jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yoshito Shimajiri | Yasutomo Kawanishi | Shinji Fujita | Yusuke Miyamoto | Atsushi Ito | D. Arzoumanian | P. André | Atsushi Nishimura | Kazuki Tokuda | Hiroyuki Kaneko | Shunya Takekawa | Shota Ueda | Toshikazu Onishi | Tsuyoshi Inoue | Shimpei Nishimoto | Ryuki Yoneda
Source: arXiv (Cornell University) (preprint, 2023-09-13)
Field: Chemistry — Spectroscopy

Predicting reliable H$_2$ column density maps from molecular line data using machine learning The total mass estimate of molecular clouds suffers from the uncertainty in the H$_2$-CO conversion factor, the so-called $X_{\rm CO}$ factor, which is used to convert the $^{12}$CO (1--0) integrated intensity to the H$_2$ column density. We demonstrate the machine learning's ability to predict the H$_2$ column density from the $^{12}$CO, $^{13}$CO, and C$^{18}$O (1--0) data set of four star-forming molecular clouds; Orion A, Orion B, Aquila, and M17. When the training is performed on a subset of each cloud, the overall distribution of the predicted column density is consistent with that of the Herschel column density. The total column density predicted and observed is consistent within 10\%, suggesting that the machine learning prediction provides a reasonable total mass estimate of each cloud. However, the distribution of the column density for values $&gt; \sim 2 \times 10^{22}$ cm$^{-2}$, which corresponds to the dense gas, could not be predicted well. This indicates that molecular line observations tracing the dense gas are required for the training. We also found a significant difference between the predicted and observed column density when we created the model after training the data on different clouds. This highlights the presence of different $X_{\rm CO}$ factors between the clouds, and further training in various clouds is required to correct for these variations. We also demonstrated that this method could predict the column density toward the area not observed by Herschel if the molecular line and column density maps are available for the small portion, and the molecular line data are available for the larger areas. Column (typography) ; Molecular cloud ; Line (geometry) ; Physics ; Astrophysics ; Geometry ; Stars ; Mathematics ; Connection (principal bundle) Spectroscopy and Laser Applications ; Atmospheric Ozone and Climate ; Phase Equilibria and Thermodynamics
Authors: Kai Riedmiller | Patrick Reiser | Elizaveta Bobkova | Kiril Maltsev | Ganna Gryn’ova | Pascal Friederich | Frauke Gräter
Source: ChemRxiv (preprint, 2023-12-06)
Field: Materials Science — Materials Chemistry

Substituting density functional theory in reaction barrier calculations for hydrogen atom transfer in proteins Hydrogen atom transfer (HAT) reactions are important in many biological systems. As these reactions are hard to observe experimentally, it is of high interest to shed light on them using simulations. Here, we present a machine learning model based on graph neural networks for the prediction of energy barriers of HAT reactions in proteins. As input, the model uses exclusively non-optimized structures as obtained from classical simulations. It was trained on more than 17,000 energy barriers calculated using hybrid density functional theory. We built and evaluated the model in the context of HAT in collagen, but we show that the same workflow can easily be applied to HAT reactions in other biological or synthetic polymers. We obtain for relevant reactions (small reaction distances) a model with good predictive power (R2 ∼ 0.9 and mean absolute error of < 3 kcal/mol). As the inference speed is high, this model enables evaluations of dozens of chemical situations within seconds. When combined with molecular dynamics in a kinetic Monte-Carlo scheme, the model paves the way toward reactive simulations. Context (archaeology) ; Hydrogen atom ; Density functional theory ; Monte Carlo method ; Inference ; Statistical physics ; Hydrogen ; Molecular dynamics ; Computer science ; Chemistry ; Atom (system on chip) ; Biological system ; Computational chemistry ; Physics ; Mathematics ; Artificial intelligence ; Quantum mechanics Corrosion Behavior and Inhibition ; Free Radicals and Antioxidants ; Collagen: Extraction and Characterization
Authors: Apurba Nandi | Péter R. Nagy
Source: ChemRxiv (preprint, 2023-12-12)
Field: Materials Science — Materials Chemistry

Combining state-of-the-art quantum chemistry and machine learning make gold standard potential energy surfaces accessible for medium-sized molecules Developing full-dimensional machine-learned potentials with the current gold-standard coupled-cluster (CC) level is a challenging already for medium-sized molecules due to the high computational cost. Consequently, researchers are often bound to use lower-level electronic structure methods such as density functional theory or second-order Moller-Plesset perturbation theory (MP2). Here, we demonstrate on a representative example that gold-standard potentials can now be effectively constructed for molecules of 15 atoms using off-the-shelf hardware. This is achieved by accelerating the CCSD(T) computations via the accurate and cost-effective frozen natural orbital (FNO) approach. The Delta-machine learning (Delta-ML) approach is employed with the use of permutationally invariant polynomials to fit a full-dimensional PES of the acetylacetone molecule, but any other effective descriptor and ML approach can similarly benefit from the accelerated data generation proposed here. Our benchmarks for the global minima, H-transfer TS, and many high-lying configurations show the excellent agreement of FNO-CCSD(T) results with conventional CCSD(T) while achieving a significant time advantage of about a factor of 30-40. The obtained Delta-ML PES shows high fidelity from multiple perspectives including energetic, structural, and vibrational properties. We obtain the symmetric double well H-transfer barrier of 3.15 kcal/mol in excellent agreement with the direct FNO-CCSD(T) barrier of 3.11 kcal/mol as well as with the benchmark CCSD(F12*)(T+)/CBS value of 3.21 kcal/mol. Furthermore, the tunneling splitting due to H-atom transfer is calculated using a 1D double-well potential, providing improved estimates over previous ones obtained using an MP2-based PES. The methodology introduced here represents a significant advancement in the efficient and precise construction of potentials at the CCSD(T) level for molecules above the current limit of 15 atoms. Molecule ; Density functional theory ; Maxima and minima ; Perturbation theory (quantum mechanics) ; Quantum ; Quantum chemistry ; Potential energy surface ; Atom (system on chip) ; Chemistry ; Physics ; Computational chemistry ; Atomic physics ; Quantum mechanics ; Computer science ; Mathematics ; Mathematical analysis Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Molecular Junctions and Nanostructures
Authors: E. E. Méndez | Rocío Semino
Source: arXiv (Cornell University) (preprint, 2023-11-27)
Field: Materials Science — Materials Chemistry

Microscopic Mechanism of the Thermal Amorphization of ZIF-4 and Melting of ZIF-zni Revealed via Molecular Dynamics and Machine Learning Techniques We investigate the microscopic mechanism of the thermally induced ambient pressure ordered-disordered phase transitions of two zeolitic imidazolate frameworks of formula Zn(C$_3$H$_3$N$_2$)$_2$: a porous (ZIF-4) and a dense, non-porous (ZIF-zni) polymorph via a combination of data science and computer simulation approaches. Molecular dynamics simulations are carried out at the atomistic level through the nb-ZIF-FF force field that incorporates ligand-metal reactivity and relies on dummy atoms to reproduce the correct tetrahedral topology around Zn$^{2+}$ centres. The force field is capable of reproducing the structure of ZIF-4, ZIF-zni and the amorphous (ZIF$\_$a) and liquid (ZIF$\_$liq) phases that respectively result when these crystalline materials are heated. Symmetry functions computed over a database of structures of the four phases, are used as inputs to train a neural network that predicts the probabilities of belonging to each of the four phases at the local Zn$^{2+}$ level with 90$\%$ accuracy. We apply this methodology to follow the time-evolution of the amorphization of ZIF-4 and the melting of ZIF-zni along a series of molecular dynamics trajectories. We first computed the transition temperature and determined associated thermodynamic state functions. Subsequently, we studied the mechanisms. Both processes consist of two steps: (i) for ZIF-4, a low-density amorphous phase is first formed, followed by the final ZIF$\_$a phase while (ii) for ZIF-zni, a ZIF$\_$a-like phase precedes the formation of the liquid phase. These processes involve connectivity changes in the first neighbour ligands around the central Zn$^{2+}$ cations. We find that the amorphization of ZIF-4 is a non-isotropic processes and we trace back the origins of this anisotropic behaviour to density and lability of coordination bonds. Zeolitic imidazolate framework ; Molecular dynamics ; Amorphous solid ; Materials science ; Phase (matter) ; Chemical physics ; Force field (fiction) ; Topology (electrical circuits) ; Porosity ; Phase transition ; Metal-organic framework ; Crystallography ; Thermodynamics ; Computational chemistry ; Chemistry ; Physical chemistry ; Adsorption ; Physics Machine Learning in Materials Science ; Crystallography and molecular interactions ; Organic and Molecular Conductors Research
Authors: Corin Wagen
Source: Front Matter (preprint, 2023-07-20)
Field: Materials Science — Materials Chemistry

Machine Learning for Explicit Solvent Molecular Dynamics <ns4:p>An important problem with simulating chemical reactions is that reactions generally take place in solvent, but most simulations are run without solvent molecules. This is a big deal, since much of the inaccuracy associated with simulation actually stems from poor treatment of solvation: when gas phase experimental data is compared to computations, the results are often quite good.</ns4:p> Solvation ; Solvent ; Molecular dynamics ; Gas phase ; Computation ; Molecule ; Computer science ; Computational chemistry ; Chemistry ; Statistical physics ; Chemical physics ; Organic chemistry ; Algorithm ; Physics Machine Learning in Materials Science ; Various Chemistry Research Topics ; Computational Drug Discovery Methods
Authors: Corin Wagen
Source: Front Matter (article, 2023-07-20)
Field: Materials Science — Materials Chemistry

Machine Learning for Explicit Solvent Molecular Dynamics <ns4:p>An important problem with simulating chemical reactions is that reactions generally take place in solvent, but most simulations are run without solvent molecules. This is a big deal, since much of the inaccuracy associated with simulation actually stems from poor treatment of solvation: when gas phase experimental data is compared to computations, the results are often quite good.</ns4:p> Molecular dynamics ; Process (computing) ; Gas phase ; Phase (matter) ; Big data ; Dynamics (music) Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Advanced Physical and Chemical Molecular Interactions
Authors: Albert Thie | Maximillian F.S.J. Menger | Shirin Faraji
Source: ChemRxiv (preprint, 2023-02-02)
Field: Materials Science — Materials Chemistry

HOAX: A Hyperparameter Optimization Algorithm Explorer for Neural Networks Computational chemistry has become an important tool to predict and understand molecular properties and reactions. Even though recent years have seen a significant growth in new algorithms and computational methods that speed up quantum chemical calculations, the bottleneck for trajectory-based methods to study photoinduced processes is still the huge number of electronic structure calculations. In this work, we present an innovative solution, in which the amount of electronic structure calculations is drastically reduced, by employing machine learning algorithms and methods borrowed from the realm of artificial intelligence. However, applying these algorithms effectively requires finding optimal hyperparameters, which remains a challenge itself. Here we present an automated user-friendly framework, HOAX, to perform the hyperparameter optimization for neural networks, which bypasses the need for a lengthy manual process. The neural network generated potential energy surfaces (PESs) reduces the computational costs compared to the ab initio-based PESs. We perform a comparative investigation on the performance of different hyperparameter optimiziation algorithms, namely grid search, simulated annealing,genetic algorithm, and bayesian optimizer in finding the optimal hyperparameters necessary for constructing the well-performing neural network in order to fit the PESs of small organic molecules. Our results show that this automated toolkit not only facilitates a straightforward way to perform the hyperparameter optimization but also the resulting neural networks-based generated PESs are in reasonable agreement with the ab initio-based PESs. Hyperparameter optimization ; Hyperparameter ; Computer science ; Machine learning ; Artificial neural network ; Artificial intelligence ; Bayesian optimization ; Bottleneck ; Algorithm ; MNIST database Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Darian Smalley | Stephanie D. Lough | Luke Holtzman | Kaikui Xu | Madisen Holbrook | Matthew R. Rosenberger | James Hone | Katayun Barmak | Masa Ishigami
Source: arXiv (Cornell University) (preprint, 2023-12-08)
Field: Materials Science — Materials Chemistry

