jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Pengjie Shi | Shizhe Feng | Zhiping Xu
Source: arXiv (Cornell University) (preprint, 2023-07-30)
Field: Materials Science — Materials Chemistry

Non-Equilibrium Nature of Fracture Determines the Crack Paths A high-fidelity neural network-based force field, NN-F$^{3}$, is developed to cover the strain states up to material failure and the non-equilibrium, intermediate nature of fracture. Simulations of fracture in 2D crystals using NN-F$^{3}$ reveal spatial complexities from lattice-scale kinks to sample-scale patterns. We find that the fracture resistance cannot be quantified by the energy densities of relaxed edges as in the literature. Instead, the fracture patterns, critical stress intensity factors at the kinks, and energy densities of edges in the intermediate, unrelaxed states offer reasonable measures for the fracture toughness and its anisotropy. Anisotropy ; Fracture (geology) ; Fracture toughness ; Fracture mechanics ; Lattice (music) ; Materials science ; Stress field ; Stress intensity factor ; Statistical physics ; Structural engineering ; Physics ; Composite material ; Engineering ; Optics Machine Learning in Materials Science ; Non-Destructive Testing Techniques ; Force Microscopy Techniques and Applications
Authors: Qiangqiang Gu | Zhanghao Zhouyin | Shishir Kumar Pandey | Peng Zhang | Linfeng Zhang | E Weinan
Source: arXiv (Cornell University) (preprint, 2023-07-10)
Field: Materials Science — Materials Chemistry

Deep learning tight-binding approach for large-scale electronic simulations at finite temperatures with $ab$ $initio$ accuracy Simulating electronic behavior in materials and devices with realistic large system sizes remains a formidable task within the $ab$ $initio$ framework due to its computational intensity. Here we show DeePTB, an efficient deep learning-based tight-binding approach with $ab$ $initio$ accuracy to address this issue. By training on structural data and corresponding $ab$ $initio$ eigenvalues, the DeePTB model can efficiently predict tight-binding Hamiltonians for unseen structures, enabling efficient simulations of large-size systems under external perturbations such as finite temperatures and strain. This capability is vital for semiconductor band gap engineering and materials design. When combined with molecular dynamics, DeePTB facilitates efficient and accurate finite-temperature simulations of both atomic and electronic behavior simultaneously. This is demonstrated by computing the temperature-dependent electronic properties of a gallium phosphide system with $10^6$ atoms. The availability of DeePTB bridges the gap between accuracy and scalability in electronic simulations, potentially advancing materials science and related fields by enabling large-scale electronic structure calculations. Ab initio ; Tight binding ; Computer science ; Eigenvalues and eigenvectors ; Task (project management) ; Band gap ; Ab initio quantum chemistry methods ; Electronic structure ; Statistical physics ; Physics ; Computational chemistry ; Chemistry ; Quantum mechanics ; Engineering ; Molecule Machine Learning in Materials Science ; Advancements in Semiconductor Devices and Circuit Design ; Semiconductor materials and devices
Authors: Yan Sun | Chengrong Wu | Feng Wang | Ruihao Bi | Yong‐Bin Zhuang | Shuai Liu | Mingshu Chen | Kelvin H. L. Zhang | Jiawei Yan | Bing‐Wei Mao | Zhong‐Qun Tian | Jun Cheng
Source: ChemRxiv (preprint, 2023-05-19)
Field: Chemistry — Electrochemistry

