jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: So-Young Kim | Yong Jae Jang | Gyeongtaek Gong | Sun‐Mi Lee | Youngsoon Um | Kyoung Heon Kim | Ja Kyong Ko
Source: Microbial Cell Factories (article, 2022-11-05)
Field: Materials Science — Biomaterials

Engineering Cupriavidus necator H16 for enhanced lithoautotrophic poly(3-hydroxybutyrate) production from CO2 The global transcriptional regulator RegA plays an important role in the regulation of carbon fixation and shows the possibility to improve autotrophic cell growth and PHB accumulation by increasing its expression level. This work represents another step forward in better understanding and improving the lithoautotrophic PHB production by C. necator H16. Cupriavidus necator ; Poly-3-hydroxybutyrate ; Carbon fixation ; Metabolic engineering ; Synthetic biology ; Bioproduction ; Autotroph ; Polyhydroxybutyrate ; Bioplastic ; Biochemistry ; Bacteria ; Chemistry ; Biology ; Polyhydroxyalkanoates ; Gene ; Computational biology biodegradable polymer synthesis and properties ; Carbon dioxide utilization in catalysis ; Microbial Metabolic Engineering and Bioproduction
Authors: Jing Wang | Mohamed Arselene Ayari | Amith Khandakar | Muhammad E. H. Chowdhury | SM Ashfaq Uz Zaman | Tawsifur Rahman | Behzad Vaferi
Source: Polymers (article, 2022-01-28)
Field: Materials Science — Materials Chemistry

Estimating the Relative Crystallinity of Biodegradable Polylactic Acid and Polyglycolide Polymer Composites by Machine Learning Methodologies Biodegradable polymers have recently found significant applications in pharmaceutics processing and drug release/delivery. Composites based on poly (L-lactic acid) (PLLA) have been suggested to enhance the crystallization rate and relative crystallinity of pure PLLA polymers. Despite the large amount of experimental research that has taken place to date, the theoretical aspects of relative crystallinity have not been comprehensively investigated. Therefore, this research uses machine learning methods to estimate the relative crystallinity of biodegradable PLLA/PGA (polyglycolide) composites. Six different artificial intelligent classes were employed to estimate the relative crystallinity of PLLA/PGA polymer composites as a function of crystallization time, temperature, and PGA content. Cumulatively, 1510 machine learning topologies, including 200 multilayer perceptron neural networks, 200 cascade feedforward neural networks (CFFNN), 160 recurrent neural networks, 800 adaptive neuro-fuzzy inference systems, and 150 least-squares support vector regressions, were developed, and their prediction accuracy compared. The modeling results show that a single hidden layer CFFNN with 9 neurons is the most accurate method for estimating 431 experimentally measured datasets. This model predicts an experimental database with an average absolute percentage difference of 8.84%, root mean squared errors of 4.67%, and correlation coefficient (R<sup>2</sup>) of 0.999008. The modeling results and relevancy studies show that relative crystallinity increases based on the PGA content and crystallization time. Furthermore, the effect of temperature on relative crystallinity is too complex to be easily explained. Crystallinity ; Materials science ; Crystallization of polymers ; Crystallization ; Polylactic acid ; Artificial neural network ; Composite material ; Machine learning ; Polymer ; Biological system ; Chemical engineering ; Computer science ; Engineering Machine Learning in Materials Science ; Additive Manufacturing and 3D Printing Technologies ; Bone Tissue Engineering Materials
Authors: Pinghui Mo | Chang Li | Dan Zhao | Yujia Zhang | Mengchao Shi | Junhua Li | Jie Liu
Source: npj Computational Materials (article, 2022-05-09)
Field: Materials Science — Materials Chemistry

Accurate and efficient molecular dynamics based on machine learning and non von Neumann architecture Abstract Force field-based classical molecular dynamics (CMD) is efficient but its potential energy surface (PES) prediction error can be very large. Density functional theory (DFT)-based ab-initio molecular dynamics (AIMD) is accurate but computational cost limits its applications to small systems. Here, we propose a molecular dynamics (MD) methodology which can simultaneously achieve both AIMD-level high accuracy and CMD-level high efficiency. The high accuracy is achieved by exploiting deep neural network (DNN)’s arbitrarily-high precision to fit PES. The high efficiency is achieved by deploying multiplication-less DNN on a carefully-optimized special-purpose non von Neumann (NvN) computer to mitigate the performance-limiting data shuttling (i.e., ‘memory wall bottleneck’). By testing on different molecules and bulk systems, we show that the proposed MD methodology is generally-applicable to various MD tasks. The proposed MD methodology has been deployed on an in-house computing server based on reconfigurable field programmable gate array (FPGA), which is freely available at http://nvnmd.picp.vip . Bottleneck ; Von Neumann architecture ; Computer science ; Molecular dynamics ; Field-programmable gate array ; Field (mathematics) ; Computational science ; Artificial neural network ; Multiplication (music) ; Limiting ; Efficient energy use ; Computer engineering ; Artificial intelligence ; Parallel computing ; Embedded system ; Mathematics ; Computational chemistry ; Chemistry ; Engineering Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Ferroelectric and Negative Capacitance Devices
Authors: K. Brajesh | Vishnu Sresht | Qingyi Yang | Ray Unwalla | Meihua Tu | Alan M. Mathiowetz | Gregory A. Bakken
Source: Journal of Chemical Information and Modeling (article, 2022-02-04)
Field: Materials Science — Materials Chemistry

TorsionNet: A Deep Neural Network to Rapidly Predict Small-Molecule Torsional Energy Profiles with the Accuracy of Quantum Mechanics Fast and accurate assessment of small-molecule dihedral energetics is crucial for molecular design and optimization in medicinal chemistry. Yet, accurate prediction of torsion energy profiles remains challenging as the current molecular mechanics (MM) methods are limited by insufficient coverage of drug-like chemical space and accurate quantum mechanical (QM) methods are too expensive. To address this limitation, we introduce TorsionNet, a deep neural network (DNN) model specifically developed to predict small-molecule torsion energy profiles with QM-level accuracy. We applied active learning to identify nearly 50k fragments (with elements H, C, N, O, F, S, and Cl) that maximized the coverage of our corporate compound library and leveraged massively parallel cloud computing resources for density functional theory (DFT) torsion scans of these fragments, generating a training data set of 1.2 million DFT energies. After training TorsionNet on this data set, we obtain a model that can rapidly predict the torsion energy profile of typical drug-like fragments with DFT-level accuracy. Importantly, our method also provides an uncertainty estimate for the predicted profiles without any additional calculations. In this report, we show that TorsionNet can accurately identify the preferred dihedral geometries observed in crystal structures. Our TorsionNet-based analysis of a diverse set of protein-ligand complexes with measured binding affinity shows a strong association between high ligand strain and low potency. We also present practical applications of TorsionNet that demonstrate how consideration of DNN-based strain energy leads to substantial improvement in existing lead discovery and design workflows. TorsionNet500, a benchmark data set comprising 500 chemically diverse fragments with DFT torsion profiles (12k MM- and DFT-optimized geometries and energies), has been created and is made publicly available. Dihedral angle ; Computer science ; Torsion (gastropod) ; Density functional theory ; Artificial neural network ; Drug discovery ; Chemical space ; Massively parallel ; Benchmark (surveying) ; Quantum ; Computational science ; Data mining ; Molecule ; Artificial intelligence ; Biological system ; Computational chemistry ; Statistical physics ; Chemistry ; Bioinformatics ; Physics ; Quantum mechanics ; Hydrogen bond ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Eric L. Buehler | Markus J. Buehler
Source: Biomedical Engineering Advances (article, 2022-06-01)
Field: Materials Science — Materials Chemistry

End-to-end prediction of multimaterial stress fields and fracture patterns using cycle-consistent adversarial and transformer neural networks The prediction of stress, strain fields or fracture patterns, along with other physical quantities as a function of boundary conditions and material microstructure, is an important objective of various physics-based simulation methods like finite element models or molecular dynamics modeling. However, such simulations may be expensive and can be costly in their execution, limiting their application to solve inverse design problems, or in the discovery of novel materials that feature a desirable performance. In other fields of studies, such as in mechanobiology, field data can often not be calculated due to a lack of models to begin with; where experimental observations may be the only source of insight. Here we introduce a data-driven method to relate input microstructures and field data using an alternative approach, enabled by deep learning, to predict complex mechanical fields directly based on input microstructure data. We report two approaches to solve said problem, first using cycle-consistent adversarial neural networks trained on unpaired images of microstructures and physical fields, and second, using a Perceiver neural networks based on an attention mechanism based on paired images, implemented in a transformer neural network. Due to the cycle consistent nature of the GAN model we can immediately make forward (from microstructure to stress field) and backward (from stress field to microstructure) predictions, offering an avenue for inverse material design. The Perceiver model, implementing an efficient transformer model that can be scaled to very large input and output data, shows strong generalizability and can be successfully applied to scenarios distinct from those in the training set. We demonstrate the model in several sample microstructures including a set of small cracks (voids) to predict both stress fields and fracture patterns, as well as in a composite microstructure of a soft matrix and rigid inclusions, and in a series of solutions to the inverse problem facilitated by the cycle consistent GAN model. The applications of the models include biomimetic biotechnology (e.g. bone-tendon-inspired attachments and gradients), armor materials, mechanobiology, soft robotics, and lightweight resilient and tough structural systems. Artificial neural network ; Computer science ; Generalizability theory ; Artificial intelligence ; Transformer ; Inverse problem ; Finite element method ; Engineering ; Mathematics ; Structural engineering Machine Learning in Materials Science ; Integrated Circuits and Semiconductor Failure Analysis ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Peikun Zheng | Wudi Yang | Wei Wu | Olexandr Isayev | Pavlo O. Dral
Source: The Journal of Physical Chemistry Letters (article, 2022-04-13)
Field: Materials Science — Materials Chemistry

Toward Chemical Accuracy in Predicting Enthalpies of Formation with General-Purpose Data-Driven Methods Enthalpies of formation and reaction are important thermodynamic properties that have a crucial impact on the outcome of chemical transformations. Here we implement the calculation of enthalpies of formation with a general-purpose ANI-1ccx neural network atomistic potential. We demonstrate on a wide range of benchmark sets that both ANI-1ccx and our other general-purpose data-driven method AIQM1 approach the coveted chemical accuracy of 1 kcal/mol with the speed of semiempirical quantum mechanical methods (AIQM1) or faster (ANI-1ccx). It is remarkably achieved without specifically training the machine learning parts of ANI-1ccx or AIQM1 on formation enthalpies. Importantly, we show that these data-driven methods provide statistical means for uncertainty quantification of their predictions, which we use to detect and eliminate outliers and revise reference experimental data. Uncertainty quantification may also help in the systematic improvement of such data-driven methods. Benchmark (surveying) ; Outlier ; Experimental data ; Computer science ; Quantum chemical ; Range (aeronautics) ; Standard enthalpy of formation ; Artificial neural network ; Standard enthalpy change of formation ; Machine learning ; Artificial intelligence ; Chemistry ; Materials science ; Mathematics ; Physical chemistry ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Thermodynamics and Molecular Structure
Authors: Maxime Ducamp | François‐Xavier Coudert
Source: The Journal of Physical Chemistry C (article, 2022-01-11)
Field: Materials Science — Materials Chemistry

Prediction of Thermal Properties of Zeolites through Machine Learning The use of machine learning for the prediction of physical and chemical properties of crystals based on their structure alone is currently an area of intense research in computational materials science. In this work, we studied the possibility of using machine-learning-trained algorithms in order to calculate the thermal properties of siliceous zeolite frameworks. We used as training data the thermal properties of 120 zeolites, calculated at the DFT level, in the quasi-harmonic approximation. We compared the statistical accuracy of trained models (based on the gradient-boosting regression technique) using different types of descriptors, including ad hoc geometrical features, topology, pore space, and general geometric descriptors. While geometric descriptors were found to perform best, we also identified limitations on the accuracy of the predictions, especially for a small group of materials with very highly negative thermal expansion coefficients. We then studied the generalizability of the technique, demonstrating that the predictions were not sensitive to the refinement of framework structures at a high level of theory. Therefore, the models are suitable for the exploration and screening of large-scale databases of hypothetical frameworks, which we illustrate on the PCOD2 database of zeolites containing around 600 000 hypothetical structures. Generalizability theory ; Machine learning ; Computer science ; Artificial intelligence ; Harmonic mean ; Gradient boosting ; Boosting (machine learning) ; Algorithm ; Mathematics ; Geometry ; Statistics Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis ; X-ray Diffraction in Crystallography
Authors: Jiawei Lai | A. Bolyachkin | Noriki Terada | Sae Dieb | Xin Tang | Tadakatsu Ohkubo | H. Sepehri‐Amin | K. Hono
Source: Acta Materialia (article, 2022-04-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning assisted development of Fe2P-type magnetocaloric compounds for cryogenic applications Magnetic refrigeration ; Materials science ; Rare earth ; Isothermal process ; Refrigeration ; Thermodynamics ; Magnetic field ; Metallurgy ; Magnetization Magnetic and transport properties of perovskites and related materials ; Advanced Condensed Matter Physics ; Multiferroics and related materials
Authors: Chenxi Liu | Jingxin Liao | Yong Zheng | Ying Chen | Hongsheng Liu | Xizhi Shi
Source: Journal of Hazardous Materials (article, 2022-07-14)
Field: Chemistry — Analytical Chemistry

Random forest algorithm-enhanced dual-emission molecularly imprinted fluorescence sensing method for rapid detection of pretilachlor in fish and water samples Fish <Actinopterygii> ; Fluorescence ; Dual (grammatical number) ; Random forest ; Chemistry ; Algorithm ; Environmental science ; Computer science ; Biology ; Fishery ; Physics ; Artificial intelligence ; Optics Analytical chemistry methods development ; Biosensors and Analytical Detection ; Advanced biosensing and bioanalysis techniques
Authors: Bohayra Mortazavi | Fazel Shojaei | Alexander V. Shapeev | Xiaoying Zhuang
Source: Carbon (article, 2022-03-31)
Field: Materials Science — Materials Chemistry

A combined first-principles and machine-learning investigation on the stability, electronic, optical, and mechanical properties of novel C6N7-based nanoporous carbon nitrides Materials science ; Density functional theory ; Nanoporous ; Carbon nitride ; Nanomaterials ; Graphitic carbon nitride ; Electronic structure ; Nanotechnology ; Semiconductor ; Graphyne ; Nitride ; Stability (learning theory) ; Nanomechanics ; Computational chemistry ; Computer science ; Graphene ; Optoelectronics ; Chemistry ; Machine learning Graphene research and applications ; 2D Materials and Applications ; MXene and MAX Phase Materials
Authors: Irène Barbarin-Bocahu | Marc Graille
Source: Acta Crystallographica Section D Structural Biology (article, 2022-03-16)
Field: Materials Science — Materials Chemistry

The X-ray crystallography phase problem solved thanks to <i>AlphaFold</i> and <i>RoseTTAFold</i> models: a case-study report The breakthrough recently made in protein structure prediction by deep-learning programs such as AlphaFold and RoseTTAFold will certainly revolutionize biology over the coming decades. The scientific community is only starting to appreciate the various applications, benefits and limitations of these protein models. Yet, after the first thrills due to this revolution, it is important to evaluate the impact of the proposed models and their overall quality to avoid the misinterpretation or overinterpretation of these models by biologists. One of the first applications of these models is in solving the `phase problem' encountered in X-ray crystallography in calculating electron-density maps from diffraction data. Indeed, the most frequently used technique to derive electron-density maps is molecular replacement. As this technique relies on knowledge of the structure of a protein that shares strong structural similarity with the studied protein, the availability of high-accuracy models is then definitely critical for successful structure solution. After the collection of a 2.45 Å resolution data set, we struggled for two years in trying to solve the crystal structure of a protein involved in the nonsense-mediated mRNA decay pathway, an mRNA quality-control pathway dedicated to the elimination of eukaryotic mRNAs harboring premature stop codons. We used different methods (isomorphous replacement, anomalous diffraction and molecular replacement) to determine this structure, but all failed until we straightforwardly succeeded thanks to both AlphaFold and RoseTTAFold models. Here, we describe how these new models helped us to solve this structure and conclude that in our case the AlphaFold model largely outcompetes the other models. We also discuss the importance of search-model generation for successful molecular replacement. Molecular replacement ; Set (abstract data type) ; Structural genomics ; Computer science ; Diffraction ; Quality (philosophy) ; Structural biology ; Similarity (geometry) ; Phase (matter) ; Computational biology ; Protein structure ; Crystallography ; Physics ; Algorithm ; Statistical physics ; Artificial intelligence ; Crystal structure ; Biology ; Chemistry ; Genetics ; Image (mathematics) ; Optics ; Quantum mechanics Enzyme Structure and Function ; RNA modifications and cancer ; RNA and protein synthesis mechanisms
Authors: Or Perlman | Christian T. Farrar | Hye‐Young Heo
Source: NMR in Biomedicine (review, 2022-02-09)
Field: Materials Science — Materials Chemistry

MR fingerprinting for semisolid magnetization transfer and chemical exchange saturation transfer quantification Chemical exchange saturation transfer (CEST) MRI has positioned itself as a promising contrast mechanism, capable of providing molecular information at sufficient resolution and amplified sensitivity. However, it has not yet become a routinely employed clinical technique, due to a variety of confounding factors affecting its contrast-weighted image interpretation and the inherently long scan time. CEST MR fingerprinting (MRF) is a novel approach for addressing these challenges, allowing simultaneous quantitation of several proton exchange parameters using rapid acquisition schemes. Recently, a number of deep-learning algorithms have been developed to further boost the performance and speed of CEST and semi-solid macromolecule magnetization transfer (MT) MRF. This review article describes the fundamental theory behind semisolid MT/CEST-MRF and its main applications. It then details supervised and unsupervised learning approaches for MRF image reconstruction and describes artificial intelligence (AI)-based pipelines for protocol optimization. Finally, practical considerations are discussed, and future perspectives are given, accompanied by basic demonstration code and data. Magnetization transfer ; Computer science ; Transfer of learning ; Artificial intelligence ; Pattern recognition (psychology) ; Biological system ; Contrast (vision) ; Machine learning ; Magnetic resonance imaging ; Biology ; Radiology Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; MRI in cancer diagnosis
Authors: Bohayra Mortazavi | Alexander V. Shapeev
Source: FlatChem (article, 2022-02-17)
Field: Materials Science — Materials Chemistry

Anisotropic mechanical response, high negative thermal expansion, and outstanding dynamical stability of biphenylene monolayer revealed by machine-learning interatomic potentials Biphenylene ; Materials science ; Monolayer ; Anisotropy ; Graphene ; Thermal stability ; Thermal expansion ; Condensed matter physics ; Composite material ; Nanotechnology ; Chemical physics ; Physics ; Chemistry ; Optics ; Organic chemistry Thermal properties of materials ; Graphene research and applications ; Boron and Carbon Nanomaterials Research