Detecting Atomic Scale Surface Defects in STM of TMDs with Ensemble Deep Learning Atomic-scale defect detection is shown in scanning tunneling microscopy images of single crystal WSe2 using an ensemble of U-Net-like convolutional neural networks. Standard deep learning test metrics indicated good detection performance with an average F1 score of 0.66 and demonstrated ensemble generalization to C-AFM images of WSe2 and STM images of MoSe2. Defect coordinates were automatically extracted from defect detections maps showing that STM image analysis enhanced by machine learning can be used to dramatically increase sample characterization throughput. Convolutional neural network ; Scanning tunneling microscope ; Artificial intelligence ; Generalization ; Characterization (materials science) ; Deep learning ; Ensemble learning ; Pattern recognition (psychology) ; Sample (material) ; Atomic units ; Computer science ; Scale (ratio) ; Atomic force microscopy ; Surface (topology) ; Materials science ; Nanotechnology ; Chemistry ; Physics ; Mathematics ; Geometry Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Thermography and Photoacoustic Techniques
Authors: Tetsuhiro Nemoto | Shingo Harada | Hiroki Takenaka | Tsubasa Ito | Haruki Kanda
Source: Research Square (preprint, 2023-09-22)
Field: Chemistry — Organic Chemistry

Valence-isomer selective cycloaddition reaction to synthesize functionalized carbocycles Cycloaddition ; Valence (chemistry) ; Chemistry ; Combinatorial chemistry ; Stereochemistry ; Catalysis ; Organic chemistry Asymmetric Synthesis and Catalysis ; Oxidative Organic Chemistry Reactions ; Catalytic C–H Functionalization Methods
Authors: Aakash Ashok Naik | Christina Ertural | Nidal Dhamrait | Philipp Benner | Janine George
Source: arXiv (Cornell University) (preprint, 2023-04-05)
Field: Materials Science — Materials Chemistry

A Quantum-Chemical Bonding Database for Solid-State Materials An in-depth insight into the chemistry and nature of the individual chemical bonds is essential for understanding materials. Bonding analysis is thus expected to provide important features for large-scale data analysis and machine learning of material properties. Such chemical bonding information can be computed using the LOBSTER software package, which post-processes modern density functional theory data by projecting the plane wave-based wave functions onto a local, atomic orbital basis. With the help of a fully automatic workflow, the VASP and LOBSTER software packages are used to generate the data. We then perform bonding analyses on 1520 compounds (insulators and semiconductors) and provide the results as a database. The database structure of the bonding analysis database, which allows easy data retrieval, is also explained. The projected densities of states and bonding indicators are benchmarked on standard density-functional theory computations and available heuristics, respectively. Lastly, we illustrate the predictive power of bonding descriptors by constructing a machine-learning model for phononic properties, which shows an increase in prediction accuracies by 27 % (mean absolute errors) compared to a benchmark model differing only by not relying on any quantum-chemical bonding features. Computer science ; Workflow ; Chemical bond ; Density functional theory ; Heuristics ; Benchmark (surveying) ; Software ; Materials science ; Database ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Johannes Voss
Source: arXiv (Cornell University) (preprint, 2023-11-01)
Field: Materials Science — Materials Chemistry

Machine learning for accuracy in density functional approximations Machine learning techniques have found their way into computational chemistry as indispensable tools to accelerate atomistic simulations and materials design. In addition, machine learning approaches hold the potential to boost the predictive power of computationally efficient electronic structure methods, such as density functional theory, to chemical accuracy and to correct for fundamental errors in density functional approaches. Here, recent progress in applying machine learning to improve the accuracy of density functional and related approximations is reviewed. Promises and challenges in devising machine learning models transferable between different chemistries and materials classes are discussed with the help of examples applying promising models to systems far outside their training sets. Computer science ; Machine learning ; Artificial intelligence ; Computational learning theory ; Density functional theory ; Active learning (machine learning) ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Ziduo Yang | Xian Wang | Yifan Li | Qiujie Lv | Calvin Yu‐Chian Chen | Lei Shen
Source: arXiv (Cornell University) (preprint, 2023-11-06)
Field: Materials Science — Materials Chemistry

Lightweight equivariant model for efficient machine learning interatomic potentials In modern computational materials science, deep learning has shown the capability to predict interatomic potentials, thereby supporting and accelerating conventional simulations. However, existing models typically sacrifice either accuracy or efficiency. Moreover, lightweight models are highly demanded for offering simulating systems on a considerably larger scale at reduced computational costs. Here, we introduce a lightweight equivariant interaction graph neural network (LEIGNN) that can enable accurate and efficient interatomic potential and force predictions for molecules and crystals. Rather than relying on higher-order representations, LEIGNN employs a scalar-vector dual representation to encode equivariant features. By learning geometric symmetry information, our model remains lightweight while ensuring prediction accuracy and robustness through the equivariance. Our results show that LEIGNN consistently outperforms the prediction performance of the representative baselines and achieves significant efficiency across diverse datasets, which include catalysts, molecules, and organic isomers. Furthermore, we conduct molecular dynamics (MD) simulations using the LEIGNN force field across solid, liquid, and gas systems. It is found that LEIGNN can achieve the accuracy of \textit{ab initio} MD across all examined systems. Equivariant map ; Computer science ; Artificial neural network ; Ab initio ; Statistical physics ; Artificial intelligence ; Algorithm ; Physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Topic Modeling ; Advanced Graph Neural Networks
Authors: Chad Risko
Source:  (article, 2023-09-29)
Field: Materials Science — Materials Chemistry

Developing a data infrastructure and tools for organic semiconductor discovery Organic semiconductors, derived from π-conjugated molecules and polymers, offer significant potential for (opto)electronics applications with finely tuned electronic, optical, and mechanical properties. While chemists have the capacity to manipulate molecular-scale electronic, redox, and optical properties that in turn determine organic semiconductor response, the chemical design space for organic semiconductors is vast, and it is not clear that current Edisonian practices of make, test, and repeat offer the ability to identify systems as efficiently as required for many needed technology advances. Here we will discuss the development of the OCELOT (Organic Crystals for Electronic and Light Oriented Technologies) data infrastructure for the collection and dissemination of experimental and computational data for π-conjugated molecules and organic semiconductors, developed under the auspices of the FAIR data principles with the aim that all data and models are AI (artificial intelligence)-ready. Among the data tools offered on OCELOT, we will discuss the development of machine-learning models to predict molecular and semiconductor electronic, redox, and optical properties. Organic semiconductor ; Semiconductor ; Computer science ; Electronics ; Nanotechnology ; Molecular electronics ; Data science ; Materials science ; Electrical engineering ; Optoelectronics ; Molecule ; Chemistry ; Engineering Machine Learning in Materials Science ; Conducting polymers and applications ; Analytical Chemistry and Sensors
Authors: Xinyu Jiang | Haofan Sun | Kamal Choudhary | Houlong Zhuang | Qiong Nian
Source: arXiv (Cornell University) (preprint, 2023-07-24)
Field: Materials Science — Materials Chemistry

Interpretable Ensemble Learning for Materials Property Prediction with Classical Interatomic Potentials: Carbon as an Example Machine learning (ML) is widely used to explore crystal materials and predict their properties. However, the training is time-consuming for deep-learning models, and the regression process is a black box that is hard to interpret. Also, the preprocess to transfer a crystal structure into the input of ML, called descriptor, needs to be designed carefully. To efficiently predict important properties of materials, we propose an approach based on ensemble learning consisting of regression trees to predict formation energy and elastic constants based on small-size datasets of carbon allotropes as an example. Without using any descriptor, the inputs are the properties calculated by molecular dynamics with 9 different classical interatomic potentials. Overall, the results from ensemble learning are more accurate than those from classical interatomic potentials, and ensemble learning can capture the relatively accurate properties from the 9 classical potentials as criteria for predicting the final properties. Ensemble learning ; Crystal structure prediction ; Computer science ; Property (philosophy) ; Artificial intelligence ; Crystal (programming language) ; Statistical physics ; Machine learning ; Molecular dynamics ; Regression ; Ensemble forecasting ; Canonical ensemble ; Process (computing) ; Crystal structure ; Mathematics ; Physics ; Chemistry ; Computational chemistry ; Statistics ; Crystallography ; Monte Carlo method Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography