jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Marcel Ruth | Dennis Gerbig | Peter R. Schreiner
Source: Journal of Chemical Theory and Computation (article, 2023-07-07)
Field: Materials Science — Materials Chemistry

Machine Learning for Bridging the Gap between Density Functional Theory and Coupled Cluster Energies Accurate electronic energies and properties are crucial for successful reaction design and mechanistic investigations. Computing energies and properties of molecular structures has proven extremely useful, and, with increasing computational power, the limits of high-level approaches (such as coupled cluster theory) are expanding to ever larger systems. However, because scaling is highly unfavorable, these methods are still not universally applicable to larger systems. To address the need for fast and accurate electronic energies of larger systems, we created a database of around 8000 small organic monomers (2000 dimers) optimized at the B3LYP-D3(BJ)/cc-pVTZ level of theory. This database also includes single-point energies computed at various levels of theory, including PBE1PBE, ωΒ97Χ, M06-2X, revTPSS, B3LYP, and BP86, for density functional theory as well as DLPNO-CCSD(T) and CCSD(T) for coupled cluster theory, all in conjunction with a cc-pVTZ basis. We used this database to train machine learning models based on graph neural networks using two different graph representations. Our models are able to make energy predictions from B3LYP-D3(BJ)/cc-pVTZ inputs to CCSD(T)/cc-pVTZ outputs with a mean absolute error of 0.78 and to DLPNO-CCSD(T)/cc-pVTZ with an mean absolute error of 0.50 and 0.18 kcal mol<sup>-1</sup> for monomers and dimers, respectively. The model for dimers was further validated on the S22 database, and the monomer model was tested on challenging systems, including those with highly conjugated or functionally complex molecules. Bridging (networking) ; Density functional theory ; Cluster (spacecraft) ; Computer science ; Coupled cluster ; Band gap ; Physics ; Chemical physics ; Quantum mechanics ; Molecule ; Computer network Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Soo Ho Choi | Sang‐Hyeok Yang | Sehwan Park | Byeong Wook Cho | Tuan Dung Nguyen | Jung Ho Kim | Young‐Min Kim | Ki Kang Kim | Young Hee Lee
Source: APL Materials (article, 2023-11-01)
Field: Materials Science — Materials Chemistry

Is chemical vapor deposition of monolayer WSe2 comparable to other synthetic routes? Chemical vapor deposition (CVD) can produce wafer-scale transition-metal dichalcogenide (TMD) monolayers for the integration of electronic and optoelectronic devices. Nonetheless, the material quality of the CVD-grown TMDs still remains controversial. Here, we compare the quality of representative WSe2 monolayers grown by CVD compared to that obtained by other synthesis methods: bulk-grown-chemical vapor transport (CVT) and flux. Through the use of a deep-learning–based algorithm to analyze atomic-resolution scanning transmission electron microscopy images, we confirm that Se vacancies (VSe) are the primary defects in WSe2, with a defect density of ∼5.3 × 1013 cm−2 in the CVD-grown sample, within the same order of magnitude of other methods (∼3.9 × 1013 cm−2 from CVT-grown samples and ∼2.7 × 1013 cm−2 from flux-grown samples). The carrier concentration in field-effect transistors at room temperature is ∼5.84 × 1012 cm−2 from a CVD-grown sample, comparable to other methods (6–7 × 1012 cm−2). The field-effect mobility of the CVD-grown sample is slightly lower than that of other synthesis methods, together with similar trends in on-current. While the difference in photoluminescence intensity is not appreciable at room temperature, different intensities of defect-related localized states appear below 60 K. We conclude that the wafer-scale CVD-grown samples can be utilized without loss of generality in the integration of electronic/optoelectronic devices. Chemical vapor deposition ; Materials science ; Monolayer ; Wafer ; Photoluminescence ; Field-effect transistor ; Optoelectronics ; Analytical Chemistry (journal) ; Nanotechnology ; Transistor ; Chemistry ; Organic chemistry 2D Materials and Applications ; Quantum Dots Synthesis And Properties ; Graphene research and applications
Authors: Asad Khan | Hilal Tayara | Kil To Chong
Source: Computational Materials Science (article, 2023-02-09)
Field: Materials Science — Materials Chemistry

Prediction of organic material band gaps using graph attention network Band gap ; Computer science ; Graph ; Mean squared error ; Machine learning ; Artificial intelligence ; Materials science ; Mathematics ; Theoretical computer science ; Statistics ; Optoelectronics Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Joshua A. Vita | Daniel Schwalbe‐Koda
Source: Machine Learning Science and Technology (article, 2023-08-16)
Field: Materials Science — Materials Chemistry

Data efficiency and extrapolation trends in neural network interatomic potentials Abstract Recently, key architectural advances have been proposed for neural network interatomic potentials (NNIPs), such as incorporating message-passing networks, equivariance, or many-body expansion terms. Although modern NNIP models exhibit small differences in test accuracy, this metric is still considered the main target when developing new NNIP architectures. In this work, we show how architectural and optimization choices influence the generalization of NNIPs, revealing trends in molecular dynamics (MD) stability, data efficiency, and loss landscapes. Using the 3BPA dataset, we uncover trends in NNIP errors and robustness to noise, showing these metrics are insufficient to predict MD stability in the high-accuracy regime. With a large-scale study on NequIP, MACE, and their optimizers, we show that our metric of loss entropy predicts out-of-distribution error and data efficiency despite being computed only on the training set. This work provides a deep learning justification for probing extrapolation and can inform the development of next-generation NNIPs. Extrapolation ; Computer science ; Robustness (evolution) ; Metric (unit) ; Artificial neural network ; Stability (learning theory) ; Generalization ; Artificial intelligence ; Machine learning ; Algorithm ; Data mining ; Mathematics ; Statistics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Mehmet Erdi Korkmaz | Munish Kumar Gupta | Gurminder Singh | Mustafa Kuntoğlu | Abhishek D. Patange | Recep Demirsöz | Nimel Sworna Ross | Brijesh Prasad
Source: The International Journal of Advanced Manufacturing Technology (article, 2023-08-09)
Field: Materials Science — Materials Chemistry

Machine learning models for online detection of wear and friction behaviour of biomedical graded stainless steel 316L under lubricating conditions Abstract Particularly in sectors where mechanisation is increasing, there has been persistent effort to maximise the use of existing assets. Since maintenance management is accountable for the accessibility of assets, it stands to acquire prominence in this setting. One of the most common methods for keeping equipment in good working order is predictive maintenance with machine learning methods. Failures can be spotted before they cause any downtime or extra expenses, and with this aim, the present work deals with the online detection of wear and friction characteristics of stainless steel 316L under lubricating conditions with machine learning models. Wear rate and friction forces were taken into account as reaction parameters, and biomedical-graded stainless steel 316L was chosen as the work material. With more testing, the J48 method’s accuracy improves to 100% in low wear conditions and 99.27% in heavy wear situations. In addition, the graphic showed the accuracy values for several models. The J48 model is the most precise amongst all others, with a value of 100% (minimum wear) and an average of 98.92% (higher wear). Amongst all the models tested under varying machining conditions, the J48’s 98.92% (low wear) and 98.92% (high wear) recall scores stand out as very impressive (higher wear). In terms of F1-score, J48 performs better than any competing model at 99.45% (low wear) and 98.92% (higher wear). As a result, the J48 improves the model’s overall performance. C4.5 algorithm ; Work (physics) ; Downtime ; Machining ; Tool wear ; Materials science ; Metallurgy ; Computer science ; Engineering ; Mechanical engineering ; Reliability engineering ; Artificial intelligence ; Support vector machine Metal Alloys Wear and Properties ; Advanced machining processes and optimization ; Mechanical Failure Analysis and Simulation
Authors: Mikko Koskenniemi | Jesper Byggmästar | K. Nordlund | Flyura Djurabekova
Source: Journal of Nuclear Materials (article, 2023-02-09)
Field: Materials Science — Materials Chemistry

Efficient atomistic simulations of radiation damage in W and W–Mo using machine-learning potentials The Gaussian approximation potential (GAP) is an accurate machine-learning interatomic potential that was recently extended to include the description of radiation effects. In this study, we seek to validate a faster version of GAP, known as tabulated GAP (tabGAP), by modelling primary radiation damage in 50–50 W–Mo alloys and pure W using classical molecular dynamics. We find that W–Mo exhibits a similar number of surviving defects as in pure W. We also observe W–Mo to possess both more efficient recombination of defects produced during the initial phase of the cascades, and in some cases, unlike pure W, recombination of all defects after the cascades cooled down. Furthermore, we observe that the tabGAP is two orders of magnitude faster than GAP, but produces a comparable number of surviving defects and cluster sizes. A small difference is noted in the fraction of interstitials that are bound into clusters. Radiation damage ; Radiation ; Nuclear engineering ; Materials science ; Nuclear physics ; Physics ; Engineering Nuclear Materials and Properties ; Fusion materials and technologies ; Machine Learning in Materials Science
Authors: Wenbo Sun | Guozheng Fan | Tammo van der Heide | Adam McSloy | Thomas Frauenheim | Bálint Aradi
Source: Journal of Chemical Theory and Computation (article, 2023-06-23)
Field: Materials Science — Materials Chemistry

Machine Learning Enhanced DFTB Method for Periodic Systems: Learning from Electronic Density of States Density functional tight binding (DFTB) is an approximate density functional based quantum chemical simulation method with low computational cost. In order to increase its accuracy, we have introduced a machine learning algorithm to optimize several parameters of the DFTB method, concentrating on solids with defects. The backpropagation algorithm was used to reduce the error between DFTB and DFT results with respect to the training data set and to obtain adjusted DFTB Hamiltonian and overlap matrix elements. Afterward, the generalization capability of the trained model was tested for geometries not being part of the training set. In the current work, we have focused on defective periodic silicon and silicon carbide systems as target materials and the density of states (DOS) as target property to demonstrate the feasibility of our approach. The trained model was able to reduce the differences between the DFTB and DFT DOS significantly, while other derived properties (for example, Mulliken population distribution, projected DOS) remained physically sound. Also, the transferability of the obtained model could be verified. Our method allows to carry out relatively fast simulations with high accuracy and only moderate training efforts, and represents a good compromise for cases, where long-range effects make direct machine learning predictions difficult. Computer science ; Mulliken population analysis ; Generalization ; Hamiltonian (control theory) ; Tight binding ; Algorithm ; Machine learning ; Artificial intelligence ; Density functional theory ; Population ; Electronic structure ; Mathematical optimization ; Computational chemistry ; Mathematics ; Chemistry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Electron and X-Ray Spectroscopy Techniques
Authors: Alexey P. Maltsev | Ilya V. Chepkasov | Artem R. Oganov
Source: ACS Applied Materials & Interfaces (article, 2023-09-01)
Field: Materials Science — Materials Chemistry

Order–Disorder Phase Transition and Ionic Conductivity in a Li<sub>2</sub>B<sub>12</sub>H<sub>12</sub> Solid Electrolyte Temperature-induced phase transitions and ionic conductivities of Li<sub>2</sub>B<sub>12</sub>H<sub>12</sub> and LiCB<sub>11</sub>H<sub>12</sub> were simulated with the use of machine learning interatomic potentials based on van der Waals-corrected density functional theory (rev-vdW-DF2 functional). The simulated temperature of order-disorder phase transition, lattice parameters, diffusion, ionic conductivity, and activation energies are in good agreement with experimental data. Our simulations of Li<sub>2</sub>B<sub>12</sub>H<sub>12</sub> uncover the importance of the reorientational motion of the [B<sub>12</sub>H<sub>12</sub>]<sup>2-</sup> anion. In the ordered α-phase (<i>T</i> < 625 K), these anions have well-defined orientations, while in the disordered β-phase (<i>T</i> > 625 K), their orientations are random. In vacancy-rich systems, its complete rotation was observed, while in the ideal crystal, the anions display limited vabrational motion, indicating the static nature of the phase transition without dynamic disordering. The use of machine learning interatomic potentials has allowed us to study large systems (>2000 atoms) in long (nanosecond-scale) molecular dynamics runs with ab initio quality. Materials science ; van der Waals force ; Phase transition ; Ionic bonding ; Ionic conductivity ; Molecular dynamics ; Density functional theory ; Vacancy defect ; Diffusion ; Ab initio ; Fast ion conductor ; Chemical physics ; Phase (matter) ; Ion ; Condensed matter physics ; Thermodynamics ; Physical chemistry ; Electrolyte ; Computational chemistry ; Physics ; Chemistry ; Molecule Hydrogen Storage and Materials ; Boron and Carbon Nanomaterials Research ; Advanced Battery Materials and Technologies
Authors: Li Zhao | Soumil Y. Joshi | Yin Wang | Sanket A. Deshmukh | John B. Matson
Source: Angewandte Chemie International Edition (article, 2023-05-17)
Field: Materials Science — Biomaterials

Supramolecular Peptide Nanostructures Regulate Catalytic Efficiency and Selectivity We report three constitutionally isomeric tetrapeptides, each comprising one glutamic acid (E) residue, one histidine (H) residue, and two lysine (K<sub>S</sub> ) residues functionalized with side-chain hydrophobic S-aroylthiooxime (SATO) groups. Depending on the order of amino acids, these amphiphilic peptides self-assembled in aqueous solution into different nanostructures:nanoribbons, a mixture of nanotoroids and nanoribbons, or nanocoils. Each nanostructure catalyzed hydrolysis of a model substrate, with the nanocoils exhibiting the greatest rate enhancement and the highest enzymatic efficiency. Coarse-grained molecular dynamics simulations, analyzed with unsupervised machine learning, revealed clusters of H residues in hydrophobic pockets along the outer edge of the nanocoils, providing insight for the observed catalytic rate enhancement. Finally, all three supramolecular nanostructures catalyzed hydrolysis of the l-substrate only when a pair of enantiomeric Boc-l/d-Phe-ONp substrates were tested. This study highlights how subtle molecular-level changes can influence supramolecular nanostructures, and ultimately affect catalytic efficiency. Selectivity ; Peptide ; Supramolecular chemistry ; Catalysis ; Nanostructure ; Nanotechnology ; Chemistry ; Combinatorial chemistry ; Materials science ; Biochemistry ; Organic chemistry ; Molecule Supramolecular Self-Assembly in Materials ; Advanced biosensing and bioanalysis techniques ; Chemical Synthesis and Analysis
Authors: Leilei Zhang | Caihong Zhang | Wenxuan Li | Liang Li | Peng Zhang | Cheng Zhu | Yanfei Ding | Sun Hong-wei
Source: Foods (article, 2023-11-14)
Field: Chemistry — Analytical Chemistry

Rapid Indentification of Auramine O Dyeing Adulteration in Dendrobium officinale, Saffron and Curcuma by SERS Raman Spectroscopy Combined with SSA-BP Neural Networks Model (1) Background: Rapid and accurate determination of the content of the chemical dye Auramine O(AO) in traditional Chinese medicines (TCMs) is critical for controlling the quality of TCMs. (2) Methods: Firstly, various models were developed to detect AO content in <i>Dendrobium officinale</i> (<i>D. officinale</i>). Then, the detection of AO content in <i>Saffron</i> and <i>Curcuma</i> using the <i>D. officinale</i> training set as a calibration model. Finally, <i>Saffron</i> and <i>Curcuma</i> samples were added to the training set of <i>D. officinale</i> to predict the AO content in <i>Saffron</i> and <i>Curcuma</i> using secondary wavelength screening. (3) Results: The results show that the sparrow search algorithm (SSA)-backpropagation (BP) neural network (SSA-BP) model can accurately predict AO content in <i>D. officinale</i>, with <i>R<sub>p</sub></i><sup>2</sup> = 0.962, and RMSEP = 0.080 mg/mL. Some <i>Curcuma</i> samples and <i>Saffron</i> samples were added to the training set and after the secondary feature wavelength screening: The Support Vector Machines (SVM) quantitative model predicted <i>R<sub>p</sub></i><sup>2</sup> fluctuated in the range of 0.780 ± 0.035 for the content of AO in <i>Saffron</i> when 579, 781, 1195, 1363, 1440, 1553 and 1657 cm<sup>-1</sup> were selected as characteristic wavelengths; the Partial Least Squares Regression (PLSR) model predicted <i>R<sub>p</sub></i><sup>2</sup> fluctuated in the range of 0.500 ± 0.035 for the content of AO in <i>Curcuma</i> when 579, 811, 1195, 1353, 1440, 1553 and 1635 cm<sup>-1</sup> were selected as the characteristic wavelengths. The robustness and generalization performance of the model were improved. (4) Conclusion: In this study, it has been discovered that the combination of surface-enhanced Raman spectroscopy (SERS) and machine learning algorithms can effectively and promptly detect the content of AO in various types of TCMs. Curcuma ; Partial least squares regression ; Mathematics ; Traditional medicine ; Chemistry ; Artificial intelligence ; Computer science ; Medicine ; Statistics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Mingxi Jiang | Zihao Yang | Ting Lu | Xinjuan Liu | Jiabao Li | Chenglong Wang | Guang Yang | Likun Pan
Source: Ceramics International (article, 2023-10-20)
Field: Materials Science — Materials Chemistry

Machine learning accelerated study for predicting the lattice constant and substitution energy of metal doped titanium dioxide Materials science ; Gradient boosting ; Doping ; Density functional theory ; Boosting (machine learning) ; Lattice constant ; Computation ; Titanium dioxide ; Lattice (music) ; Oxide ; Machine learning ; Computer science ; Computational chemistry ; Algorithm ; Physics ; Metallurgy ; Optoelectronics ; Optics ; Chemistry Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; X-ray Diffraction in Crystallography
Authors: Ryan Snyder | Bryant Kim | Xiaoliang Pan | Yihan Shao | Jingzhi Pu
Source: The Journal of Chemical Physics (article, 2023-08-02)
Field: Materials Science — Materials Chemistry

Bridging semiempirical and <i>ab initio</i> QM/MM potentials by Gaussian process regression and its sparse variants for free energy simulation Free energy simulations that employ combined quantum mechanical and molecular mechanical (QM/MM) potentials at ab initio QM (AI) levels are computationally highly demanding. Here, we present a machine-learning-facilitated approach for obtaining AI/MM-quality free energy profiles at the cost of efficient semiempirical QM/MM (SE/MM) methods. Specifically, we use Gaussian process regression (GPR) to learn the potential energy corrections needed for an SE/MM level to match an AI/MM target along the minimum free energy path (MFEP). Force modification using gradients of the GPR potential allows us to improve configurational sampling and update the MFEP. To adaptively train our model, we further employ the sparse variational GP (SVGP) and streaming sparse GPR (SSGPR) methods, which efficiently incorporate previous sample information without significantly increasing the training data size. We applied the QM-(SS)GPR/MM method to the solution-phase SN2 Menshutkin reaction, NH3+CH3Cl→CH3NH3++Cl-, using AM1/MM and B3LYP/6-31+G(d,p)/MM as the base and target levels, respectively. For 4000 configurations sampled along the MFEP, the iteratively optimized AM1-SSGPR-4/MM model reduces the energy error in AM1/MM from 18.2 to 4.4 kcal/mol. Although not explicitly fitting forces, our method also reduces the key internal force errors from 25.5 to 11.1 kcal/mol/Å and from 30.2 to 10.3 kcal/mol/Å for the N-C and C-Cl bonds, respectively. Compared to the uncorrected simulations, the AM1-SSGPR-4/MM method lowers the predicted free energy barrier from 28.7 to 11.7 kcal/mol and decreases the reaction free energy from -12.4 to -41.9 kcal/mol, bringing these results into closer agreement with their AI/MM and experimental benchmarks. Bridging (networking) ; Gaussian ; Gaussian process ; Kriging ; Ab initio ; Energy (signal processing) ; Statistical physics ; Computational chemistry ; Physics ; Computer science ; Chemistry ; Quantum mechanics ; Machine learning Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Jun Li | Kun Luo | Qi An
Source: Journal of the European Ceramic Society (article, 2023-09-29)
Field: Materials Science — Materials Chemistry

Mobile dislocation mediated Hall-Petch and inverse Hall-Petch behaviors in nanocrystalline Al-doped boron carbide Materials science ; Grain boundary strengthening ; Nanocrystalline material ; Nucleation ; Dislocation ; Grain size ; Condensed matter physics ; Grain boundary ; Boron carbide ; Metallurgy ; Composite material ; Nanotechnology ; Microstructure ; Physics Diamond and Carbon-based Materials Research ; Microstructure and mechanical properties ; Boron and Carbon Nanomaterials Research