jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Anderson D. S. Duraes
Source: arXiv (Cornell University) (preprint, 2023-04-01)
Field: Materials Science — Materials Chemistry

Compression of Exact Wavefunctions with Restricted Boltzmann Machine Auto-Encoders Virtually, every ab-initio electronic structure method (Coupled Cluster, DMRG, etc.) can be viewed as an algorithm to compress the ground-state wavefunction. This compression is usually obtained by exploiting some physical structure of the wavefunction, which leads to issues when the system changes and that structure is lost. Compressions which are efficient near equilibrium (coupled cluster) or in 1-D systems (DMRG) often fail catastrophically elsewhere. To overcome these issues, we seek a scheme that compresses wavefunctions without any supervised physical information. In this manuscript, we introduce a scheme to compress molecular wavefunctions using a model for high dimensional functions from machine learning: a restricted Boltzmann machine (RBM). We show that, while maintaining chemical accuracy, the RBM can significantly compress the exact wavefunction. Wave function ; Restricted Boltzmann machine ; Coupled cluster ; Boltzmann machine ; Physics ; Function (biology) ; Algorithm ; Statistical physics ; Quantum mechanics ; Computer science ; Artificial intelligence ; Deep learning ; Molecule Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Neural Networks and Reservoir Computing
Authors: Jiaxing Qu | Yuxuan Richard Xie | Ciesielski, Kamil M. | Porter, Claire E. | Toberer, Eric S. | Ertekin, Elif
Source: arXiv (Cornell University) (preprint, 2023-05-01)
Field: Materials Science — Materials Chemistry

Leveraging Language Representation for Material Recommendation, Ranking, and Exploration Data-driven approaches for material discovery and design have been accelerated by emerging efforts in machine learning. However, general representations of crystals to explore the vast material search space remain limited. We introduce a material discovery framework that uses natural language embeddings derived from language models as representations of compositional and structural features. The discovery framework consists of a joint scheme that first recalls relevant candidates, and next ranks the candidates based on multiple target properties. The contextual knowledge encoded in language representations conveys information about material properties and structures, enabling both representational similarity analysis for recall, and multi-task learning to share information across related properties. By applying the framework to thermoelectrics, we demonstrate diversified recommendations of prototype structures and identify under-studied high-performance material spaces. The recommended materials are corroborated by first-principles calculations and experiments, revealing novel materials with potential high performance. Our framework provides a task-agnostic means for effective material recommendation and can be applied to various material systems. Computer science ; Ranking (information retrieval) ; Task (project management) ; Representation (politics) ; Space (punctuation) ; Similarity (geometry) ; Artificial intelligence ; Data science ; Information retrieval ; Natural language processing ; Engineering Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Taija L. Fischer | Margarethe Bödecker | Sophie M. Schweer | Jennifer Dupont | Valéria Lepère | Anne Zehnacker | Martin A. Suhm | Benjamin Schröder | Tobias Henkes | Diego M. Andrada | Roman M. Balabin | Haobam Kisan Singh | Himangshu Pratim Bhattacharyya | Manabendra Sarma | Silvan Käser | Kai Töpfer | Luis Itza Vazquez-Salazar | Eric D. Boittier | Markus Meuwly | Giacomo Mandelli | Cecilia Lanzi | Riccardo Conte | Michele Ceotto | Fabian Dietrich | Vicente Cisternas | Ramachandran Gnanasekaran | Michael Hippler | Mahmoud Jarraya | M. Hochlaf | Narasimhan Viswanathan | Thomas Nevolianis | Gabriel Rath | Wassja A. Kopp | Kai Leonhard | Ricardo A. Mata
Source: ChemRxiv (preprint, 2023-03-03)
Field: Chemistry — Spectroscopy

The first HyDRA challenge for computational vibrational spectroscopy Vibrational spectroscopy in supersonic jet expansions is a powerful tool to assess molecular aggregates in close to ideal conditions for the benchmarking of quantum chemical approaches. The low temperatures achieved as well as the absence of environment effects allow for a direct comparison between computed and experimental spectra. This provides potential benchmarking data which can be revisited to hone different computational techniques, and it allows for the critical analysis of procedures under the setting of a blind challenge. In the latter case, the final result is unknown to modellers, providing an unbiased testing opportunity for quantum chemical models. In this work, we present the spectroscopic and computational results for the first HyDRA blind challenge. The latter deals with the prediction of water donor stretching vibrations in monohydrates of organic molecules. This edition features a test set of 10 systems. Experimental water donor OH vibrational wavenumbers for the vacuum-isolated monohydrates of formaldehyde, tetrahydrofuran, pyridine, tetrahydrothiophene, trifluoroethanol, methyl lactate, dimethylimidazolidinone, cyclooctanone, trifluoroacetophenone and 1-phenylcyclohexane-cis-1,2-diol are provided. The results of the challenge show promising predictive properties in both purely quantum mechanical approaches as well as regression and other machine learning strategies. Molecule ; Spectroscopy ; Quantum ; Tetrahydrothiophene ; Molecular vibration ; Pyridine ; Quantum chemistry ; Computer science ; Computational chemistry ; Chemistry ; Physics ; Quantum mechanics ; Organic chemistry Spectroscopy and Laser Applications ; Atmospheric Ozone and Climate ; Spectroscopy and Quantum Chemical Studies
Authors: Tao Xu | Chunyu Wang | Yuquan Zhu | Yu Wang | Yabin Yan | Jie Wang | Takahiro Shimada | Takayuki Kitamura
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Active Learning Assisted High-Throughput Phase-Field Design of Linear-Superelastic NiTi Alloys with Controllable Modulus Driven by Temperature Gradients Nickel titanium ; Materials science ; Throughput ; Shape-memory alloy ; Field (mathematics) ; Phase (matter) ; Modulus ; Composite material ; Computer science ; Physics ; Mathematics Shape Memory Alloy Transformations ; Piezoelectric Actuators and Control ; Aeroelasticity and Vibration Control
Authors: Peter R. Spackman | Mark A. Spackman | Dylan Jayatilaka | Julian D. Gale
Source: Acta Crystallographica Section A Foundations and Advances (article, 2023-08-22)
Field: Materials Science — Materials Chemistry

How can we use intermolecular interactions in crystals? Lattice energies, predicting crystal growth and more Accurate and fast models for intermolecular interaction energies provide quantitative insight into crystal packing, but their value can extend beyond understanding neighbouring interactions into the prediction of lattice energies [1] and, when combined with solvation models, yield sensible estimates for crystal solubilities in different solvents.We present a new and improved CrystalExplorer interaction energy model [2,3], and its application to predicting lattice energies, solubilities and energies for crystal growth from solution.We also discuss state-of-the-art Density Functional Tight Binding methods like GFN2-xTB [4], general purpose forcefield methods like GFN-FF, [5,6] and machine learning methods to compute intermolecular interaction energies, and how they might be used by theorists and experimentalists alike.In crystals these energies may be visualised through energy frameworks [7], as in Fig. 1, which provide qualitative insight into the underlying energetic structure, and they often correlate with the elastic and mechanical properties of molecular crystals.Figure 1.Coulomb energy framework for uracil crystal, CSD ref. code URACIL, showing the strong hydrogen bond interactions (red), along with weakly repulsive non-binding Coulomb interactions (yellow). Intermolecular force ; Materials science ; Lattice (music) ; Chemical physics ; Crystallography ; Lattice energy ; Crystal (programming language) ; Crystal structure ; Condensed matter physics ; Chemistry ; Physics ; Computer science ; Molecule ; Organic chemistry Crystallization and Solubility Studies
Authors: Taija L. Fischer | Margarethe Bödecker | Sophie M. Schweer | Jennifer Dupont | Valéria Lepère | Anne Zehnacker | Martin A. Suhm | Benjamin Schröder | Tobias Henkes | Diego M. Andrada | Roman M. Balabin | Haobam Kisan Singh | Himangshu Pratim Bhattacharyya | Manabendra Sarma | Silvan Käser | Kai Töpfer | Luis Itza Vazquez-Salazar | Eric D. Boittier | Markus Meuwly | Giacomo Mandelli | Cecilia Lanzi | Riccardo Conte | Michele Ceotto | Fabian Dietrich | Vicente Cisternas | Ramachandran Gnanasekaran | Michael Hippler | Mahmoud Jarraya | M. Hochlaf | Narasimhan Viswanathan | Thomas Nevolianis | Gabriel Rath | Wassja A. Kopp | Kai Leonhard | Ricardo A. Mata
Source: ChemRxiv (preprint, 2023-03-10)
Field: Chemistry — Spectroscopy

The first HyDRA challenge for computational vibrational spectroscopy Vibrational spectroscopy in supersonic jet expansions is a powerful tool to assess molecular aggregates in close to ideal conditions for the benchmarking of quantum chemical approaches. The low temperatures achieved as well as the absence of environment effects allow for a direct comparison between computed and experimental spectra. This provides potential benchmarking data which can be revisited to hone different computational techniques, and it allows for the critical analysis of procedures under the setting of a blind challenge. In the latter case, the final result is unknown to modellers, providing an unbiased testing opportunity for quantum chemical models. In this work, we present the spectroscopic and computational results for the first HyDRA blind challenge. The latter deals with the prediction of water donor stretching vibrations in monohydrates of organic molecules. This edition features a test set of 10 systems. Experimental water donor OH vibrational wavenumbers for the vacuum-isolated monohydrates of formaldehyde, tetrahydrofuran, pyridine, tetrahydrothiophene, trifluoroethanol, methyl lactate, dimethylimidazolidinone, cyclooctanone, trifluoroacetophenone and 1-phenylcyclohexane-cis-1,2-diol are provided. The results of the challenge show promising predictive properties in both purely quantum mechanical approaches as well as regression and other machine learning strategies. Molecule ; Spectroscopy ; Tetrahydrothiophene ; Quantum ; Quantum chemistry ; Computer science ; Quantum chemical ; Molecular vibration ; Computational chemistry ; Chemistry ; Physics ; Quantum mechanics ; Organic chemistry Spectroscopy and Laser Applications ; Atmospheric Ozone and Climate ; Spectroscopy and Quantum Chemical Studies
Authors: Daniel Wines | Kamal Choudhary
Source: arXiv (Cornell University) (preprint, 2023-12-20)
Field: Materials Science — Materials Chemistry

Data-driven Design of High Pressure Hydride Superconductors using DFT and Deep Learning The observation of superconductivity in hydride-based materials under ultrahigh pressures (for example, H$_3$S and LaH$_{10}$) has fueled the interest in a more data-driven approach to discovering new high-pressure hydride superconductors. In this work, we performed density functional theory (DFT) calculations to predict the critical temperature ($T_c$) of over 900 hydride materials under a pressure range of (0 to 500) GPa, where we found 122 dynamically stable structures with a $T_c$ above MgB$_2$ (39 K). To accelerate screening, we trained a graph neural network (GNN) model to predict $T_c$ and demonstrated that a universal machine learned force-field can be used to relax hydride structures under arbitrary pressures, with significantly reduced cost. By combining DFT and GNNs, we can establish a more complete map of hydrides under pressure. Hydride ; Superconductivity ; Density functional theory ; Artificial neural network ; High pressure ; Condensed matter physics ; Chemistry ; Materials science ; Computer science ; Thermodynamics ; Physics ; Computational chemistry ; Machine learning ; Quantum mechanics Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research ; Inorganic Chemistry and Materials
Authors: Peter Minch | Romakanta Bhattarai | Trevor David Rhone
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Data-Driven Study of Magnetic Anisotropy in Transition Metal Dichalcogenide Monolayers Monolayer ; Transition metal ; Anisotropy ; Condensed matter physics ; Materials science ; Nanotechnology ; Chemistry ; Physics ; Optics ; Catalysis 2D Materials and Applications ; Machine Learning in Materials Science ; Magnetic and transport properties of perovskites and related materials
Authors: Shiloh Elliott
Source: OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (review, 2023-03-01)
Field: Materials Science — Materials Chemistry

A Review on the State of the Art of Machine Learning and Satellite Imaging: Detecting Scene Changes in Selected Nuclear Fuel Cycle Datasets The timely detection of clandestine nuclear facilities is one of the greatest challenges faced by the International Atomic Energy Agency’s (IAEA). Idaho National Laboratory is currently applying machine learning (ML) to existing satellite imagery (SI) datasets to find facilities within the nuclear fuel life cycle, with primary focus placed on identifying critical predecessor (i.e., fuel fabrication and fuel enrichment) and successor (i.e., nuclear power plants) facilities. This could provide a satellite image methodology that the IAEA could leverage to discover clandestine facilities. The work presented in this paper describes the evolution of a workflow developed by this team for object detection related to critical infrastructure by expanding that workflow for the purpose of identifying nuclear fuel cycle components and automating dependency assessments. This will be done using two methods housed within a single pipeline. The first method involves implementing a DenseNet161 convolutional neural network to classify the images and explain the results using Local Interpretable Model-Agnostic Explanations (LIME). The second method implements You Only Look Once version 5 (YoloV5), to detect objects within images, provide a probability for the detection, and provide a bounding box that corresponds to the object of interest. The results of this work are anticipated to provide a clear picture of this portion of the nuclear fuel cycle and perform as a stand-alone tool for image assessment that can be expanded to additional fuel cycle components and implemented in international safeguards and national security domains. This capability addresses the IAEA’s need to detect undeclared nuclear materials and activities within a state while encompassing the entire nuclear fuel cycle. Satellite ; Computer science ; State (computer science) ; Artificial intelligence ; Remote sensing ; Computer vision ; Engineering ; Geology ; Aerospace engineering Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Nuclear and radioactivity studies
Authors: Pingping Guo | Wenmei Huang | Xiaobo Feng | Zeyuan Zhang | Yuxin Liu
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Hysteresis Measurement and Generic Modeling of Magnetostrictive Materials Under High-Frequency Excitation and High-Intensity Bias Field Hysteresis ; Magnetostriction ; Excitation ; Field intensity ; Intensity (physics) ; Materials science ; Field (mathematics) ; Condensed matter physics ; Acoustics ; Nuclear magnetic resonance ; Physics ; Electrical engineering ; Optics ; Mathematics ; Magnetic field ; Engineering Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Piezoelectric Actuators and Control
Authors: Tuğbey Kocabaş | Murat Keçeli | Álvaro Vázquez‐Mayagoitia | Cem Sevik
Source: ChemRxiv (preprint, 2023-01-25)
Field: Materials Science — Materials Chemistry

Gaussian Approximation Potentials for Accurate ThermalProperties of Two-Dimensional Materials Two-dimensional materials (2DMs) continue to attract a lot of attention, particularly for their extreme flexibility and superior thermal properties. Molecular dynamics simulations are among the most powerful methods for computing these properties, but their reliability depends on the accuracy of interatomic interactions. While first principles approaches provide the most accurate description of interatomic forces, they are computationally expensive. In contrast, classical force fields are computationally efficient, but have limited accuracy in interatomic force description. Machine learning interatomic potentials, such as Gaussian Approximation Potentials, trained on density functional theory (DFT) calculations offer a compromise by providing both accurate estimation and computational efficiency. In this work, we present a systematic procedure to develop Gaussian approximation potentials for selected 2DMs, graphene, buckled silicene, and <i>h</i>-XN (X = B, Al, and Ga, as binary compounds) structures. We validate our approach through calculations that require various levels of accuracy in interatomic interactions. The calculated phonon dispersion curves and lattice thermal conductivity, obtained through harmonic and anharmonic force constants (including fourth order) are in excellent agreement with DFT results. HIPHIVE calculations, in which the generated GAP potentials were used to compute higher-order force constants instead of DFT, demonstrated the first-principles level accuracy of the potentials for interatomic force description. Molecular dynamics simulations based on phonon density of states calculations, which agree closely with DFT-based calculations, also show the success of the generated potentials in high-temperature simulations. Interatomic potential ; Anharmonicity ; Molecular dynamics ; Gaussian ; Density functional theory ; Phonon ; Statistical physics ; Work (physics) ; Physics ; Quantum mechanics Thermal properties of materials ; Machine Learning in Materials Science ; Graphene research and applications
Authors: Qingwei Zhang | Lin Cai | yunhua lu | Jun’an Zhang | Chao Zhang | Kangli Zeng | jintao yu
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Work Function Prediction by Graph Neural Networks for Configurationally Hybrized Boron-Doped Graphene Graphene ; Boron ; Graph ; Doping ; Artificial neural network ; Function (biology) ; Computer science ; Materials science ; Artificial intelligence ; Theoretical computer science ; Nanotechnology ; Chemistry ; Optoelectronics ; Cell biology ; Organic chemistry ; Biology Machine Learning in Materials Science ; Graphene research and applications ; Advanced Memory and Neural Computing
Authors: Emanuele Costa | Rosario Fazio | Sebastiano Pilati
Source: arXiv (Cornell University) (preprint, 2023-05-24)
Field: Materials Science — Materials Chemistry

Deep learning nonlocal and scalable energy functionals for quantum Ising models Density functional theory (DFT) is routinely employed in material science and in quantum chemistry to simulate weakly correlated electronic systems. Recently, deep learning (DL) techniques have been adopted to develop promising functionals for the strongly correlated regime. DFT can be applied to quantum spin models too, but functionals based on DL have not been developed yet. Here, we investigate DL-based DFTs for random quantum Ising chains, both with nearest-neighbor and up to next-nearest neighbor couplings. Our neural functionals are trained and tested on data produced via the Jordan-Wigner transformation, exact diagonalization, and tensor-network methods. An economical gradient-descent optimization is used to find the ground-state properties of previously unseen Hamiltonian instances. Notably, our non-local functionals drastically improve upon the common local density approximations, and they are designed to be scalable, allowing us to simulate chain sizes much larger than those used for training. The prediction accuracy is analyzed, paying attention to the spatial correlations of the learned functionals and to the role of quantum criticality. Our findings indicate a suitable strategy to extend the reach of other computational methods with a controllable accuracy. Ising model ; Hamiltonian (control theory) ; Quantum ; Statistical physics ; Scalability ; Density functional theory ; k-nearest neighbors algorithm ; Computer science ; Gradient descent ; Ground state ; Artificial neural network ; Physics ; Mathematics ; Artificial intelligence ; Quantum mechanics ; Mathematical optimization Machine Learning in Materials Science ; Quantum many-body systems ; Spectroscopy and Quantum Chemical Studies
Authors: Wai-Pan Ng | Qiujiang Liang | Jun Yang
Source: arXiv (Cornell University) (preprint, 2023-05-17)
Field: Materials Science — Materials Chemistry

Low-data deep quantum chemical learning for accurate MP2 and coupled-cluster correlations Accurate ab-initio prediction of electronic energies is very expensive for macromolecules by explicitly solving post-Hartree-Fock equations. We here exploit the physically justified local correlation feature in compact basis of small molecules, and construct an expressive low-data deep neural network (dNN) model to obtain machine-learned electron correlation energies on par with MP2 and CCSD levels of theory for more complex molecules and different datasets that are not represented in the training set. We show that our dNN-powered model is data efficient and makes highly transferable prediction across alkanes of various lengths, organic molecules with non-covalent and biomolecular interactions, as well as water clusters of different sizes and morphologies. In particular, by training 800 (H$_2$O)$_8$ clusters with the local correlation descriptors, accurate MP2/cc-pVTZ correlation energies up to (H$_2$O)$_{128}$ can be predicted with a small random error within chemical accuracy from exact values, while a majority of prediction deviations are attributed to an intrinsically systematic error. Our results reveal that an extremely compact local correlation feature set, which is poor for any direct post-Hartree-Fock calculations, has however a prominent advantage in reserving important electron correlation patterns for making accurate transferable predictions across distinct molecular compositions, bond types and geometries. Electronic correlation ; Statistical physics ; Coupled cluster ; Correlation ; Artificial neural network ; Ab initio ; Feature (linguistics) ; Set (abstract data type) ; Basis (linear algebra) ; Cluster (spacecraft) ; Basis set ; Molecule ; Quantum ; Computer science ; Physics ; Chemistry ; Artificial intelligence ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Various Chemistry Research Topics