jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xingyue Ma | L. Bellaïche | Di Wu | Yu, Zhanbo | Prokhorenko, Sergei | Wen, Zheng | Zhong, Zhicheng | Iñiguez, Jorge | Bellaiche, L. | Wu, Di | Yang, Yurong
Source: arXiv (Cornell University) (preprint, 2023-07-18)
Field: Materials Science — Materials Chemistry

Active learning of effective Hamiltonian for super-large-scale atomic structures The first-principles-based effective Hamiltonian scheme provides one of the most accurate modeling technique for large-scale structures, especially for ferroelectrics. However, the parameterization of the effective Hamiltonian is complicated and can be difficult for some complex systems such as high-entropy perovskites. Here, we propose a general form of effective Hamiltonian and develop an active machine learning approach to parameterize the effective Hamiltonian based on Bayesian linear regression. The parameterization is employed in molecular dynamics simulations with the prediction of energy, forces, stress and their uncertainties at each step, which decides whether first-principles calculations are executed to retrain the parameters. Structures of BaTiO$_3$, Pb(Zr$_{0.75}$Ti$_{0.25}$)O$_3$ and (Pb,Sr)TiO$_3$ system are taken as examples to show the accuracy of this approach, as compared with conventional parametrization method and experiments. This machine learning approach provides a universal and automatic way to compute the effective Hamiltonian parameters for any considered complex systems with super-large-scale (more than $10^7$ atoms) atomic structures. Parametrization (atmospheric modeling) ; Hamiltonian (control theory) ; Hamiltonian system ; Computer science ; Complex system ; Statistical physics ; Applied mathematics ; Physics ; Mathematics ; Classical mechanics ; Mathematical optimization ; Artificial intelligence ; Quantum mechanics Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Fuel Cells and Related Materials
Authors: Owais Ahmad | Naveen Kumar | R. Mukherjee | Somnath Bhowmick
Source: arXiv (Cornell University) (preprint, 2023-05-01)
Field: Materials Science — Materials Chemistry

Accelerating microstructure modelling via machine learning: a new method combining Autoencoder and ConvLSTM Phase-field modeling is an elegant and versatile computation tool to predict microstructure evolution in materials in the mesoscale regime. However, these simulations require rigorous numerical solutions of differential equations, which are accurate but computationally expensive. To overcome this difficulty, we combine two popular machine learning techniques, autoencoder and convolutional long short-term memory (ConvLSTM), to accelerate the study of microstructural evolution without compromising the resolution of the microstructural representation. After training with phase-field generated microstructures of ten known compositions, the model can accurately predict the microstructure for the future nth frames based on previous m frames for an unknown composition. Replacing n phase-field steps with machine-learned microstructures can significantly accelerate the in silico study of microstructure evolution. Autoencoder ; Microstructure ; Representation (politics) ; Computer science ; Artificial intelligence ; Field (mathematics) ; Computation ; Phase (matter) ; Mesoscale meteorology ; Machine learning ; Deep learning ; Algorithm ; Pattern recognition (psychology) ; Materials science ; Mathematics ; Physics ; Metallurgy Solidification and crystal growth phenomena ; Magnetic Properties and Applications ; Microstructure and Mechanical Properties of Steels
Authors: Corin Wagen
Source: Front Matter (article, 2023-12-01)
Field: Materials Science — Materials Chemistry

Bitter Lessons in Chemistry <ns4:p>"And I took the little scroll from the hand of the angel and ate it. It was sweet as honey in my mouth, but when I had eaten it my stomach was made bitter." –Revelation 10:10 As machine learning becomes more and more important to chemistry, it's worth reflecting on Richard Sutton's 2019 blog post about the "bitter lesson." In this now-famous post, Sutton argues that "the biggest lesson that can be read from 70 years of AI research is that</ns4:p> Scroll ; Government (linguistics) ; George (robot) Machine Learning in Materials Science ; History and advancements in chemistry ; Various Chemistry Research Topics
Authors: ZHAO GUOBIN | Chung Yongchul G. | Chen, Yu
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2023-03-15)
Field: Materials Science — Materials Chemistry

Multiscale high-throughput computational screening of nanoporous materials for Xe/Kr adsorption separation Xe/Kr separation project: 1. GCMC isotherms and heat of adsorption 2. Machine learning 3. Ideal Vacuum Swing Adsorption 4. High fidelity VSA simulation High-Throughput, Multiscale Computational Screening of Metal–Organic Frameworks for Xe/Kr Separation with Machine-Learned Parameters Guobin Zhao, Yu Chen, and Yongchul G. Chung Industrial & Engineering Chemistry Research 2023 62 (37), 15176-15189 DOI: 10.1021/acs.iecr.3c02211 Nanoporous ; Separation (statistics) ; Adsorption ; Throughput ; Materials science ; High-throughput screening ; Chemistry ; Nanotechnology ; Physical chemistry ; Computer science ; Machine learning Catalytic Processes in Materials Science ; Mesoporous Materials and Catalysis ; Covalent Organic Framework Applications
Authors: 亜希子 池田
Source: Institutional Repositories DataBase (IRDB) (article, 2023-11-01)
Field: Materials Science — Materials Chemistry

Contributing to monozukuri manufacturing through numerical simulations : an extensive field of study that encompasses prosthetic limb defect detection, apps that recommend metallographic structures, quantum computing, and more Using the "finite element method" to investigate how objects deform I want to find defects in prosthetic limbs : using machine learning, FEM, and application experiments I want to know which structures determine a material's properties : using machine learning, FEM, and phase-field modeling New developments in numerical simulation Field (mathematics) ; Finite element method ; Computer simulation ; Quantum ; Numerical control Machine Learning in Materials Science ; Shape Memory Alloy Transformations ; Advanced Materials Characterization Techniques
Authors: Corin Wagen
Source: Front Matter (preprint, 2023-12-01)
Field: Chemistry — Physical and Theoretical Chemistry

Bitter Lessons in Chemistry <ns4:p>"And I took the little scroll from the hand of the angel and ate it. It was sweet as honey in my mouth, but when I had eaten it my stomach was made bitter." –Revelation 10:10 As machine learning becomes more and more important to chemistry, it's worth reflecting on Richard Sutton's 2019 blog post about the "bitter lesson." In this now-famous post, Sutton argues that "the biggest lesson that can be read from 70 years of AI research is that</ns4:p> Revelation ; Scroll ; Chemistry ; Engineering ; Art history ; Art ; Literature ; Mechanical engineering Various Chemistry Research Topics
Authors: Jingxi Li | Tianyi Gan | Yifan Zhao | Bijie Bai | Che‐Yung Shen | Songyu Sun | Mona Jarrahi | Aydogan Özcan
Source:  (article, 2023-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Unidirectional Imaging Using Diffractive Materials We present deep learning-optimized diffractive materials forming a broadband and polarization-insensitive unidirectional imager that permits imaging along only one direction while blocking the light transmission in the reverse direction. Broadband ; Polarization (electrochemistry) ; Optics ; Diffraction ; Blocking (statistics) ; Transmission (telecommunications) ; Materials science ; Computer science ; Optoelectronics ; Physics ; Telecommunications Metamaterials and Metasurfaces Applications ; Magneto-Optical Properties and Applications ; Photonic Crystals and Applications
Authors: Elena Gazzarrini | Rose K. Cersonsky | Marnik Bercx | Carl S. Adorf | Nicola Marzari
Source: arXiv (Cornell University) (preprint, 2023-07-27)
Field: Materials Science — Materials Chemistry

The rule of four: anomalous stoichiometries of inorganic compounds Why are materials with specific characteristics more abundant than others? This is a fundamental question in materials science and one that is traditionally difficult to tackle, given the vastness of compositional and configurational space. We highlight here the anomalous abundance of inorganic compounds whose primitive unit cell contains a number of atoms that is a multiple of four. This occurrence - named here the 'rule of four' - has to our knowledge not previously been reported or studied. Here, we first highlight the rule's existence, especially notable when restricting oneself to experimentally known compounds, and explore its possible relationship with established descriptors of crystal structures, from symmetries to energies. We then investigate this relative abundance by looking at structural descriptors, both of global (packing configurations) and local (the smooth overlap of atomic positions) nature. Contrary to intuition, the overabundance does not correlate with low-energy or high-symmetry structures; in fact, structures which obey the 'rule of four' are characterized by low symmetries and loosely packed arrangements maximizing the free volume. We are able to correlate this abundance with local structural symmetries, and visualize the results using a hybrid supervised-unsupervised machine learning method. Homogeneous space ; Intuition ; Theoretical physics ; Stoichiometry ; Relative species abundance ; Space (punctuation) ; Crystal structure prediction ; Physics ; Statistical physics ; Crystal structure ; Chemistry ; Abundance (ecology) ; Computer science ; Mathematics ; Crystallography ; Geometry ; Epistemology ; Biology ; Physical chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Mykhailo Moroz | Oleg Bovgyra
Source:  (article, 2023-09-26)
Field: Materials Science — Materials Chemistry

Optimizing Neural Network Wavefunctions Using Variational Monte Carlo with Evolution Strategies Obtaining accurate solutions to many-electron quantum systems is of critical importance, in principle, such solutions allow computing most of possible chemical and physical properties from first principles. Exact solutions require utilizing exponentially scaling algorithms, which are not practical for large systems. Neural networks provide a new approach to approximating the solution while keeping a high level of accuracy. In this work, we explore a novel method of optimizing neural network wavefunctions by employing Evolution Strategies (ES) within a Variational Monte Carlo (VMC) framework. This allows us to more robustly find solutions while keeping a small number of parameters, making it an efficient alternative to traditional methods. Wave function ; Monte Carlo method ; Artificial neural network ; Computer science ; Quantum Monte Carlo ; Scaling ; Variational Monte Carlo ; Quantum ; Statistical physics ; Mathematical optimization ; Applied mathematics ; Algorithm ; Artificial intelligence ; Mathematics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Computational Drug Discovery Methods
Authors: Qing‐Jie Li | Mahmut Nedim Cinbiz | Yin Zhang | Qi He | Geoffrey Beausoleil | Ju Li
Source: arXiv (Cornell University) (preprint, 2023-04-02)
Field: Materials Science — Materials Chemistry

Robust Deep Learning Framework for Constitutive-Relation Modeling Modeling the full-range deformation behaviors of materials under complex loading and materials conditions is a significant challenge for constitutive relations (CRs) modeling. We propose a general encoder-decoder deep learning framework that can model high-dimensional stress-strain data and complex loading histories with robustness and universal capability. The framework employs an encoder to project high-dimensional input information (e.g., loading history, loading conditions, and materials information) to a lower-dimensional hidden space and a decoder to map the hidden representation to the stress of interest. We evaluated various encoder architectures, including gated recurrent unit (GRU), GRU with attention, temporal convolutional network (TCN), and the Transformer encoder, on two complex stress-strain datasets that were designed to include a wide range of complex loading histories and loading conditions. All architectures achieved excellent test results with an RMSE below 1 MPa. Additionally, we analyzed the capability of the different architectures to make predictions on out-of-domain applications, with an uncertainty estimation based on deep ensembles. The proposed approach provides a robust alternative to empirical/semi-empirical models for CRs modeling, offering the potential for more accurate and efficient materials design and optimization. Robustness (evolution) ; Computer science ; Encoder ; Deep learning ; Artificial intelligence ; Convolutional neural network ; Algorithm ; Computer engineering Machine Learning in Materials Science ; Drilling and Well Engineering ; Orthopaedic implants and arthroplasty
Authors: JingChun Wang | Yao Wang | Rui–Xue Xu | GuanHua Chen | Zheng Xiao
Source: ChemRxiv (preprint, 2023-02-26)
Field: Materials Science — Materials Chemistry

A Semilocal Machine-Learning Correction to Density Functional Approximations Machine learning (ML) has demonstrated its potential usefulness for the development of density functional theory methods. In this work, we construct an ML model to correct the density functional approximations, which adopts semilocal descriptors of electron density and density derivative and is trained by accurate reference data of relative and absolute energies. The resulting ML-corrected functional is tested on a comprehensive dataset including various types of energetic properties. Particularly, the ML-corrected B3LYP functional achieves a substantial improvement over the original B3LYP on the prediction of total energies of atoms and molecules and atomization energies, and a marginal improvement on the prediction of ionization potentials, electron affinities, and bond dissociation energies; while it preserves the same level of accuracy for isomerization energies and reaction barrier heights. The ML-corrected functional allows for fully self-consistent-field calculation with a similar efficiency to the parent functional. This study highlights the practicality of the proposed semilocal ML correction to provide a functional that performs uniformly better than B3LYP. Density functional theory ; Hybrid functional ; Dissociation (chemistry) ; Orbital-free density functional theory ; Derivative (finance) ; Ionization ; Bond-dissociation energy ; Electron ; Chemistry ; Work (physics) ; Computational chemistry ; Physics ; Thermodynamics ; Quantum mechanics ; Physical chemistry ; Ion Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Computational Drug Discovery Methods
Authors: Wei Hu | Fan Zhang | Yajuan Feng
Source:  (article, 2023-02-17)
Field: Materials Science — Materials Chemistry

Contrastive Learning of Graph-Kernel-based Representations for Proteins Protein property prediction is to infer protein properties by finding proteins with similar sequences, structures, surface clefts, and chemical properties. Since the acquisition of protein molecular property labels usually require complex and time-consuming experiments, protein property prediction based on supervised learning requires high costs. Through contrastive learning, our proposed model is capable of self-supervised learning on graphs kernels of proteins. It utilizes a strategy of subgraph removal for data augmentation, and its contrastive estimator maximizes the coherence of subgraphs from the same graphs of proteins while minimizing the coherence of subgraphs from different graphs of proteins. Experiments show that the framework can not only transform graph kernels of proteins into new graph-kernel-based representations for proteins, but also improve the performance of graph neural network encoders on protein property prediction. Computer science ; Graph ; Kernel (algebra) ; Artificial intelligence ; Property (philosophy) ; Theoretical computer science ; Pattern recognition (psychology) ; Machine learning ; Mathematics ; Combinatorics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Jan Schuchardt | Yan Scholten | Stephan Günnemann
Source: arXiv (Cornell University) (preprint, 2023-12-05)
Field: Materials Science — Materials Chemistry

Provable Adversarial Robustness for Group Equivariant Tasks: Graphs, Point Clouds, Molecules, and More A machine learning model is traditionally considered robust if its prediction remains (almost) constant under input perturbations with small norm. However, real-world tasks like molecular property prediction or point cloud segmentation have inherent equivariances, such as rotation or permutation equivariance. In such tasks, even perturbations with large norm do not necessarily change an input's semantic content. Furthermore, there are perturbations for which a model's prediction explicitly needs to change. For the first time, we propose a sound notion of adversarial robustness that accounts for task equivariance. We then demonstrate that provable robustness can be achieved by (1) choosing a model that matches the task's equivariances (2) certifying traditional adversarial robustness. Certification methods are, however, unavailable for many models, such as those with continuous equivariances. We close this gap by developing the framework of equivariance-preserving randomized smoothing, which enables architecture-agnostic certification. We additionally derive the first architecture-specific graph edit distance certificates, i.e. sound robustness guarantees for isomorphism equivariant tasks like node classification. Overall, a sound notion of robustness is an important prerequisite for future work at the intersection of robust and geometric machine learning. Computer science ; Robustness (evolution) ; Outlier ; Artificial intelligence ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Kevin M. Roccapriore | Riccardo Torsi | Joshua A. Robinson | Sergei V. Kalinin | Maxim Ziatdinov
Source: arXiv (Cornell University) (preprint, 2023-10-19)
Field: Materials Science — Surfaces, Coatings and Films

Dynamic STEM-EELS for single atom and defect measurement during electron beam transformations On- and off-axis electron energy loss spectroscopy (EELS) is a powerful method for probing local electronic structure on single atom level. However, many materials undergo electron-beam induced transformation during the scanning transmission electron microscopy (STEM) and spectroscopy, the problem particularly acute for off-axis EELS signals. Here, we propose and operationalize the rapid object detection and action system (RODAS) for dynamic exploration of the structure-property relationships in STEM-EELS. In this approach, the electron beam is used to induce dynamic transformations creating new defect types at sufficiently small rates and avoiding complete material destruction. The deep convolutional neural networks trained via the ensemble learning iterative training (ELIT) approach are used to identify the defects as they form and perform EELS measurements only at specific defect types. Overall, in this case the EEL spectra are collected only at predefined objects of interest, avoiding measurements on the ideal regions or holes. We note that this approach can be extended to identify new defect classes as they appear, allowing for efficient collection of structure-property relationship data via balanced sampling over defect types. Scanning transmission electron microscopy ; Property (philosophy) ; Atom (system on chip) ; Electron energy loss spectroscopy ; Electron ; Computer science ; Materials science ; Atomic physics ; Physics ; Transmission electron microscopy ; Nanotechnology ; Embedded system Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Industrial Vision Systems and Defect Detection
Authors: Amirkianoosh Kiani | Kavian Khosravinia
Source: Proceedings of the International Conference of Theoretical and Applied Nanoscience and Nanotechnology (article, 2023-06-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Cracking the Code to Electrode-Specific Degradation: Insights from Data-Driven Approaches in ULPING Fabricated Pseudocapacitor To advance research in supercapacitors, it is crucial to develop a cost-effective manufacturing process for pseudocapacitor electrodes that incorporates binder-free and green synthesis methods, along with a single-step fabrication approach. The proposed method, Ultra-Short Laser Pulses for In Situ Nanostructure Generation (ULPING), aims to produce efficient and sustainable pseudocapacitor electrodes on transition metal (Titanium-Ti) without the use of binders or carbon. The research paper focuses on investigating the electrochemical performance of laser-fabricated pseudocapacitor electrodes. The study utilizes the Random Forest (RF) machine learning (ML) technique to establish a theoretical connection between laser parameters and the electrochemical behavior of the pseudocapacitors. The findings highlight the potential of ULPING and ML algorithms in advancing the development of optimal electrodes for pseudocapacitors. Pseudocapacitor ; Degradation (telecommunications) ; Computer science ; Cracking ; Code (set theory) ; Electrode ; Materials science ; Chemistry ; Programming language ; Capacitance ; Composite material ; Telecommunications ; Supercapacitor Supercapacitor Materials and Fabrication ; Fuel Cells and Related Materials ; Electric and Hybrid Vehicle Technologies
Authors: Xiandong Zou | Xiangyu Zhao | Píetro Lió | Yiren Zhao
Source: arXiv (Cornell University) (preprint, 2023-08-23)
Field: Materials Science — Materials Chemistry

Will More Expressive Graph Neural Networks do Better on Generative Tasks? Graph generation poses a significant challenge as it involves predicting a complete graph with multiple nodes and edges based on simply a given label. This task also carries fundamental importance to numerous real-world applications, including de-novo drug and molecular design. In recent years, several successful methods have emerged in the field of graph generation. However, these approaches suffer from two significant shortcomings: (1) the underlying Graph Neural Network (GNN) architectures used in these methods are often underexplored; and (2) these methods are often evaluated on only a limited number of metrics. To fill this gap, we investigate the expressiveness of GNNs under the context of the molecular graph generation task, by replacing the underlying GNNs of graph generative models with more expressive GNNs. Specifically, we analyse the performance of six GNNs in two different generative frameworks -- autoregressive generation models, such as GCPN and GraphAF, and one-shot generation models, such as GraphEBM -- on six different molecular generative objectives on the ZINC-250k dataset. Through our extensive experiments, we demonstrate that advanced GNNs can indeed improve the performance of GCPN, GraphAF, and GraphEBM on molecular generation tasks, but GNN expressiveness is not a necessary condition for a good GNN-based generative model. Moreover, we show that GCPN and GraphAF with advanced GNNs can achieve state-of-the-art results across 17 other non-GNN-based graph generative approaches, such as variational autoencoders and Bayesian optimisation models, on the proposed molecular generative objectives (DRD2, Median1, Median2), which are important metrics for de-novo molecular design. Computer science ; Generative grammar ; Graph ; Machine learning ; Artificial intelligence ; Generative model ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics