies also show that computational chemistry can greatly increase the rate of discovery of chemically responsive LC systems. Additionally, non-equilibrium states of LCs are being revealed to be useful for design of biological sensors and more complex autonomous systems that integrate self-regulated actuation along with sensing. These topics and others are addressed in this article with the aim of highlighting approaches and goals for future research that will realise the full potential of LC-based sensors. Liquid crystal ; Nanotechnology ; Computer science ; Materials science ; Biochemical engineering ; Optoelectronics ; Engineering Liquid Crystal Research Advancements ; Photonic and Optical Devices ; Semiconductor Lasers and Optical Devices
Authors: Yulou Ouyang | Zhongwei Zhang | Cuiqian Yu | Jia He | Gang Yan | Jie Chen
Source: Chinese Physics Letters (article, 2020-12-01)
Field: Materials Science — Materials Chemistry

Accuracy of Machine Learning Potential for Predictions of Multiple-Target Physical Properties* The accurate and rapid prediction of materials’ physical properties, such as thermal transport and mechanical properties, are of particular importance for potential applications of featuring novel materials. We demonstrate, using graphene as an example, how machine learning potential, combined with the Boltzmann transport equation and molecular dynamics simulations, can simultaneously provide an accurate prediction of multiple-target physical properties, with an accuracy comparable to that of density functional theory calculation and/or experimental measurements. Benchmarked quantities include the Grüneisen parameter, the thermal expansion coefficient, Young’s modulus, Poisson’s ratio, and thermal conductivity. Moreover, the transferability of commonly used empirical potential in predicting multiple-target physical properties is also examined. Our study suggests that atomic simulation, in conjunction with machine learning potential, represents a promising method of exploring the various physical properties of novel materials. Transferability ; Thermal conductivity ; Statistical physics ; Computer science ; Boltzmann equation ; Molecular dynamics ; Materials science ; Machine learning ; Thermodynamics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Thermal properties of materials ; Graphene research and applications
Authors: Jiahao Ren | Xirong Lin | Jinyun Liu | Tianli Han | Zhilong Wang | Haikuo Zhang | Jinjin Li
Source: Materials Today Energy (article, 2020-09-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Engineering early prediction of supercapacitors’ cycle life using neural networks Artificial neural network ; Reliability (semiconductor) ; Computer science ; Set (abstract data type) ; Data set ; Artificial intelligence ; Machine learning ; Training set ; Data mining Supercapacitor Materials and Fabrication ; Fuel Cells and Related Materials ; Advanced Battery Technologies Research
Authors: Christian Grech | Marco Buzio | Mariano Pentella | Nicholas Sammut
Source: Materials (article, 2020-06-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Dynamic Ferromagnetic Hysteresis Modelling Using a Preisach-Recurrent Neural Network Model In this work, a Preisach-recurrent neural network model is proposed to predict the dynamic hysteresis in ARMCO pure iron, an important soft magnetic material in particle accelerator magnets. A recurrent neural network coupled with Preisach play operators is proposed, along with a novel validation method for the identification of the model's parameters. The proposed model is found to predict the magnetic flux density of ARMCO pure iron with a Normalised Root Mean Square Error (NRMSE) better than 0.7%, when trained with just six different hysteresis loops. The model is evaluated using ramp-rates not used in the training procedure, which shows the ability of the model to predict data which has not been measured. The results demonstrate that the Preisach model based on a recurrent neural network can accurately describe ferromagnetic dynamic hysteresis when trained with a limited amount of data, showing the model's potential in the field of materials science. Hysteresis ; Magnetic hysteresis ; Artificial neural network ; Ferromagnetism ; Preisach model of hysteresis ; Magnet ; Work (physics) ; Materials science ; Mean squared error ; Magnetic field ; Control theory (sociology) ; Condensed matter physics ; Computer science ; Magnetization ; Physics ; Artificial intelligence ; Engineering ; Mathematics ; Thermodynamics ; Mechanical engineering ; Statistics Magnetic Properties and Applications ; Microstructure and Mechanical Properties of Steels ; Electric Motor Design and Analysis
Authors: B. Miller | Mario Geiger | Tess Smidt | Frank Noé
Source: arXiv (Cornell University) (preprint, 2020-08-19)
Field: Materials Science — Materials Chemistry

Relevance of Rotationally Equivariant Convolutions for Predicting Molecular Properties Equivariant neural networks (ENNs) are graph neural networks embedded in $\mathbb{R}^3$ and are well suited for predicting molecular properties. The ENN library e3nn has customizable convolutions, which can be designed to depend only on distances between points, or also on angular features, making them rotationally invariant, or equivariant, respectively. This paper studies the practical value of including angular dependencies for molecular property prediction directly via an ablation study with \texttt{e3nn} and the QM9 data set. We find that, for fixed network depth and parameter count, adding angular features decreased test error by an average of 23%. Meanwhile, increasing network depth decreased test error by only 4% on average, implying that rotationally equivariant layers are comparatively parameter efficient. We present an explanation of the accuracy improvement on the dipole moment, the target which benefited most from the introduction of angular features. Equivariant map ; Relevance (law) ; Mathematics ; Physics ; Computer science ; Pure mathematics ; Political science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Gerardo Raggi | Ignacio Fdez. Galván | Christian L. Ritterhoff | Morgane Vacher | Roland Lindh
Source: Journal of Chemical Theory and Computation (article, 2020-05-06)
Field: Materials Science — Materials Chemistry

Restricted-Variance Molecular Geometry Optimization Based on Gradient-Enhanced Kriging Machine learning techniques, specifically gradient-enhanced Kriging (GEK), have been implemented for molecular geometry optimization. GEK-based optimization has many advantages compared to conventional-step-restricted second-order truncated expansion-molecular optimization methods. In particular, the surrogate model given by GEK can have multiple stationary points, will smoothly converge to the exact model as the number of sample points increases, and contains an explicit expression for the expected error of the model function at an arbitrary point. Machine learning is, however, associated with abundance of data, contrary to the situation desired for efficient geometry optimizations. In this paper, we demonstrate how the GEK procedure can be utilized in a fashion such that in the presence of few data points, the surrogate surface will in a robust way guide the optimization to a minimum of a potential energy surface. In this respect, the GEK procedure will be used to mimic the behavior of a conventional second-order scheme but retaining the flexibility of the superior machine learning approach. Moreover, the expected error will be used in the optimizations to facilitate restricted-variance optimizations. A procedure which relates the eigenvalues of the approximate guessed Hessian with the individual characteristic lengths, used in the GEK model, reduces the number of empirical parameters to optimize to two: the value of the trend function and the maximum allowed variance. These parameters are determined using the extended Baker (e-Baker) and part of the Baker transition-state (Baker-TS) test suites as a training set. The so-created optimization procedure is tested using the e-Baker, full Baker-TS, and S22 test suites, at the density functional theory and second-order Møller-Plesset levels of approximation. The results show that the new method is generally of similar or better performance than a state-of-the-art conventional method, even for cases where no significant improvement was expected. Hessian matrix ; Kriging ; Variance (accounting) ; Data point ; Flexibility (engineering) ; Function (biology) ; Algorithm ; Mathematical optimization ; Mathematics ; Surrogate model ; Optimization problem ; Computer science ; Applied mathematics ; Machine learning ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Giovanni Ribaudo | Marco Bortoli | Chiara Pavan | Giuseppe Zagotto | Laura Orian
Source: Antioxidants (review, 2020-08-06)
Field: Chemistry — Organic Chemistry

Antioxidant Potential of Psychotropic Drugs: From Clinical Evidence to In Vitro and In Vivo Assessment and toward a New Challenge for in Silico Molecular Design Due to high oxygen consumption, the brain is particularly vulnerable to oxidative stress, which is considered an important element in the etiopathogenesis of several mental disorders, including schizophrenia, depression and dependencies. Despite the fact that it is not established yet whether oxidative stress is a cause or a consequence of clinic manifestations, the intake of antioxidant supplements in combination with the psychotropic therapy constitutes a valuable solution in patients' treatment. Anyway, some drugs possess antioxidant capacity themselves and this aspect is discussed in this review, focusing on antipsychotics and antidepressants. In the context of a collection of clinical observations, in vitro and in vivo results are critically reported, often highlighting controversial aspects. Finally, a new challenge is discussed, i.e., the possibility of assessing in silico the antioxidant potential of these drugs, exploiting computational chemistry methodologies and machine learning. Despite the physiological environment being incredibly complex and the detection of meaningful oxidative stress biomarkers being all but an easy task, a rigorous and systematic analysis of the structural and reactivity properties of antioxidant drugs seems to be a promising route to better interpret therapeutic outcomes and provide elements for the rational design of novel drugs. In silico ; Context (archaeology) ; Schizophrenia (object-oriented programming) ; Oxidative stress ; Pharmacology ; Medicine ; Clinical trial ; Bioinformatics ; Computational biology ; Psychiatry ; Chemistry ; Biology ; Biochemistry ; Internal medicine ; Gene Free Radicals and Antioxidants ; Tryptophan and brain disorders ; Computational Drug Discovery Methods
Authors: Kailun Fang | Qidan Li | Wei Yu | Changyang Zhou | Wenhui Guo | Jiaqi Shen | Ruoxi Wu | Wenqin Ying | Lu Yu | Jin Zi | Yuxing Zhang | Hui Yang | Siqi Liu | Charlie Degui Chen
Source: Molecular & Cellular Proteomics (article, 2020-11-30)
Field: Chemistry — Spectroscopy

Prediction and Validation of Mouse Meiosis-Essential Genes Based on Spermatogenesis Proteome Dynamics Meiosis ; Biology ; Gene ; Computational biology ; Proteomics ; Genetics ; Proteome ; Gene expression profiling ; Phenotype ; Gene expression Advanced Proteomics Techniques and Applications ; Epigenetics and DNA Methylation ; Bioinformatics and Genomic Networks
Authors: Nabanita Mukherjee | Anindyasundar Adak | Surajit Ghosh
Source: Soft Matter (review, 2020-01-01)
Field: Materials Science — Biomaterials

Recent trends in the development of peptide and protein-based hydrogel therapeutics for the healing of CNS injury Traumatic brain injury (TBI) and spinal cord injury (SCI) cause millions of deaths and permanent or prolonged physical disabilities around the globe every year. It generally happens due to various incidents, such as accidents during sports, war, physical assault, and strokes which result in severe damage to brain and spinal cord. If this remains untreated, traumatic CNS injuries may lead to early development of several neurodegenerative diseases like Alzheimer's, Parkinson, multiple sclerosis, and other mental illnesses. The initial physical reaction, which is also termed as the primary phase, includes swelling, followed by inflammation as a result of internal haemorrhage causing damage to indigenous tissue, i.e., axonal shear injury, rupture of blood vessels, and partial impaired supply of oxygen and essential nutrients in the neurons, thereby initiating a cascade of events causing secondary injuries such as hypoxia, hypotension, cognitive impairment, seizures, imbalanced calcium homeostasis and glutamate-induced excitotoxicity resulting in concomitant neuronal cell death and cumulative permanent tissue damage. In the modern era of advanced biomedical technology, we are still living with scarcity of the clinically applicable comparative non-invasive therapeutic strategies for regeneration or functional recovery of neurons or neural networks after a massive CNS injury. One of the key reasons for this scarcity is the limited regenerative ability of neurons in CNS. Growth-impermissive glial scar and the lack of a synthetic biocompatible platform for proper neural tissue engineering and controlled supply of drugs further retard the healing process. Injectable or implantable hydrogel materials, consisting majorly of water in its porous three-dimensional (3D) structure, can serve as an excellent drug delivery platform as well as a transplanted cell-supporting scaffold medium. Among the various neuro-compatible bioinspired materials, we are limiting our discussion to the recent advancement of engineered biomaterials comprising mainly of peptides and proteins due to their growing demand, low immunogenicity and versatility in the fabrication of neuro regenerative medicine. In this article, we try to explore all the recent scientific avenues that are developing gradually to make peptide and peptide-conjugated biomaterial hydrogels as a therapeutic and supporting scaffold for treating CNS injuries. Peptide ; Scaffold ; Drug delivery ; Tissue engineering ; Medicine ; Pharmacology ; Chemistry ; Nanotechnology ; Biomedical engineering ; Biochemistry ; Materials science Supramolecular Self-Assembly in Materials ; Nerve injury and regeneration ; RNA Interference and Gene Delivery
Authors: Jinlong Fu | Shaoqing Cui | Song Cen | Chenfeng Li
Source: Computer Methods in Applied Mechanics and Engineering (article, 2020-11-06)
Field: Materials Science — Materials Chemistry

Statistical characterization and reconstruction of heterogeneous microstructures using deep neural network Microstructure ; Artificial neural network ; Characterization (materials science) ; Markov random field ; Computer science ; Artificial intelligence ; Algorithm ; Pattern recognition (psychology) ; Materials science ; Biological system ; Image (mathematics) ; Image segmentation ; Composite material ; Nanotechnology Machine Learning in Materials Science ; Industrial Vision Systems and Defect Detection ; Advanced Neural Network Applications
Authors: Arunachalam Thirunavukkarasu | R. Nithya
Source: Journal of the Taiwan Institute of Chemical Engineers (article, 2020-06-01)
Field: Chemistry — Organic Chemistry

Adsorption of acid orange 7 using green synthesized CaO/CeO2 composite: An insight into kinetics, equilibrium, thermodynamics, mass transfer and statistical models Response surface methodology ; Adsorption ; Central composite design ; Gravimetric analysis ; Oxide ; Fourier transform infrared spectroscopy ; Composite number ; Chemistry ; Materials science ; Mean squared error ; Coefficient of determination ; BET theory ; Thermogravimetric analysis ; Nuclear chemistry ; Mathematics ; Chemical engineering ; Physical chemistry ; Chromatography ; Organic chemistry ; Composite material Nanomaterials for catalytic reactions ; Adsorption and biosorption for pollutant removal