jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chen Cheng | Sheng Zhang | Gia-Wei Chern
Source: arXiv (Cornell University) (preprint, 2023-03-06)
Field: Materials Science — Materials Chemistry

Machine learning for phase ordering dynamics of charge density waves We present a machine learning (ML) framework for large-scale dynamical simulations of charge density wave (CDW) states. The charge modulation in a CDW state is often accompanied by a concomitant structural distortion, and the adiabatic evolution of a CDW order is governed by the dynamics of the lattice distortion. Calculation of the electronic contribution to the driving forces, however, is computationally very expensive for large systems. Assuming the principle of locality for electron systems, a neural-network model is developed to accurately and efficiently predict local electronic forces with input from neighborhood configurations. Importantly, the ML model makes possible a linear complexity algorithm for dynamical simulations of CDWs. As a demonstration, we apply our approach to investigate the phase ordering dynamics of the Holstein model, a canonical system of CDW order. Our large-scale simulations uncover an intriguing growth of the CDW domains that deviates significantly from the expected Allen-Cahn law for phase ordering of Ising-type order parameter field. This anomalous domain-growth could be attributed to the complex structure of domain-walls in this system. Our work highlights the promising potential of ML-based force-field models for dynamical simulations of functional electronic materials. Statistical physics ; Charge density wave ; Physics ; Lattice (music) ; Ising model ; Adiabatic process ; Charge (physics) ; Condensed matter physics ; Quantum mechanics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Physics of Superconductivity and Magnetism
Authors: Nicholas S. M. Herringer | Siva Dasetty | Diya Gandhi | Junhee Lee | Andrew L. Ferguson
Source: arXiv (Cornell University) (preprint, 2023-08-16)
Field: Materials Science — Materials Chemistry

Permutationally Invariant Networks for Enhanced Sampling (PINES): Discovery of Multi-Molecular and Solvent-Inclusive Collective Variables The typically rugged nature of molecular free energy landscapes can frustrate efficient sampling of the thermodynamically relevant phase space due to the presence of high free energy barriers. Enhanced sampling techniques can improve phase space exploration by accelerating sampling along particular collective variables (CVs). A number of techniques exist for data-driven discovery of CVs parameterizing the important large scale motions of the system. A challenge to CV discovery is learning CVs invariant to symmetries of the molecular system, frequently rigid translation, rigid rotation, and permutational relabeling of identical particles. Of these, permutational invariance have proved a persistent challenge in frustrating the the data-driven discovery of multi-molecular CVs in systems of self-assembling particles and solvent-inclusive CVs for solvated systems. In this work, we integrate Permutation Invariant Vector (PIV) featurizations with autoencoding neural networks to learn nonlinear CVs invariant to translation, rotation, and permutation, and perform interleaved rounds of CV discovery and enhanced sampling to iteratively expand sampling of configurational phase space and obtain converged CVs and free energy landscapes. We demonstrate the Permutationally Invariant Network for Enhanced Sampling (PINES) approach in applications to the self-assembly of a 13-atom Argon cluster, association/dissociation of a NaCl ion pair in water, and hydrophobic collapse of a C45H92 n-pentatetracontane polymer chain. We make the approach freely available as a new module within the PLUMED2 enhanced sampling libraries. Invariant (physics) ; Computer science ; Statistical physics ; Sampling (signal processing) ; Scale invariance ; Molecular dynamics ; Phase space ; Homogeneous space ; Energy landscape ; Physics ; Algorithm ; Theoretical computer science ; Mathematics ; Quantum mechanics ; Geometry ; Thermodynamics ; Computer vision Machine Learning in Materials Science ; Hydrocarbon exploration and reservoir analysis ; Protein Structure and Dynamics
Authors: Etienne Palos | Saswata Dasgupta | Eleftherios Lambros | Francesco Paesani
Source: ChemRxiv (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Data-Driven Many-Body Potentials from Density Functional Theory for Aqueous Phase Chemistry Density functional theory (DFT) has been applied to modeling molecular interactions in water for over three decades. The ubiquity of water in chemical and biological processes demands a unified understanding of its physics, from the single-molecule to the thermodynamic limit and everything in between. Recent advances in the development of data-driven and machine-learning potentials have accelerated simulation of water and aqueous systems with DFT accuracy. However, the anomalous properties of water in the condensed phase, where a rigorous treatment of both local and non-local many-body interactions is in order, is often unsatisfactory or partially missing in DFT models of water. In this review, we discuss the modeling of water and aqueous systems based on DFT, and provide a comprehensive description of a general theoretical/computational framework for the development of data-driven many-body potentials from DFT reference data. This framework, coined MB-DFT, readily enables efficient many-body molecular dynamics (MB-MD) simulations of small molecules, in both gas and condensed phases, while preserving the accuracy of the underly- ing DFT model. Theoretical considerations are emphasized, including the role that the delocalization error plays in MB-DFT potentials of water, and the possibility to elevate DFT and MB-DFT to near-chemical-accuracy through a density-corrected formalism. The development of the MB-DFT framework is described in detail, along with its application in MB-MD simulations and recent extension to the modeling of reactive processes in solution within a quantum mechanics/many-body molecular mechanics (QM/MB-MM) scheme, using water as a prototypical solvent. Finally, we identify open challenges and discuss future directions for MB-DFT and QM/MB-MM simulations in condensed phases. Density functional theory ; Delocalized electron ; Statistical physics ; Molecular dynamics ; Formalism (music) ; Aqueous solution ; Molecule ; Chemistry ; Computational chemistry ; Computer science ; Chemical physics ; Physics ; Quantum mechanics ; Physical chemistry Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Quantum, superfluid, helium dynamics
Authors: Yaoguang Zhai | Alessandro Caruso | Sigbjørn Løland Bore | Zhishang Luo | Francesco Paesani
Source: ChemRxiv (preprint, 2023-01-31)
Field: Materials Science — Materials Chemistry

A “short blanket” dilemma for a state-of-the-art neural network potential for water: Reproducing experimental properties or the physics of the underlying many-body interactions? Deep neural network (DNN) potentials have recently gained popularity in computer simulations of a wide range of molecular systems, from liquids to materials. In this study, we explore the possibility of combining the computational efficiency of the DeePMD framework and the demonstrated accuracy of the MB-pol data-driven many-body potential to train a DNN potential for large-scale simulations of water across its phase diagram. We find that the DNN potential is able to reliably reproduce the MB-pol results for liquid water but provides a less accurate description of the vapor-liquid equilibrium properties. This shortcoming is traced back to the inability of the DNN potential to correctly represent many-body interactions. An attempt to explicitly include information about many-body effects results in a new DNN potential that exhibits opposite performance, being able to correctly reproduce the MB-pol vapor-liquid equilibrium properties but losing accuracy in the description of the liquid properties. These results suggest that DeePMD-based DNN potentials are not able to correctly "learn" and, consequently, represent many-body interactions, which implies that DNN potentials may have limited ability to predict properties for state points that are not explicitly included in the training process. The computational efficiency of the DeePMD framework can still be exploited to train DNN potentials on data-driven many-body potentials, which can thus enable large-scale, "chemically accurate" simulations of various molecular systems, with the caveat that the target state points must have been adequately sampled by the reference data-driven many-body potential in order to guarantee a faithful representation of the associated properties. Computer science ; Process (computing) ; Artificial neural network ; Range (aeronautics) ; Scale (ratio) ; Artificial intelligence ; Liquid water ; State (computer science) ; Statistical physics ; Physics ; Algorithm ; Aerospace engineering ; Engineering Machine Learning in Materials Science ; Quantum, superfluid, helium dynamics ; Quantum many-body systems
Authors: Ahmed Agiza | Rajarshi Roy | Teodor Dumitru Ene | Saad Godil | Sherief Reda | Bryan Catanzaro
Source: arXiv (Cornell University) (preprint, 2023-08-07)
Field: Materials Science — Materials Chemistry

GraPhSyM: Graph Physical Synthesis Model In this work, we introduce GraPhSyM, a Graph Attention Network (GATv2) model for fast and accurate estimation of post-physical synthesis circuit delay and area metrics from pre-physical synthesis circuit netlists. Once trained, GraPhSyM provides accurate visibility of final design metrics to early EDA stages, such as logic synthesis, without running the slow physical synthesis flow, enabling global co-optimization across stages. Additionally, the swift and precise feedback provided by GraPhSyM is instrumental for machine-learning-based EDA optimization frameworks. Given a gate-level netlist of a circuit represented as a graph, GraPhSyM utilizes graph structure, connectivity, and electrical property features to predict the impact of physical synthesis transformations such as buffer insertion and gate sizing. When trained on a dataset of 6000 prefix adder designs synthesized at an aggressive delay target, GraPhSyM can accurately predict the post-synthesis delay (98.3%) and area (96.1%) metrics of unseen adders with a fast 0.22s inference time. Furthermore, we illustrate the compositionality of GraPhSyM by employing the model trained on a fixed delay target to accurately anticipate post-synthesis metrics at a variety of unseen delay targets. Lastly, we report promising generalization capabilities of the GraPhSyM model when it is evaluated on circuits different from the adders it was exclusively trained on. The results show the potential for GraPhSyM to serve as a powerful tool for advanced optimization techniques and as an oracle for EDA machine learning frameworks. Netlist ; Computer science ; Adder ; High-level synthesis ; Computer engineering ; Physical design ; Graph ; Logic synthesis ; Algorithm ; Computer architecture ; Parallel computing ; Theoretical computer science ; Circuit design ; Logic gate ; Field-programmable gate array ; Computer hardware ; Embedded system Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; VLSI and Analog Circuit Testing
Authors: Ryosuke Nishitsuji | Tomoharu Nakashima | Kenji Sueyoshi | Hideaki Hisamoto | Tatsuro Endo
Source:  (article, 2023-04-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Discrimination of self-assembled monolayer composition on gold nanostructure surface by combining machine learning and surface enhanced Raman scattering Raman spectroscopy is a spectroscopic method which indirectly measures molecular vibration and can identify molecules by obtaining their unique vibrational fingerprints. Raman spectroscopy has the advantage of non-invasive and label-free detection, but it has the problem of low sensitivity due to the very weak intensity of Raman scattered light. In order to make Raman spectroscopy high sensitivity, surface enhanced Raman scattering (SERS) induced via metallic nanostructures has been widely studied. SERS is attracting attention in various fields of analysis such as bioanalysis, material analysis, environmental analysis, and food analysis because of its high sensitivity and selectivity. However, SERS spectra of adsorbed molecules with similar structures have very similar shapes, making it difficult to discriminate molecules from their spectra. Especially, it is even more difficult to determine the mixing ratio of mixtures of those molecules. Thus, we focused on the use of machine learning to determine the mixing ratio of similar structural molecules on gold nanostructure. In this study, we fabricated gold nanostructures by depositing gold on a cyclo-olefin polymer (COP) film with periodical nanostructure. Then, self-assembled monolayer (SAM) of mixed benzene thiol derivatives, as a model of surface adsorbed molecules, were prepared on the surface of gold nanostructure, and measured SERS spectra. We examined several machine learning models that can accurately determine mixing ratios from the obtained spectra. As a result, we succeeded in determining the mixing ratios of molecules with approximately 99% accuracy, with multilayer perceptron (MLP) model being the most accurate. Raman spectroscopy ; Nanostructure ; Raman scattering ; Monolayer ; Materials science ; Surface-enhanced Raman spectroscopy ; Molecule ; Spectroscopy ; Analytical Chemistry (journal) ; Adsorption ; Nanotechnology ; Chemistry ; Optics ; Physical chemistry ; Organic chemistry Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Dendrimers and Hyperbranched Polymers
Authors: Kevin K. Huguenin-Dumittan | Philip Loche | Ni Haoran | Michele Ceriotti
Source: arXiv (Cornell University) (preprint, 2023-08-25)
Field: Materials Science — Materials Chemistry

Physics-inspired Equivariant Descriptors of Non-bonded Interactions One essential ingredient in many machine learning (ML) based methods for atomistic modeling of materials and molecules is the use of locality. While allowing better system-size scaling, this systematically neglects long-range (LR) effects, such as electrostatics or dispersion interaction. We present an extension of the long distance equivariant (LODE) framework that can handle diverse LR interactions in a consistent way, and seamlessly integrates with preexisting methods by building new sets of atom centered features. We provide a direct physical interpretation of these using the multipole expansion, which allows for simpler and more efficient implementations. The framework is applied to simple toy systems as proof of concept, and a heterogeneous set of molecular dimers to push the method to its limits. By generalizing LODE to arbitrary asymptotic behaviors, we provide a coherent approach to treat arbitrary two- and many-body non-bonded interactions in the data-driven modeling of matter. Lode ; Multipole expansion ; Computer science ; Equivariant map ; Extension (predicate logic) ; Locality ; Scaling ; Set (abstract data type) ; Theoretical computer science ; Range (aeronautics) ; Simple (philosophy) ; Interpretation (philosophy) ; Statistical physics ; Algorithm ; Topology (electrical circuits) ; Theoretical physics ; Physics ; Mathematics ; Pure mathematics ; Programming language ; Geometry ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Topic Modeling
Authors: Anwen Liu | Tongqi Wen | Jian Han | David J. Srolovitz
Source: arXiv (Cornell University) (preprint, 2023-06-19)
Field: Materials Science — Materials Chemistry

Finite-temperature screw dislocation core structures and dynamics in $α$-titanium A multiscale approach based on molecular dynamics (MD) and kinetic Monte Carlo (kMC) methods is developed to simulate the dynamics of an $\langle \mathbf{a} \rangle$ screw dislocation in $α$-Ti. The free energy barriers for the core dissociation transitions and Peierls barriers for dislocation glide as a function of temperature are extracted from the MD simulations (based on Machine Learning interatomic potentials and optimization); these form the input to kMC simulations. Random walk dislocation trajectories from kMC agree well with those predicted by MD. On some planes, dislocations move via a locking-unlocking mechanism. Surprisingly, some dislocations glide in directions that are not parallel with the core dissociation direction. The MD/kMC multiscale method proposed is applicable to dislocation motion in simple and complex materials (not only screw dislocations in Ti) as a function of temperature and stress state. Dislocation ; Molecular dynamics ; Peierls stress ; Materials science ; Kinetic Monte Carlo ; Dissociation (chemistry) ; Interatomic potential ; Monte Carlo method ; Statistical physics ; Core (optical fiber) ; Condensed matter physics ; Kinetic energy ; Dislocation creep ; Physics ; Classical mechanics ; Physical chemistry ; Chemistry ; Mathematics Microstructure and mechanical properties ; Titanium Alloys Microstructure and Properties ; Advanced Materials Characterization Techniques
Authors: Anders Ø. Madsen | Anders S. Larsen | Toms Rekis
Source: ChemRxiv (preprint, 2023-11-08)
Field: Materials Science — Materials Chemistry

PhAI: A deep learning approach to solve the crystallographic phase problem For more than 100 years, X-ray crystallography has provided a unique view on the three-dimensional structure of atoms and molecules in crystals. However, to determine even the simplest structures now and a hundred years ago, one needs to overcome a mathematical hurdle for which the solution is not known even to this day. To reconstruct the 3-dimensional electron density map, from which the structure can be inferred, the complex structure factors F = |F| exp(iφ) of a sufficiently large number of diffracted reflections must be known. In a conventional diffraction experiment, only the amplitudes |F| are obtained, while the phases φ are lost. This is the crystallographic phase problem. Seventy years of research has established successful ab initio phasing methods such as direct methods and charge flipping. However, these methods are limited to atomic- resolution data, complicating structure determination from weakly-scattering crystals. Here, we show that a neural network can solve the crystallographic phase problem at a resolution of only 2 Å. We have developed an approach to generate millions of artificial structures and respective diffraction data for training of a neural network. We demonstrate that ab initio phasing based on this neural network is possible using 10 % to 20 % of the data needed for present-day methods, breaking the paradigm that atomic resolution is necessary for ab initio structure solution. The current neural network works in common centrosymmetric space groups and for modest unit cell dimensions, and suggests that neural networks can be used to solve the phase problem in the general case. This approach will enable structure solution for weakly-scattering crystals such as metal-organic frameworks or nanometer-sized crystals investigated using electron diffraction. Phaser ; Phase problem ; Ab initio ; Artificial neural network ; Diffraction ; Phase (matter) ; Resolution (logic) ; Computer science ; Physics ; Crystallography ; Algorithm ; Artificial intelligence ; Chemistry ; Quantum mechanics ; Optics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Joseph Thacker | David J. Bray | Patrick B. Warren | R. L. Anderson
Source: ChemRxiv (preprint, 2023-02-20)
Field: Chemistry — Organic Chemistry

Can machine learning predict the phase behavior of surfactants? We explore the prediction of surfactant phase behavior using state-of-the-art machine learning methods, using a data set for twenty-three non-ionic surfactants in line with Bell, Phil. Trans. R. Soc. A 2016, 374, 20150137. Most machine learning classifiers we tested are capable of filling in missing data in a partially complete data set. However, strong data bias and a lack of chemical space information generally lead to poorer results entire de novo phase diagram prediction. Although some machine learn- ing classifiers perform better than others, these observations are largely robust to the particular choice of algorithm. Finally we explore how de novo phase diagram prediction can be improved by the inclusion of observations from state points sampled by analogy to commonly used experimental protocols. Our results indicate what factors should be considered when preparing for machine learning prediction of surfactant phase behavior in future studies. Machine learning ; Artificial intelligence ; Computer science ; Set (abstract data type) ; Phase diagram ; Data set ; Phase (matter) ; Analogy ; Algorithm ; Chemistry Surfactants and Colloidal Systems ; Machine Learning in Materials Science
Authors: Deqing Xue | Bueryi Shen | G.J. Zhang | Ruihao Yuan
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Multi-objective optimization for shape memory alloys with large recoverable strain and low hysteresis Hysteresis ; Shape-memory alloy ; Strain (injury) ; Materials science ; Composite material ; Condensed matter physics ; Physics ; Biology Shape Memory Alloy Transformations ; High Entropy Alloys Studies ; Titanium Alloys Microstructure and Properties
Authors: Zhang, Grace
Source: Digital Access to Scholarship at Harvard (DASH) (Harvard University) (article, 2023-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Fluctuations, disorder, and geometry in soft matter Soft matter refers to a broad category of materials, united by the characteristic that their shape or internal structure is easily deformable. Such materials are typically made up of individual constituents on the mesoscopic length scale, with collective excitation energies on the room temperature thermal energy scale. These systems make up the world around us (e.g. a crushed soda can, the liquid crystal display of a TV screen, a spoonful of mayonnaise, or the cells inside our bodies) and can display a variety of interesting phenomena due to the interplay of their geometry, thermodynamic fluctuations, and environmental disorder. This thesis studies a breadth of soft matter systems, namely (1) the effect of biophysical stochasticity on the dynamical states of neural networks, (2) the consequences of thermal fluctuations on defect assemblies in crystalline solids, (3) the implication of geometry-induced inhomogeneity on phonon localization in colloidal crystals, and (4) the influence of surface curvature and boundary conditions on the ground state of liquid crystals. These studies demonstrate the non-negligible and intriguing effects that fluctuations and disorder can have on the geometrical properties of soft materials. Mesoscopic physics ; Soft matter ; Curvature ; Thermal ; State of matter ; Thermal fluctuations ; Boundary (topology) ; Phonon ; Liquid crystal Advanced Physical and Chemical Molecular Interactions ; Chemical and Physical Properties of Materials ; Theoretical and Computational Physics
Authors: Xuan Dong | Shenghao Wang | Chao Liu | Shanshan Dong | Qing Huang
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Rapid Detection of Residual Chlorpyrifos and Pyrimethanil on Fruit Surface by Surface-Enhanced Raman Spectroscopy Integrated with Deep Learning Approach Residual ; Pyrimethanil ; Surface (topology) ; Chlorpyrifos ; Raman spectroscopy ; Artificial intelligence ; Materials science ; Environmental science ; Analytical Chemistry (journal) ; Computer science ; Chemistry ; Environmental chemistry ; Horticulture ; Mathematics ; Physics ; Optics ; Biology ; Algorithm ; Agronomy ; Pesticide Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis