jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Prabudhya Roy Chowdhury | Xiulin Ruan
Source:  (preprint, 2021-07-16)
Field: Materials Science — Materials Chemistry

An Iterative Machine Learning Approach for Discovering Unexpected Thermal Conductivity Enhancement in Aperiodic Superlattices While machine learning (ML) has shown increasing effectiveness in optimizing materials properties under known physics, its application in challenging conventional wisdom and discovering new physics still remains challenging due to its interpolative nature.In this work, we demonstrate the potential of using ML for such applications by implementing an adaptive ML-accelerated search process that can discover unexpected lattice thermal conductivity (κ l ) enhancement instead of reduction in aperiodic superlattices (SLs) as compared to periodic superlattices.We use non-equilibrium molecular dynamics (NEMD) simulations for high-fidelity calculations of κ l for a small fraction of SLs in the search space, along with a convolutional neural network (CNN) which can rapidly predict κ l for a large number of structures.To ensure accurate prediction by the CNN for the target unknown structures, we iteratively identify aperiodic SLs containing structural features which lead to locally enhanced thermal transport, and include them as additional training data for the CNN in each iteration.As a result, our CNN can accurately predict the high κ l of aperiodic SLs that are absent from the initial training dataset, which allows us to identify the previously unseen exceptional structures.The identified RML structures exhibit increased coherent phonon contribution to thermal conductivity owing to the presence of closely spaced interfaces.Our work describes a general purpose machine learning approach for identifying low-probability-of-occurrence exceptional solutions within an extremely large subspace and discovering the underlying physics. Aperiodic graph ; Superlattice ; Thermal conductivity ; Computer science ; Artificial intelligence ; Materials science ; Optoelectronics ; Mathematics ; Composite material Thermal properties of materials ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Iaroslav Gaponenko | Loïc Musy | Neus Domingo | N. Stucki | Albert Verdaguer | Nazanin Bassiri‐Gharb | Patrycja Paruch
Source: npj Computational Materials (preprint, 2021-10-05)
Field: Materials Science — Materials Chemistry

Local and correlated studies of humidity-mediated ferroelectric thin film surface charge dynamics Abstract Electrochemical phenomena in ferroelectrics are of particular interest for catalysis and sensing applications, with recent studies highlighting the combined role of the ferroelectric polarisation, applied surface voltage and overall switching history. Here, we present a systematic Kelvin probe microscopy study of the effect of relative humidity and polarisation switching history on the surface charge dissipation in ferroelectric Pb(Zr 0.2 Ti 0.8 )O 3 thin films. We analyse the interaction of surface charges with ferroelectric domains through the framework of physically constrained unsupervised machine learning matrix factorisation, Dictionary Learning, and reveal a complex interplay of voltage-mediated physical processes underlying the observed signal decays. Additional insight into the observed behaviours is given by a Fitzhugh–Nagumo reaction–diffusion model, highlighting the lateral spread and charge passivation process contributors within the Dictionary Learning analysis. Ferroelectricity ; Materials science ; Passivation ; Thin film ; Charge (physics) ; Chemical physics ; Surface charge ; Hysteresis ; Nanotechnology ; Optoelectronics ; Condensed matter physics ; Chemistry ; Physics ; Physical chemistry Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials ; Ferroelectric and Negative Capacitance Devices
Authors: Kishalay Das | Bidisha Samanta | Pawan Goyal | Seung‐Cheol Lee | Satadeep Bhattacharjee | Niloy Ganguly
Source: arXiv (Cornell University) (preprint, 2021-04-22)
Field: Materials Science — Materials Chemistry

CrysXPP:An Explainable Property Predictor for Crystalline Material We present a deep-learning framework, CrysXPP, to allow rapid prediction of electronic, magnetic and elastic properties of a wide range of materials with reasonable precision. Although our work is consistent with several recent attempts to build deep learning-based property predictors, it overcomes some of their limitations. CrysXPP lowers the need for a large volume of tagged data to train a deep learning model by intelligently designing an autoencoder CrysAE and passing the structural information to the property prediction process. The autoencoder in turn is trained on a huge volume of untagged crystal graphs, the designed loss function helps in capturing all their important structural and chemical information. Moreover, CrysXPP uses only a small amount of tagged data for property prediction, and also trains a feature selector that provides interpretability to the results obtained. We demonstrate that CrysXPP convincingly performs better than all the competing and recent baseline algorithms across seven diverse set of properties. Most notably, when given a small amount of experimental data, CrysXPP is consistently able to outperform conventional DFT. We release the large pretrained model CrysAE so that it could be fine-tuned using small amount of tagged data by the research community on various applications with restricted data source. Interpretability ; Autoencoder ; Property (philosophy) ; Computer science ; Artificial intelligence ; Machine learning ; Deep learning ; Feature (linguistics) ; Process (computing) ; Data mining ; Set (abstract data type) ; Volume (thermodynamics) ; Function (biology) ; Pattern recognition (psychology) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Shampa Raghunathan
Source:  (preprint, 2021-07-05)
Field: Materials Science — Materials Chemistry

Intramolecular proton transfer reaction dynamics using machine learned ab initio potential energy surfaces Hydrogen bonding interactions central to various physicochemical processes are investigated in the present study using ab initio-based machine learning potential energy surfaces. Abnormally strong intramolecular O-H...O hydrogen bonds occurring in beta-diketone enols of malonaldehyde, and its derivatives with substituents ranging from various electron-withdrawing to electron-donating functional groups are studied. Machine learning force fields were constructed by using a kernel-based force learning model employing ab initio molecular dynamics reference data. These models were used for molecular dynamics simulations at finite temperature, and dynamical properties were determined by computing their proton transfer free energy surfaces. Chemical systems studied here show progression towards forming barrierless proton transfer events at an accuracy of highly correlated electronic structure methods. Markov state models of the conformational states indicate shorter intramolecular hydrogen bonds exhibiting higher proton transfer rates. We demonstrate how functional group substitution can modulate the strength of intramolecular hydrogen bonds by studying their thermodynamic and kinetic properties. Intramolecular force ; Ab initio ; Hydrogen bond ; Chemistry ; Proton ; Molecular dynamics ; Computational chemistry ; Electron transfer ; Density functional theory ; Ab initio quantum chemistry methods ; Proton-coupled electron transfer ; Chemical physics ; Molecule ; Physical chemistry ; Physics ; Stereochemistry ; Quantum mechanics Machine Learning in Materials Science ; Crystallography and molecular interactions ; Advanced Chemical Physics Studies
Authors: Shampa Raghunathan
Source:  (preprint, 2021-06-29)
Field: Materials Science — Materials Chemistry

Intramolecular proton transfer reaction dynamics using machine learned ab initio potential energy surfaces Hydrogen bonding interactions central to various physicochemical processes are investigated in the present study using ab initio-based machine learning potential energy surfaces. Abnormally strong intramolecular O-H...O hydrogen bonds occurring in beta-diketone enols of malonaldehyde, and its derivatives with substituents ranging from various electron-withdrawing to electron-donating functional groups are studied. Machine learning force fields were constructed by using a kernel-based force learning model employing ab initio molecular dynamics reference data. These models were used for molecular dynamics simulations at finite temperature, and dynamical properties were determined by computing their proton transfer free energy surfaces. Chemical systems studied here show progression towards forming barrierless proton transfer events at an accuracy of highly correlated electronic structure methods. Markov state models of the conformational states indicate shorter intramolecular hydrogen bonds exhibiting higher proton transfer rates. We demonstrate how functional group substitution can modulate the strength of intramolecular hydrogen bonds by studying their thermodynamic and kinetic properties. Intramolecular force ; Ab initio ; Hydrogen bond ; Chemistry ; Proton ; Molecular dynamics ; Computational chemistry ; Electron transfer ; Density functional theory ; Ab initio quantum chemistry methods ; Proton-coupled electron transfer ; Chemical physics ; Physical chemistry ; Molecule ; Physics ; Stereochemistry ; Organic chemistry ; Quantum mechanics Machine Learning in Materials Science ; Crystallography and molecular interactions ; Advanced Chemical Physics Studies
Authors: Aria Mansouri Tehrani | Bastien F. Grosso | Ramón Frey | Nicola A. Spaldin
Source: Acta Crystallographica Section A Foundations and Advances (article, 2021-08-14)
Field: Materials Science — Materials Chemistry

Predicting ground-state and metastable crystal structures using elemental and phonon mode descriptors We present a method to predict the crystal structure of any given composition using machine learning methods.Then, using the example of bismuth ferrite, we illustrate how crystal structure, decomposed into distortion modes, can be implemented as a feature to explore the energy surface leading to the identification of metastable polymorphs.Crystal structure plays a crucial role in determining the electronic structure and property of any composition.Therefore, it has always been of great interest to predict the crystal structure of any composition without requiring synthesis and characterization.To achieve this goal, we combine machine learning and density functional theory (DFT) calculations.Initially, a classification model predicts the point groups of the given stoichiometries.Based on the predicted point group, a series of high-throughput DFT calculations determine the ground state of non-centrosymmetric crystal structures.In addition to the ground state structure, identifying metastable polymorphs that might get stabilized by controlling the synthetic conditions is of great importance as they can exhibit different functionalities.Therefore, we studied BiFeO3 as a multifunctional compound with a rich low-energy phase space.A training set is constructed by mapping the phase space based on possible distortion modes starting from the cubic perovskite structure.A machine learning model is built using the generated training set predicting the energy surface of BiFeO3 to explore new metastable phases. Metastability ; Crystal structure prediction ; Crystal structure ; Density functional theory ; Materials science ; Ground state ; Crystal (programming language) ; Chemical physics ; Artificial intelligence ; Crystallography ; Computer science ; Chemistry ; Computational chemistry ; Physics ; Atomic physics ; Quantum mechanics Machine Learning in Materials Science
Authors: Kishalay Das | Bidisha Samanta | Pawan Goyal | Seung-Cheol Lee | Satadeep Bhattacharjee | Niloy Ganguly
Source: arXiv (Cornell University) (preprint, 2021-04-22)
Field: Materials Science — Materials Chemistry

CrysXPP:An Explainable Property Predictor for Crystalline Materials We present a deep-learning framework, CrysXPP, to allow rapid prediction of electronic, magnetic and elastic properties of a wide range of materials with reasonable precision. Although our work is consistent with several recent attempts to build deep learning-based property predictors, it overcomes some of their limitations. CrysXPP lowers the need for a large volume of tagged data to train a deep learning model by intelligently designing an autoencoder CrysAE and passing the structural information to the property prediction process. The autoencoder in turn is trained on a huge volume of untagged crystal graphs, the designed loss function helps in capturing all their important structural and chemical information. Moreover, CrysXPP uses only a small amount of tagged data for property prediction, and also trains a feature selector that provides interpretability to the results obtained. We demonstrate that CrysXPP convincingly performs better than all the competing and recent baseline algorithms across seven diverse set of properties. Most notably, when given a small amount of experimental data, CrysXPP is consistently able to outperform conventional DFT. We release the large pretrained model CrysAE so that it could be fine-tuned using small amount of tagged data by the research community on various applications with restricted data source. Interpretability ; Autoencoder ; Property (philosophy) ; Computer science ; Artificial intelligence ; Deep learning ; Machine learning ; Feature (linguistics) ; Volume (thermodynamics) ; Process (computing) ; Set (abstract data type) ; Data mining ; Pattern recognition (psychology) Machine Learning in Materials Science ; Topic Modeling ; Advanced Graph Neural Networks
Authors: Prabudhya Roy Chowdhury | Xiulin Ruan
Source:  (preprint, 2021-07-16)
Field: Materials Science — Materials Chemistry

An Iterative Machine Learning Approach for Discovering Unexpected Thermal Conductivity Enhancement in Aperiodic Superlattices While machine learning (ML) has shown increasing effectiveness in optimizing materials properties under known physics, its application in challenging conventional wisdom and discovering new physics still remains challenging due to its interpolative nature.In this work, we demonstrate the potential of using ML for such applications by implementing an adaptive ML-accelerated search process that can discover unexpected lattice thermal conductivity (κ l ) enhancement instead of reduction in aperiodic superlattices (SLs) as compared to periodic superlattices.We use non-equilibrium molecular dynamics (NEMD) simulations for high-fidelity calculations of κ l for a small fraction of SLs in the search space, along with a convolutional neural network (CNN) which can rapidly predict κ l for a large number of structures.To ensure accurate prediction by the CNN for the target unknown structures, we iteratively identify aperiodic SLs containing structural features which lead to locally enhanced thermal transport, and include them as additional training data for the CNN in each iteration.As a result, our CNN can accurately predict the high κ l of aperiodic SLs that are absent from the initial training dataset, which allows us to identify the previously unseen exceptional structures.The identified RML structures exhibit increased coherent phonon contribution to thermal conductivity owing to the presence of closely spaced interfaces.Our work describes a general purpose machine learning approach for identifying low-probability-of-occurrence exceptional solutions within an extremely large subspace and discovering the underlying physics. Aperiodic graph ; Superlattice ; Thermal conductivity ; Computer science ; Materials science ; Thermal ; Condensed matter physics ; Artificial intelligence ; Physics ; Optoelectronics ; Mathematics ; Thermodynamics ; Composite material ; Combinatorics Machine Learning in Materials Science ; Thermal properties of materials ; Advanced Thermoelectric Materials and Devices
Authors: Lucian Chan | Garrett M. Morris | Geoffrey Hutchison
Source: ChemRxiv (preprint, 2021-02-28)
Field: Materials Science — Materials Chemistry

Understanding Conformational Entropy in Small Molecules The calculation of the entropy of flexible molecules can be challenging, since the number of possible conformers grows exponentially with molecule size and many low-energy conformers may be thermally accessible. Different methods have been proposed to approximate the contribution of conformational entropy to the molecular standard entropy, including performing thermochemistry calculations with all possible stable conformations, and developing empirical corrections from experimental data. We have performed conformer sampling on over 120,000 small molecules generating some 12 million conformers, to develop models to predict conformational entropy across a wide range of molecules. Using insight into the nature of conformational disorder, our cross-validated physically-motivated statistical model can outperform common machine learning and deep learning methods, with a mean absolute error ≈4.8 J/mol•K, or under 0.4 kcal/mol at 300 K. Beyond predicting molecular entropies and free energies, the model implies a high degree of correlation between torsions in most molecules, often as- sumed to be independent. While individual dihedral rotations may have low energetic barriers, the shape and chemical functionality of most molecules necessarily correlate their torsional degrees of freedom, and hence restrict the number of low-energy conformations immensely. Our simple models capture these correlations, and advance our understanding of small molecule conformational entropy. Conformational isomerism ; Conformational entropy ; Dihedral angle ; Entropy (arrow of time) ; Molecule ; Thermochemistry ; Statistical physics ; Conformational ensembles ; Chemistry ; Configuration entropy ; Computational chemistry ; Thermodynamics ; Molecular dynamics ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Nathan C. Frey | Siddharth Samsi | Bharath Ramsundar | Connor W. Coley | Vijay Gadepally
Source: arXiv (Cornell University) (preprint, 2021-12-09)
Field: Materials Science — Materials Chemistry

Bringing Atomistic Deep Learning to Prime Time Artificial intelligence has not yet revolutionized the design of materials and molecules. In this perspective, we identify four barriers preventing the integration of atomistic deep learning, molecular science, and high-performance computing. We outline focused research efforts to address the opportunities presented by these challenges. Deep learning ; Prime (order theory) ; Perspective (graphical) ; Prime time ; Data science ; Nanotechnology ; Artificial intelligence ; Computer science ; Engineering ethics ; Engineering ; Materials science ; Mathematics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Electron and X-Ray Spectroscopy Techniques
Authors: John M. Simmie
Source: ChemRxiv (preprint, 2021-12-14)
Field: Chemistry — Physical and Theoretical Chemistry

C2H5NO Isomers: from Acetamide to1,2-Oxazetidine and Beyond This work documents the properties of a number of isomers of molecular formula C2H5NO from the most stable, acetamide, through 1,2-oxazetidine and including even higher energy species largely of a dipolar nature. Only two of the isomers have been detected in emissions from the interstellar medium (ISM); possible further candidates are identifi ed and the likelihood of their being detectable are considered. In general hardly any of these compounds have featured in the existing chemical literature so this work represents an important contribution extending the canon of chemical bonding which can contribute to machine-learning | providing a more exacting test of AI applications. The presence of acetamide, CH3C(O)NH2, is the subject of current debate with no clear and obvious paths to its formation; it is shown that a 1,3[H]-transfer from (E,Z ) ethanimidic acid, CH3C(OH){{NH, is feasible in spite of an energy barrier of 130 kJ/mol. It is speculated that the imidic acid can itself be formed from abundant precursors, H2O and CH3C{{{N, in an acid-induced, water addition, auto-catalytic reaction on water-ice grains. Acetamide ; Catalysis ; Chemistry ; Work (physics) ; Computational chemistry ; Thermodynamics ; Organic chemistry ; Physics Chemical Reactions and Mechanisms ; Molecular Spectroscopy and Structure ; Advanced Chemical Physics Studies
Authors: Haidong Xie | Xueshuang Xiang | Chen, Yuanqing
Source: arXiv (Cornell University) (preprint, 2021-09-18)
Field: Materials Science — Materials Chemistry

Improving the Deconvolution of Spectrum at Finite Temperature via Neural Network In the study of condensed matter physics, spectral information plays an important role for understand the mechanism of materials. However, it is difficult to obtain the spectrum directly through experiments or simulation. For example, the spectral information deconvoluted by scanning tunneling spectroscopy suffers from the temperature broadening effect, which is ill-posed and makes the deconvolution result unstable. To solve this problem, the core idea of existing methods, such as the maximum entropy method, tends to select appropriate regularization to suppress unstable oscillations. However, the choice of regularization is difficult, and the oscillation has not been completely eliminated. We think non-uniform sampling is the core improvement direction, combined with stochastic optimization and deep learning, we introduce a neural network based discretization scheme to solve the deconvolution problem. Due to the neural network can represent any piece-wise linear function, our method replace the target spectrum by network and can find a better approximation solution through optimization accurate and efficient. Experiments on theoretical datasets about superconductors demonstrate that the gap is estimated to be more accurate and oscillating less, plugin real experimental data, our approach can get clearer results for material analysis. Deconvolution ; Artificial neural network ; Discretization ; Regularization (linguistics) ; Computer science ; Algorithm ; Mathematical optimization ; Applied mathematics ; Statistical physics ; Mathematics ; Artificial intelligence ; Physics ; Mathematical analysis Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: John M. Simmie
Source: ChemRxiv (preprint, 2021-12-20)
Field: Chemistry — Physical and Theoretical Chemistry

C2H5NO Isomers: from Acetamide to1,2-Oxazetidine and Beyond This work documents the properties of a number of isomers of molecular formula C2H5NO from the most stable, acetamide, through 1,2-oxazetidine and including even higher energy species largely of a dipolar nature. Only two of the isomers have been detected in emissions from the interstellar medium (ISM); possible further candidates are identifi ed and the likelihood of their being detectable are considered. In general hardly any of these compounds have featured in the existing chemical literature so this work represents an important contribution extending the canon of chemical bonding which can contribute to machine-learning | providing a more exacting test of AI applications. The presence of acetamide, CH3C(O)NH2, is the subject of current debate with no clear and obvious paths to its formation; it is shown that a 1,3[H]-transfer from (E,Z ) ethanimidic acid, CH3C(OH){{NH, is feasible in spite of an energy barrier of 130 kJ/mol. It is speculated that the imidic acid can itself be formed from abundant precursors, H2O and CH3C{{{N, in an acid-induced, water addition, auto-catalytic reaction on water-ice grains. Acetamide ; Catalysis ; Chemistry ; Work (physics) ; Computational chemistry ; Organic chemistry ; Thermodynamics ; Physics Chemical Reactions and Mechanisms ; Molecular Spectroscopy and Structure ; Advanced Chemical Physics Studies