Step-induced double-row pattern of interfacial water on rutile TiO2(110) at electrochemical conditions Metal oxides are promising (photo)electrocatalysts for sustainable energy technologies due to their good activity and abundant resources. Their applications such as photocatalytic water splitting predominantly involve aqueous inter- faces at electrochemical conditions, but in situ probing oxide-water interfaces is proven to be extremely challenging. Here, we present an electrochemical scanning tunneling microscopy (EC-STM) study on the rutile TiO2(110)-water interface, and by tuning surface redox chemistry with careful potential control we are able to obtain high quality images of interfacial structures with atomic details. It is interesting to find that the interfacial water exhibits an unexpected double-row pattern that has never been observed. This finding is confirmed by performing a large scale simulation of a stepped interface model enabled by machine learning accelerated molecular dynamics (MLMD) at ab initio accuracy. Furthermore, we show that this pattern is induced by the steps present on the surface, which can propagate across the terraces by interfacial hydrogen bonds. Our work demonstrates that by combining EC-STM and MLMD we can obtain new atomic details of interfacial structures that are valuable to understand the activity of oxides at realistic conditions. Rutile ; Electrochemistry ; Scanning tunneling microscope ; Oxide ; Materials science ; Water splitting ; Atomic units ; Aqueous solution ; Nanotechnology ; Chemical physics ; Photocatalysis ; Chemical engineering ; Electrode ; Chemistry ; Physical chemistry ; Catalysis ; Metallurgy Electrochemical Analysis and Applications ; Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides
Authors: Phillip E. Pope | David Jacobs
Source: arXiv (Cornell University) (preprint, 2023-10-28)
Field: Materials Science — Materials Chemistry

Towards Combinatorial Generalization for Catalysts: A Kohn-Sham Charge-Density Approach The Kohn-Sham equations underlie many important applications such as the discovery of new catalysts. Recent machine learning work on catalyst modeling has focused on prediction of the energy, but has so far not yet demonstrated significant out-of-distribution generalization. Here we investigate another approach based on the pointwise learning of the Kohn-Sham charge-density. On a new dataset of bulk catalysts with charge densities, we show density models can generalize to new structures with combinations of elements not seen at train time, a form of combinatorial generalization. We show that over 80% of binary and ternary test cases achieve faster convergence than standard baselines in Density Functional Theory, amounting to an average reduction of 13% in the number of iterations required to reach convergence, which may be of independent interest. Our results suggest that density learning is a viable alternative, trading greater inference costs for a step towards combinatorial generalization, a key property for applications. Generalization ; Ternary operation ; Density functional theory ; Convergence (economics) ; Kohn–Sham equations ; Inference ; Pointwise ; Charge density ; Binary number ; Charge (physics) ; Applied mathematics ; Computer science ; Work (physics) ; Mathematics ; Statistical physics ; Materials science ; Artificial intelligence ; Computational chemistry ; Chemistry ; Physics ; Quantum mechanics ; Mathematical analysis ; Arithmetic Machine Learning in Materials Science ; Topic Modeling ; Theoretical and Computational Physics
Authors: Raul Ortega Ochoa | Bardi Benediktsson | Peter Bjørn Jørgensen | Arghya Bhowmik
Source: ChemRxiv (preprint, 2023-06-14)
Field: Materials Science — Materials Chemistry

Introducing Materials Funnel 2.0 - hierarchical search ininfinite chemical space Inventing new ways of exploring the materials phase space accelerates functional materials discovery. For new breakthroughs materials, faster exploration of bigger phase spaces is a key goal. High throughput computational screening (HTCS) is widely used to quickly search for materials with the right functional property. In this article we redefine HTCS methods to combine deep learning and physics based model to explore much large chemical spaces than possible by pure physics driven HTCS. Deep generative models are used to autonomously create materials libraries with high likelihood of desired properties, inverting the design paradigm. Additionally machine learnt surrogates enable next layer of screening to prune the set further such as high quality quantum mechanical simulations can be performed. With organic photovoltaic (OPV) molecules as test bench, we show the power of this redesigned HTCS approach in inverse design OPV molecules with very limited computational expense. Chemical space ; Set (abstract data type) ; Space (punctuation) ; Computer science ; Phase space ; Property (philosophy) ; Nanotechnology ; Artificial intelligence ; Materials science ; Physics ; Drug discovery ; Chemistry ; Quantum mechanics Machine Learning in Materials Science
Authors: Gihan Panapitiya | Emily Saldanha
Source: ChemRxiv (preprint, 2023-02-01)
Field: Materials Science — Materials Chemistry

Outlier-Based Domain of ApplicabilityIdentification for Materials PropertyPrediction Models Machine learning models have been widely applied for material property prediction. However, practical application of these models can be hindered by a lack of information about how well they will perform on previously unseen types of materials. Because machine learning model predictions depend on the quality of the available training data, different domains of the material feature space are predicted with different accuracy levels by such models. The ability to identify such domains enables the ability to find the confidence level of each prediction, to determine when and how the model should be employed depending on the prediction accuracy requirements of different tasks, and to improve the model for domains with high errors. In this work, we propose a method to find domains of applicability using a large feature space and also introduce analysis techniques to gain more insight into the detected domains and subdomains. Computer science ; Outlier ; Feature (linguistics) ; Domain (mathematical analysis) ; Machine learning ; Artificial intelligence ; Property (philosophy) ; Space (punctuation) ; Quality (philosophy) ; Anomaly detection ; Data mining ; Feature vector ; Mathematics Machine Learning in Materials Science ; Non-Destructive Testing Techniques ; Industrial Vision Systems and Defect Detection
Authors: Ratchaneekorn Thamvichai | Daniel Pike | Joel A. Greenberg | Amit Ashok
Source:  (article, 2023-06-14)
Field: Materials Science — Materials Chemistry

Classifiers for material discrimination with dual modality x-ray transmission and coherent x-ray diffraction system X-ray coherent diffraction-based measurements can provide more specific material information that is complementary to transmission-based material information. With increasing capability of the X-ray coherent diffraction-based systems and recent development of dual modality of X-ray transmission-based and diffraction-based systems, there is a significant potential for improving the overall system threat detection performance for material discrimination. Dual modality systems can yield higher detection probability (Pd) while lowering the probability of false alarm (Pfa), relative to the transmission modality. In this work, we analyze the material discrimination performance for two different machine learning classifiers: support vector machines (SVM) and neural networks (NN), using both simulation and experimental data obtained with a dual-modality X-ray system. Using simulation studies, we demonstrate significant improvement in material discrimination performance afforded by additional complementary information by coherent diffraction for a variety of materials. We further validate these improvements using an experimental dataset collected using real-world objects and materials. Modality (human–computer interaction) ; Diffraction ; Transmission (telecommunications) ; Computer science ; Support vector machine ; Artificial intelligence ; Dual (grammatical number) ; Artificial neural network ; Optics ; Physics ; Telecommunications X-ray Diffraction in Crystallography ; Advanced X-ray and CT Imaging ; Advanced X-ray Imaging Techniques
Authors: Gihan Panapitiya | Emily Saldanha
Source: arXiv (Cornell University) (preprint, 2023-01-17)
Field: Materials Science — Materials Chemistry

Outlier-Based Domain of Applicability Identification for Materials Property Prediction Models Machine learning models have been widely applied for material property prediction. However, practical application of these models can be hindered by a lack of information about how well they will perform on previously unseen types of materials. Because machine learning model predictions depend on the quality of the available training data, different domains of the material feature space are predicted with different accuracy levels by such models. The ability to identify such domains enables the ability to find the confidence level of each prediction, to determine when and how the model should be employed depending on the prediction accuracy requirements of different tasks, and to improve the model for domains with high errors. In this work, we propose a method to find domains of applicability using a large feature space and also introduce analysis techniques to gain more insight into the detected domains and subdomains. Computer science ; Outlier ; Property (philosophy) ; Identification (biology) ; Feature (linguistics) ; Machine learning ; Domain (mathematical analysis) ; Artificial intelligence ; Data mining ; Predictive modelling ; Space (punctuation) ; Feature vector ; Anomaly detection ; Mathematics Machine Learning in Materials Science ; Non-Destructive Testing Techniques ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: James L. McDonagh | Stamatia Zavitsanou | Alexander Harrison | Dimitry Zubarev | Benjamin H. Wunsch | Theordore van Kessel | Flaviu Cipcigan
Source: ChemRxiv (preprint, 2023-05-18)
Field: Materials Science — Materials Chemistry

CHEMICAL SPACE ANALYSIS AND PROPERTY PREDICTION FOR CARBON CAPTURE AMINE MOLECULES Carbon capture and storage is part of the roadmap towards net zero for many countries around the world, since emissions from existing infrastructure are close to estimated carbon budgets. To address this problem, currently 87 carbon capture projects are proposed worldwide in the next 10 years. A major class of commercial carbon capture technology involves capture systems using solvents. Commonly carbon capture solvents feature blends of amines and water. Whilst these blends have proved valuable there is an increasing need to identify new candidate molecules which are more efficient and improve performance. Systematic approaches to improve on the current technology are now needed with increasing urgency to expedite the introduction of cutting edge carbon capture methods. Here, we present a chemical space analysis of amines and carbon capture usage. We proceed to show a framework for computational screening relevant to carbon capture solvents. We demonstrate the use of cloud computing, novel molecular representations and machine learning to screen potential candidates. We show the utility of machine learning in this field for high throughput virtual screening with an exemplar application to absorption capacity classification. Additionally, we highlight the need for improved data awareness and accessibility to enable this field to advance at a pace commensurate to its global importance. Our research brings together multiple methods and domains of expertise to accelerate the discovery of carbon capture solvents. Chemical space ; Pace ; Carbon capture and storage (timeline) ; Computer science ; Carbon fibers ; Greenhouse gas ; Space (punctuation) ; Field (mathematics) ; Data science ; Chemistry ; Drug discovery ; Climate change ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Sergei Manzhos | Johann Lüder | Manabu Ihara
Source: arXiv (Cornell University) (preprint, 2023-09-07)
Field: Materials Science — Materials Chemistry

Machine learning of kinetic energy densities with target and feature averaging: better results with fewer training data Machine learning of kinetic energy functionals (KEF), in particular kinetic energy density (KED) functionals, has recently attracted attention as a promising way to construct KEFs for orbital-free density functional theory (OF-DFT). Neural networks (NN) and kernel methods including Gaussian process regression (GPR) have been used to learn Kohn-Sham (KS) KED from density-based descriptors derived from KS DFT calculations. The descriptors are typically expressed as functions of different powers and derivatives of the electron density. This can generate large and extremely unevenly distributed datasets, which complicates effective application of machine learning techniques. Very uneven data distributions require many training data points, can cause overfitting, and ultimately lower the quality of a ML KED model. We show that one can produce more accurate ML models from fewer data by working with partially averaged density-dependent variables and KED. Averaging palliates the issue of very uneven data distributions and associated difficulties of sampling, while retaining enough spatial structure necessary for working within the paradigm of KEDF. We use GPR as a function of partially spatially averaged terms of the 4th order gradient expansion and the Kohn-Sham effective potential and obtain accurate and stable (with respect to different random choices of training points) kinetic energy models for Al, Mg, and Si simultaneously from as few as 2000 samples (about 0.3% of the total KS DFT data). In particular, accuracies on the order of 1% in a measure of the quality of energy-volume dependence B' = \frac{E(V_0-ΔV)-2E(V_0)+E(V_0 + ΔV)}{\big(\frac{ΔV}{V_0}\big)^2} are obtained simultaneously for all three materials. Overfitting ; Kinetic energy ; Kriging ; Artificial intelligence ; Generalization ; Statistical physics ; Kernel density estimation ; Energy (signal processing) ; Computer science ; Function (biology) ; Artificial neural network ; Machine learning ; Mathematics ; Physics ; Statistics ; Quantum mechanics ; Mathematical analysis Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Aniruddha Dive
Source: Faculty of 1000 Research Ltd (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Referee report. For: Machine learning guided prediction of the yield strength and hardness of multi-principal element alloys [version 1; peer review: 1 approved, 1 approved with reservations] Finite element method ; Yield (engineering) ; Element (criminal law) ; Artificial neural network Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; Metal Alloys Wear and Properties
Authors: Koki Matsunoshita | Yudai Yamaguchi | Masato Hamaie | Motoki Horibe | Naoto Tanibata | Hayami Takeda | Masanobu Nakayama | Masayuki Karasuyama | Ryo Kobayashi
Source: Science and Technology of Advanced Materials Methods (article, 2023-08-31)
Field: Materials Science — Materials Chemistry

Optimization of force-field potential parameters using conditional variational autoencoder Owing to their high-speed, force-field (FF) calculations for inorganic solid-state materials using the parametric potential have been widely employed as an effective tool for high-throughput calculations such as exhaustive property evaluation of material databases and/or calculations for models containing tens of thousands of atoms or more, including amorphous and grain boundary models. However, the accuracy of such calculations strongly depends on the choice of the FF parameters. Today, compounds containing three or more elements are targeted in the research and development of functional materials, in which case the number of parameters often exceeds 10 dimensions. This significantly increases the search space, making it difficult to determine the parameters rationally. To resolve this issue, we previously reported an FF parameter determination approach based on meta-heuristics (APL Materials, 8, 081111 (2020)). In this study, we further investigated a methodology to efficiently determine the FF parameters using a conditional variational autoencoder (CVAE), a type of deep learning method, which can reduce the dimension of the input parameters and distribute them in a probabilistic manner in the latent space. As a demonstration, we applied the method to the optimization of 11 FF parameters for an argyrodite-type Li7PS6 material, which has attracted considerable attention as a solid electrolyte for all-solid-state batteries. The results confirmed that the proposed approach can generate valid FFs that are highly consistent with the results of first-principles calculations, even when using a limited set of meta-heuristics-generated FF parameters. Autoencoder ; Heuristics ; Computer science ; Parametric statistics ; Field (mathematics) ; Probabilistic logic ; Mathematical optimization ; Algorithm ; Materials science ; Mathematics ; Machine learning ; Deep learning ; Artificial intelligence ; Statistics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; X-ray Diffraction in Crystallography
Authors: Xi Zhang | Yutian Zhang | Yunpeng Wang | Yiran Li | Shixuan Du | Yuyang Zhang | Sokrates T. Pantelides
Source: arXiv (Cornell University) (preprint, 2023-09-27)
Field: Materials Science — Materials Chemistry

Structure and mechanical properties of monolayer amorphous carbon and boron nitride Amorphous materials exhibit various characteristics that are not featured by crystals and can sometimes be tuned by their degree of disorder (DOD). Here, we report results on the mechanical properties of monolayer amorphous carbon (MAC) and monolayer amorphous boron nitride (maBN) with different DOD. The pertinent structures are obtained by kinetic-Monte-Carlo (kMC) simulations using machine-learning potentials (MLP) with density-functional-theory (DFT)-level accuracy. An intuitive order parameter, namely the areal fraction Fx occupied by crystallites within the continuous random network, is proposed to describe the DOD. We find that Fx captures the essence of the DOD: Samples with the same Fx but different sizes and distributions of crystallites have virtually identical radial distributions functions as well as bond-length and bond-angle distributions. Furthermore, by simulating the fracture process with molecular dynamics, we found that the mechanical responses of MAC and maBN before fracture are solely determined by Fx and are insensitive to the sizes and specific arrangements of the crystallites. The behavior of cracks in the two materials is analyzed and found to mainly propagate in meandering paths in the CRN region and to be influenced by crystallites in distinct ways that toughen the material. The present results reveal the relation between structure and mechanical properties in amorphous monolayers and may provide a universal toughening strategy for 2D materials. Crystallite ; Boron nitride ; Monolayer ; Materials science ; Amorphous solid ; Characterization (materials science) ; Molecular dynamics ; Composite material ; Nanotechnology ; Chemical physics ; Crystallography ; Computational chemistry ; Physics ; Chemistry ; Metallurgy Graphene research and applications ; Thermal properties of materials ; Diamond and Carbon-based Materials Research
Authors: Salilaporn Nuankaew | Nattawut Boonyuen | Kanjana Thumanu | Natapol Pornputtapong
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Development of a Machine Learning Model for Systematics of Aspergillus Section Nigri Using Synchrotron Radiation-Based Fourier Transform Infrared Spectroscopy Fourier transform infrared spectroscopy ; Fourier transform ; Artificial intelligence ; Convolutional neural network ; Pattern recognition (psychology) ; Biological system ; Computer science ; Physics ; Biology ; Materials science ; Optics Spectroscopy and Chemometric Analyses