jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Thanh Danh Nguyen | Lei Tao | Ying Li
Source: Frontiers in Chemistry (article, 2022-01-24)
Field: Materials Science — Materials Chemistry

Integration of Machine Learning and Coarse-Grained Molecular Simulations for Polymer Materials: Physical Understandings and Molecular Design In recent years, the synthesis of monomer sequence-defined polymers has expanded into broad-spectrum applications in biomedical, chemical, and materials science fields. Pursuing the characterization and inverse design of these polymer systems requires our fundamental understanding not only at the individual monomer level, but also considering the chain scales, such as polymer configuration, self-assembly, and phase separation. However, our accessibility to this field is still rudimentary due to the limitations of traditional design approaches, the complexity of chemical space along with the burdened cost and time issues that prevent us from unveiling the underlying monomer sequence-structure-property relationships. Fortunately, thanks to the recent advancements in molecular dynamics simulations and machine learning (ML) algorithms, the bottlenecks in the tasks of establishing the structure-function correlation of the polymer chains can be overcome. In this review, we will discuss the applications of the integration between ML techniques and coarse-grained molecular dynamics (CGMD) simulations to solve the current issues in polymer science at the chain level. In particular, we focus on the case studies in three important topics-polymeric configuration characterization, feed-forward property prediction, and inverse design-in which CGMD simulations are leveraged to generate training datasets to develop ML-based surrogate models for specific polymer systems and designs. By doing so, this computational hybridization allows us to well establish the monomer sequence-functional behavior relationship of the polymers as well as guide us toward the best polymer chain candidates for the inverse design in undiscovered chemical space with reasonable computational cost and time. Even though there are still limitations and challenges ahead in this field, we finally conclude that this CGMD/ML integration is very promising, not only in the attempt of bridging the monomeric and macroscopic characterizations of polymer materials, but also enabling further tailored designs for sequence-specific polymers with superior properties in many practical applications. Computer science ; Polymer ; Sequence (biology) ; Chemical space ; Molecular dynamics ; Materials science ; Computational chemistry ; Chemistry Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Advanced Polymer Synthesis and Characterization
Authors: Peter Bjørn Jørgensen | Arghya Bhowmik
Source: npj Computational Materials (article, 2022-08-23)
Field: Materials Science — Materials Chemistry

Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids Abstract Electron density $$\rho (\overrightarrow{{{{\bf{r}}}}})$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>ρ</mml:mi> <mml:mrow> <mml:mo>(</mml:mo> <mml:mrow> <mml:mover> <mml:mrow> <mml:mi>r</mml:mi> </mml:mrow> <mml:mo>→</mml:mo> </mml:mover> </mml:mrow> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> is the fundamental variable in the calculation of ground state energy with density functional theory (DFT). Beyond total energy, features and changes in $$\rho (\overrightarrow{{{{\bf{r}}}}})$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>ρ</mml:mi> <mml:mrow> <mml:mo>(</mml:mo> <mml:mrow> <mml:mover> <mml:mrow> <mml:mi>r</mml:mi> </mml:mrow> <mml:mo>→</mml:mo> </mml:mover> </mml:mrow> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> distributions are often used to capture critical physicochemical phenomena in functional materials. We present a machine learning framework for the prediction of $$\rho (\overrightarrow{{{{\bf{r}}}}})$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>ρ</mml:mi> <mml:mrow> <mml:mo>(</mml:mo> <mml:mrow> <mml:mover> <mml:mrow> <mml:mi>r</mml:mi> </mml:mrow> <mml:mo>→</mml:mo> </mml:mover> </mml:mrow> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> . The model is based on equivariant graph neural networks and the electron density is predicted at special query point vertices that are part of the message-passing graph, but only receive messages. The model is tested across multiple datasets of molecules (QM9), liquid ethylene carbonate electrolyte (EC) and LixNiyMnzCo(1-y-z)O2 lithium ion battery cathodes (NMC). For QM9 molecules, the accuracy of the proposed model exceeds typical variability in $$\rho (\overrightarrow{{{{\bf{r}}}}})$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>ρ</mml:mi> <mml:mrow> <mml:mo>(</mml:mo> <mml:mrow> <mml:mover> <mml:mrow> <mml:mi>r</mml:mi> </mml:mrow> <mml:mo>→</mml:mo> </mml:mover> </mml:mrow> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> obtained from DFT done with different exchange-correlation functionals. The accuracy on all three datasets is beyond state of the art and the computation time is orders of magnitude faster than DFT. Artificial neural network ; Equivariant map ; Graph ; Estimation ; Electron ; Molecule ; Computer science ; Mathematics ; Artificial intelligence ; Theoretical computer science ; Physics ; Pure mathematics ; Quantum mechanics ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Johannes Gasteiger | Muhammed Shuaibi | Anuroop Sriram | Stephan Günnemann | Zachary W. Ulissi | C. Lawrence Zitnick | Abhishek Das
Source: arXiv (Cornell University) (preprint, 2022-04-06)
Field: Materials Science — Materials Chemistry

GemNet-OC: Developing Graph Neural Networks for Large and Diverse Molecular Simulation Datasets Recent years have seen the advent of molecular simulation datasets that are orders of magnitude larger and more diverse. These new datasets differ substantially in four aspects of complexity: 1. Chemical diversity (number of different elements), 2. system size (number of atoms per sample), 3. dataset size (number of data samples), and 4. domain shift (similarity of the training and test set). Despite these large differences, benchmarks on small and narrow datasets remain the predominant method of demonstrating progress in graph neural networks (GNNs) for molecular simulation, likely due to cheaper training compute requirements. This raises the question -- does GNN progress on small and narrow datasets translate to these more complex datasets? This work investigates this question by first developing the GemNet-OC model based on the large Open Catalyst 2020 (OC20) dataset. GemNet-OC outperforms the previous state-of-the-art on OC20 by 16% while reducing training time by a factor of 10. We then compare the impact of 18 model components and hyperparameter choices on performance in multiple datasets. We find that the resulting model would be drastically different depending on the dataset used for making model choices. To isolate the source of this discrepancy we study six subsets of the OC20 dataset that individually test each of the above-mentioned four dataset aspects. We find that results on the OC-2M subset correlate well with the full OC20 dataset while being substantially cheaper to train on. Our findings challenge the common practice of developing GNNs solely on small datasets, but highlight ways of achieving fast development cycles and generalizable results via moderately-sized, representative datasets such as OC-2M and efficient models such as GemNet-OC. Our code and pretrained model weights are open-sourced. Hyperparameter ; Computer science ; Machine learning ; Training set ; Graph ; Set (abstract data type) ; Artificial neural network ; Similarity (geometry) ; Data mining ; Deep neural networks ; Artificial intelligence ; Theoretical computer science Machine Learning in Materials Science ; Topic Modeling ; Advanced Graph Neural Networks
Authors: Leif D. Jacobson | James Stevenson | Farhad Ramezanghorbani | Delaram Ghoreishi | Karl Leswing | Edward Harder | Robert Abel
Source: Journal of Chemical Theory and Computation (article, 2022-03-15)
Field: Materials Science — Materials Chemistry

Transferable Neural Network Potential Energy Surfaces for Closed-Shell Organic Molecules: Extension to Ions Transferable high dimensional neural network potentials (HDNNPs) have shown great promise as an avenue to increase the accuracy and domain of applicability of existing atomistic force fields for organic systems relevant to life science. We have previously reported such a potential (Schrödinger-ANI) that has broad coverage of druglike molecules. We extend that work here to cover ionic and zwitterionic druglike molecules expected to be relevant to drug discovery research activities. We report a novel HDNNP architecture, which we call QRNN, that predicts atomic charges and uses these charges as descriptors in an energy model that delivers conformational energies within chemical accuracy when measured against the reference theory it is trained to. Further, we find that delta learning based on a semiempirical level of theory approximately halves the errors. We test the models on torsion energy profiles, relative conformational energies, geometric parameters, and relative tautomer errors. Potential energy ; Molecule ; Computer science ; Artificial neural network ; Ion ; Organic molecules ; Ionic bonding ; Torsion (gastropod) ; Work (physics) ; Chemical physics ; Biological system ; Chemistry ; Physics ; Artificial intelligence ; Atomic physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Qionghai Chen | Zhiyu Zhang | Yongdi Huang | Hengheng Zhao | Zhudan Chen | Ke Gao | Tongkui Yue | Liqun Zhang | Jun Liu
Source: ACS Applied Polymer Materials (article, 2022-04-19)
Field: Materials Science — Polymers and Plastics

Structure–Mechanics Relation of Natural Rubber: Insights from Molecular Dynamics Simulations Attributed to its strain-induced crystallization (SIC), natural rubber (NR) exhibits more excellent mechanical properties compared to other elastomeric materials and has been attracting numerous scientific and technological attention. However, a systematical understanding of the structure–mechanics relation of NR is still lacking. Herein, for the first time, we employ molecular dynamics simulation to examine the effects of the key structural factors on the SIC and mechanical properties at the molecular level. We examine the effects of phospholipid and protein mass fraction (ω), the strength of hydrogen-bond interaction (εH), and the strength of non-hydrogen-bond interaction (εNH) on structural morphology, dynamic behavior, and mechanical properties. NR tends to form local clusters due to the hydrogen-bond interaction formed between phospholipids or proteins and chain ends, which is absent in the case of cis-1,4-polyisoprene (PIP). The polymer chain mobility of NR is retarded due to the formed clusters or even physical network at great εH and high ω. Interestingly, we find that the stress–strain behavior of NR is greatly manipulated by εH and ω, as evidenced by the increase of the chain orientation and the SIC, compared with the cases of PIP. This underlying mechanism results from the alignment of the molecular chains induced by the formed clusters along the deformed direction, and the clusters during the deformation become more stable, particularly at great εH. Lastly, we adopt a machine learning algorithm named extreme gradient boosting via data augmentation, finding that εH has the most significant influencing weight factor on the stress–strain behavior of NR. In general, this work demonstrates a detailed molecular-level structure–mechanics relation of NR and provides some rational guidelines for experimentally designing and synthesizing biomimetic NR. Elastomer ; Molecular dynamics ; Hydrogen bond ; Natural rubber ; Materials science ; Chemical physics ; Polymer ; Crystallization ; Deformation (meteorology) ; Polymer science ; Computational chemistry ; Composite material ; Chemistry ; Molecule ; Thermodynamics ; Physics Polymer crystallization and properties ; Polymer Nanocomposites and Properties ; Rheology and Fluid Dynamics Studies
Authors: Ruili Li | Zi Wang | Zhipeng Zhang | Xiaotong Sun | Yuyang Hu | Haoyang Wang | Kecen Chen | Qi Liu | Miao Chen | Xiaoqing Chen
Source: Analytical Chemistry (article, 2022-11-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep Learning-Based Multicapturer SERS Platform on Plasmonic Nanocube Metasurfaces for Multiplex Detection of Organophosphorus Pesticides in Environmental Water In situ rapid detection of contaminants in environmental water is crucial for protecting the ecological environment and human health; however, it is always hindered by the complexity of sample matrices, trace content, and unknown species. Herein, we demonstrate a deep learning-based multicapturer surface-enhanced Raman scattering (SERS) platform on plasmonic nanocube metasurfaces for multiplex determination of organophosphorus pesticides (OPPs) residues. Poly(vinylpyrrolidone), 4-mercaptobenzoic acid, and l-cysteine are assembled on Ag nanocubes (AgNCs) and act as capturers to chemically define OPPs. Meanwhile, the OPPs-captured AgNCs efficiently close the interparticle distance and generate plasmonic metasurfaces, guaranteeing ultrasensitive and reproducible SERS analysis. Furthermore, by strategically combining all capturer-OPP SERS spectra, comprehensive "combined-SERS spectra" are reconstructed to enhance spectral variations of each OPP. Based on the combined-SERS spectra, a deep learning model is trained to predict OPPs, which significantly improve the qualitative and quantitative analysis accuracy. We successfully identified multiple OPPs in farmland, river, and fishpond water using this strategy. The whole detection procedure requires only 30 min, including sampling, SERS measurements, and deep learning analyses. This combination of a multicapturer SERS platform with the deep learning algorithm creates a rapid and reliable analytical strategy for multiplex detection of target molecules, providing a potential paradigm shift for environment-related research. Chemistry ; Multiplex ; Plasmon ; Raman scattering ; Nanotechnology ; Raman spectroscopy ; Molecule ; Environmental chemistry ; Organic chemistry ; Materials science ; Optoelectronics Gold and Silver Nanoparticles Synthesis and Applications ; Advanced biosensing and bioanalysis techniques ; Biosensors and Analytical Detection
Authors: Shobhit K. Patel | Juveriya Parmar | Vijay Katkar
Source: Optical Materials (article, 2022-02-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Metasurface-based solar absorber with absorption prediction using machine learning Absorption (acoustics) ; Metric (unit) ; Wavelength ; Performance metric ; Resonator ; Range (aeronautics) ; Optics ; Linear regression ; Computer science ; Materials science ; Mathematics ; Biological system ; Optoelectronics ; Physics ; Machine learning ; Engineering ; Composite material Metamaterials and Metasurfaces Applications ; Thermal Radiation and Cooling Technologies ; Energy Harvesting in Wireless Networks
Authors: Smarak Rath | G. Sudha Priyanga | N. Nagappan | Tiju Thomas
Source: Computational Materials Science (article, 2022-05-02)
Field: Materials Science — Materials Chemistry

Discovery of direct band gap perovskites for light harvesting by using machine learning Band gap ; Confusion ; Direct and indirect band gaps ; Python (programming language) ; Machine learning ; Computer science ; Materials science ; Confusion matrix ; Classifier (UML) ; Artificial intelligence ; Optoelectronics Machine Learning in Materials Science ; Perovskite Materials and Applications ; Chalcogenide Semiconductor Thin Films
Authors: Yiwei Liu | Cheng ZHANG | Zhonghua Liu | Donald G. Truhlar | Ying Wang | Xiao He
Source: Nature Computational Science (article, 2022-12-23)
Field: Chemistry — Spectroscopy

Supervised learning of a chemistry functional with damped dispersion Kohn-Sham density functional theory is widely used in chemistry, but no functional can accurately predict the whole range of chemical properties, although recent progress by some doubly hybrid functionals comes close. Here, we optimized a singly hybrid functional called CF22D with higher across-the-board accuracy for chemistry than most of the existing non-doubly hybrid functionals by using a flexible functional form that combines a global hybrid meta-nonseparable gradient approximation that depends on density and occupied orbitals with a damped dispersion term that depends on geometry. We optimized this energy functional by using a large database and performance-triggered iterative supervised training. We combined several databases to create a very large, combined database whose use demonstrated the good performance of CF22D on barrier heights, isomerization energies, thermochemistry, noncovalent interactions, radical and nonradical chemistry, small and large systems, simple and complex systems and transition-metal chemistry. Dispersion (optics) ; Chemistry ; Computer science ; Physics ; Optics Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Molecular spectroscopy and chirality
Authors: Alexander Klein | Petra Rovó | Varun V. Sakhrani | Yangyang Wang | Jacob B. Holmes | Viktoriia Liu | Patricia Skowronek | Laura Kukuk | Suresh K. Vasa | Peter Güntert | Leonard J. Mueller | Rasmus Linser
Source: Proceedings of the National Academy of Sciences (article, 2022-01-20)
Field: Chemistry — Spectroscopy

Atomic-resolution chemical characterization of (2x)72-kDa tryptophan synthase via four- and five-dimensional <sup>1</sup> H-detected solid-state NMR NMR chemical shifts provide detailed information on the chemical properties of molecules, thereby complementing structural data from techniques like X-ray crystallography and electron microscopy. Detailed analysis of protein NMR data, however, often hinges on comprehensive, site-specific assignment of backbone resonances, which becomes a bottleneck for molecular weights beyond 40 to 45 kDa. Here, we show that assignments for the (2x)72-kDa protein tryptophan synthase (665 amino acids per asymmetric unit) can be achieved via higher-dimensional, proton-detected, solid-state NMR using a single, 1-mg, uniformly labeled, microcrystalline sample. This framework grants access to atom-specific characterization of chemical properties and relaxation for the backbone and side chains, including those residues important for the catalytic turnover. Combined with first-principles calculations, the chemical shifts in the β-subunit active site suggest a connection between active-site chemistry, the electrostatic environment, and catalytically important dynamics of the portal to the β-subunit from solution. Protonation ; Chemical shift ; Tryptophan synthase ; Chemistry ; Enzyme ; Chemical modification ; Tryptophan ; Characterization (materials science) ; Solid-state nuclear magnetic resonance ; ATP synthase ; Chemical structure ; Active site ; Catalysis ; Stereochemistry ; Biochemistry ; Amino acid ; Nanotechnology ; Organic chemistry ; Materials science ; Physics ; Physical chemistry ; Nuclear magnetic resonance Advanced NMR Techniques and Applications ; Muon and positron interactions and applications ; Enzyme Structure and Function
Authors: Tshun Li Yap | Adrian Chun Minh Loy | Bridgid Lai Fui Chin | Juin Yau Lim | Hatem Alhamzi | Yee Ho Chai | Chung Loong Yiin | Kin Wai Cheah | Melvin Xin Jie Wee | Man Kee Lam | Zeinab Abbas Jawad | Suzana Yusup | Serene Sow Mun Lock
Source: Journal of environmental chemical engineering (article, 2022-02-15)
Field: Materials Science — Materials Chemistry

Synergistic effects of catalytic co-pyrolysis Chlorella vulgaris and polyethylene mixtures using artificial neuron network: Thermodynamic and empirical kinetic analyses High-density polyethylene ; Catalysis ; Chlorella vulgaris ; Bifunctional ; Polyethylene ; Pyrolysis ; Materials science ; Kinetic energy ; Mean absolute percentage error ; Thermodynamics ; Chemistry ; Chemical engineering ; Organic chemistry ; Artificial neural network ; Computer science Thermal and Kinetic Analysis ; Thermochemical Biomass Conversion Processes ; Petroleum Processing and Analysis
Authors: Ruomeng Qiu | Ivan R. Sasselli | Zaida Álvarez | Hiroaki Sai | Wei Ji | Liam C. Palmer | Samuel I. Stupp
Source: Journal of the American Chemical Society (article, 2022-03-17)
Field: Materials Science — Biomaterials

Supramolecular Copolymers of Peptides and Lipidated Peptides and Their Therapeutic Potential Supramolecular peptide chemistry offers a versatile strategy to create chemical systems useful as new biomaterials with potential to deliver nearly 1000 known candidate peptide therapeutics or integrate other types of bioactivity. We report here on the co-assembly of lipidated β-sheet-forming peptides with soluble short peptides, yielding <i>supramolecular copolymers</i> with various degrees of internal order. At low peptide concentrations, the co-monomer is protected by lodging within internal aqueous compartments and stabilizing internal β-sheets formed by the lipidated peptides. At higher concentrations, the peptide copolymerizes with the lipidated peptide and disrupts the β-sheet secondary structure. The thermodynamic metastability of the co-assembly in turn leads to the spontaneous release of peptide monomers and thus serves as a potential mechanism for drug delivery. We demonstrated the function of these supramolecular systems using a drug candidate for Alzheimer's disease and found that the copolymers enhance neuronal cell viability when the soluble peptide is released from the assemblies. Chemistry ; Peptide ; Supramolecular chemistry ; Monomer ; Drug delivery ; Combinatorial chemistry ; Biophysics ; Copolymer ; Nanotechnology ; Biochemistry ; Molecule ; Polymer ; Organic chemistry Supramolecular Self-Assembly in Materials ; Polydiacetylene-based materials and applications ; RNA Interference and Gene Delivery
Authors: Simona Sawan | Abdelhamid Errachid | Rita Maalouf | Nicole Jaffrézic‐Renault
Source: TrAC Trends in Analytical Chemistry (article, 2022-08-01)
Field: Chemistry — Spectroscopy

Aptamers functionalized metal and metal oxide nanoparticles: Recent advances in heavy metal monitoring Metal ; Political science ; Aptamer ; Oxide ; Library science ; Nanotechnology ; Chemistry ; Materials science ; Computer science ; Molecular biology ; Biology Molecular Sensors and Ion Detection ; Electrochemical Analysis and Applications ; Advanced biosensing and bioanalysis techniques