jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Emil Annevelink | Venkatasubramanian Viswanathan
Source: arXiv (Cornell University) (preprint, 2023-08-29)
Field: Materials Science — Materials Chemistry

Statistical methods for resolving poor uncertainty quantification in machine learning interatomic potentials Machine learning interatomic potentials (MLIPs) are promising surrogates for quantum mechanics evaluations in ab-initio molecular dynamics simulations due to their ability to reproduce the energy and force landscape within chemical accuracy at four orders of magnitude less cost. While developing uncertainty quantification (UQ) tools for MLIPs is critical to build production MLIP datasets using active learning, only limited progress has been made and the most robust method, ensembling, still shows low correlation between high error and high uncertainty predictions. Here we develop a rigorous method rooted in statistics for determining an error cutoff that distinguishes regions of high and low UQ performance. The statistical cutoff illuminates that a main cause of the poor UQ performance is due to the machine learning model already describing the entire dataset and not having any datapoints with error greater than the statistical error distribution. Second, we extend the statistical analysis to create an interpretable connection between the error and uncertainty distributions to predict an uncertainty cutoff separating high and low errors. We showcase the statistical cutoff in active learning benchmarks on two datasets of varying chemical complexity for three common UQ methods: ensembling, sparse Gaussian processes, and latent distance metrics and compare them to the true error and random sampling, showing that the statistical cutoff is generalizable to a variety of different UQ methods and protocols and performs similarly to using the true error. Importantly, we conclude that utilizing this uncertainty cutoff enables using significantly lower cost uncertainty quantification tools such as sparse gaussian processes and latent distances compared to ensembling approaches for generating MLIP datasets at a fraction of the cost. Cutoff ; Gaussian ; Computer science ; Uncertainty quantification ; Gaussian process ; Machine learning ; Artificial intelligence ; Algorithm ; Physics Machine Learning in Materials Science ; Mass Spectrometry Techniques and Applications ; Protein Structure and Dynamics
Authors: Hao Wu | Frank Noé
Source: arXiv (Cornell University) (preprint, 2023-09-11)
Field: Materials Science — Materials Chemistry

Reaction coordinate flows for model reduction of molecular kinetics In this work, we introduce a flow based machine learning approach, called reaction coordinate (RC) flow, for discovery of low-dimensional kinetic models of molecular systems. The RC flow utilizes a normalizing flow to design the coordinate transformation and a Brownian dynamics model to approximate the kinetics of RC, where all model parameters can be estimated in a data-driven manner. In contrast to existing model reduction methods for molecular kinetics, RC flow offers a trainable and tractable model of reduced kinetics in continuous time and space due to the invertibility of the normalizing flow. Furthermore, the Brownian dynamics-based reduced kinetic model investigated in this work yields a readily discernible representation of metastable states within the phase space of the molecular system. Numerical experiments demonstrate how effectively the proposed method discovers interpretable and accurate low-dimensional representations of given full-state kinetics from simulations. Metastability ; Kinetics ; Brownian dynamics ; Flow (mathematics) ; Representation (politics) ; Reaction coordinate ; Work (physics) ; Reduction (mathematics) ; Coordinate system ; Kinetic energy ; Statistical physics ; Phase space ; Coordinate space ; Thermodynamics ; Biological system ; Brownian motion ; Mathematics ; Physics ; Chemistry ; Mechanics ; Computational chemistry ; Classical mechanics ; Geometry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Fuel Cells and Related Materials
Authors: Jie Chen | Yiyan Song | Shu‐Zong Li | Zhi-Xiong Que | WeiBing ZHANG
Source: Scientia Sinica Technologica (article, 2023-05-29)
Field: Materials Science — Materials Chemistry

Machine-learning-based prediction of cubic perovskite formation energy and magnetism <p indent="0mm">Perovskite materials have garnered considerable attention due to their excellent properties. Thus, the accurate prediction of their physical properties is paramount. In this study, we developed an element attribute matrix descriptor via element attribute addition or phase division to improve the prediction efficiency of formation energy and magnetism of cubic perovskite. For this purpose, we trained three machine-learning models, namely extra tree, gradient boosting, and multi-layer perceptron, based on 18928 cubic perovskite data. Subsequent evaluation was conducted to evaluate the predictive efficiency of formation energy, volume-normalized magnetic moment, and magnetic classification. The results revealed that the efficiency of the developed descriptor was far higher than that of element attribute statistics and force-field inspired descriptors. The <italic>R</italic><sup>2</sup> fractions for the prediction of formation energy and magnetization were found to be 97% and 80%, respectively. In addition, the AUC of magnetic/non-magnetic classification reached 93%, with the accuracy exceeding 90%. Thus, this paper provides an important reference for the prediction of perovskite materials based on machine learning by constructing a simple and effective material descriptor. Magnetism ; Perovskite (structure) ; Materials science ; Energy (signal processing) ; Condensed matter physics ; Crystallography ; Physics ; Chemistry ; Quantum mechanics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; X-ray Diffraction in Crystallography
Authors: Nathaniel Tomczak | Sanmukh R. Kuppannagari
Source:  (article, 2023-09-25)
Field: Materials Science — Materials Chemistry

Automated Indexing Of TEM Diffraction Patterns Using Machine Learning Indexing Transmission Electron Microscopy (TEM) diffraction patterns is a critical step in materials characterization. Despite the manually intensive indexing process, work related to Machine Learning (ML) in its space is sparse. We present an evaluation of current state-of-the-art classification models and a Convolutional Neural Network (CNN), found through a Neural Architecture Search (NAS), in the TEM diffraction domain. Both convolution and transformer-based architectures were considered. Our NAS model achieved the greatest top-1 accuracy of 77.03% and F1 score of 0.751. The convolution-based architectures performed better, with EfficientNet-B3 achieving the highest average accuracy of 71.82% and tying the NAS model with the largest average F1 score of 0.686. These results can be used to guide further research into the better classification and creation of TEM diffraction data. Computer science ; Convolutional neural network ; Convolution (computer science) ; Diffraction ; Search engine indexing ; Artificial intelligence ; Feature extraction ; Pattern recognition (psychology) ; Artificial neural network ; Machine learning ; Optics ; Physics Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; X-ray Diffraction in Crystallography
Authors: Pinaki Saha | Minh Tho Nguyen
Source: RSC Advances (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Electron density mapping of boron clusters <i>via</i> convolutional neural networks to augment structure prediction algorithms Determination and prediction of atomic cluster structures is an important endeavor in the field of nanoclusters and thereby in materials research. To a large extent the fundamental properties of a nanocluster are mainly governed by its molecular structure. Traditionally, structure elucidation is achieved using quantum mechanics (QM) based calculations that are usually tedious and time consuming for large nanoclusters. Various structural prediction algorithms have been reported in the literature (CALYPSO, USPEX). Although they tend to accelerate the structure exploration, they still require the aid of QM based calculations for structure evaluation. This makes the structure prediction process quite a computationally expensive affair. In this paper, we report on the creation of a convolutional neural network model, which can give relatively accurate energies for the ground state of nanoclusters from the promolecule density on the fly and could thereby be utilized for aiding structure prediction algorithms. We tested our model on dataset consisting of pure boron nanoclusters of varying sizes. Nanoclusters ; Boron ; Convolutional neural network ; Algorithm ; Augment ; Computer science ; Electron density ; Materials science ; Electron ; Artificial intelligence ; Chemistry ; Pattern recognition (psychology) ; Nanotechnology ; Physics ; Organic chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Boron and Carbon Nanomaterials Research
Authors: Jing Tang | Zifan Wang | Qishuo Tan | Jun Cao | Xi Ling
Source: Journal of Raman Spectroscopy (article, 2023-07-27)
Field: Materials Science — Materials Chemistry

Determining the twist angle of stacked MoS<sub>2</sub> layers using machine learning‐assisted low‐frequency interlayer Raman fingerprints Abstract The investigation of twisted stacked few‐layer MoS 2 has revealed novel electronic, optical, and vibrational properties over an extended period. For the successful integration of twisted stacked few‐layer MoS 2 into a wide range of applications, it is crucial to employ a noninvasive, versatile technique for characterizing the layered architecture of these complex structures. In this work, we introduce a machine learning‐assisted low‐frequency Raman spectroscopy method to characterize the twist angle of few‐layer stacked MoS 2 samples. A feedforward neural network (FNN) is utilized to analyze the low‐frequency breathing mode as a function of the twist angle. Moreover, using finite difference method (FDM) and density functional theory (DFT) calculations, we show that the low‐frequency Raman spectra of MoS 2 are mainly influenced by the effect of the nearest and second nearest layers. A new improved linear chain model (TA‐LCM) with taking the twist angle into the consideration is developed to understand the interlayer breathing modes of stacked few‐layer MoS 2 . This approach can be extended to other 2D materials systems and provides an intelligent way to investigate naturally stacked and twisted interlayer interactions. Raman spectroscopy ; Materials science ; Twist ; Layer (electronics) ; Density functional theory ; Optoelectronics ; Artificial neural network ; Low frequency ; Computer science ; Nanotechnology ; Optics ; Chemistry ; Physics ; Computational chemistry ; Artificial intelligence ; Telecommunications 2D Materials and Applications ; Graphene research and applications ; Gas Sensing Nanomaterials and Sensors
Authors: Cecilia Clementi | Nicholas E. Charron | Félix Musil | Andrea Guljas | Yaoyi Chen | Klara Bonneau | Aldo Pasos-Trejo | Jacopo Venturin | Daria Gusew | Iryna Zaporozhets | Andreas Krämer | Clark Templeton | Atharva Kelkar | Aleksander E. P. Durumeric | Simon Olsson | Adrià Pérez | Maciej Majewski | Brooke E. Husic | Ankit Patel | Gianni De Fabritiis | Frank Noé
Source: Research Square (preprint, 2023-11-19)
Field: Materials Science — Materials Chemistry

Navigating protein landscapes with a machine-learned transferable coarse-grained model Parametrization (atmospheric modeling) ; Transferability ; Computer science ; Molecular dynamics ; Metastability ; Set (abstract data type) ; Force field (fiction) ; Atom (system on chip) ; Artificial intelligence ; Field (mathematics) ; Machine learning ; Algorithm ; Physics ; Chemistry ; Computational chemistry ; Mathematics ; Parallel computing Machine Learning in Materials Science ; Protein Structure and Dynamics ; Block Copolymer Self-Assembly
Authors: Hung‐Hsuan Lin | Chun-I Wang | Chou‐Hsun Yang | Muhammad Khari Secario | Chao‐Ping Hsu
Source: The Journal of Physical Chemistry A (article, 2023-12-29)
Field: Materials Science — Materials Chemistry

Two-Step Machine Learning Approach for Charge-Transfer Coupling with Structurally Diverse Data Electronic coupling is important in determining charge-transfer rates and dynamics. Coupling strength is sensitive to both intermolecular, e.g., orientation or distance, and intramolecular degrees of freedom. Hence, it is challenging to build an accurate machine learning model to predict electronic coupling of molecular pairs, especially for those derived from the amorphous phase, for which intermolecular configurations are much more diverse than those derived from crystals. In this work, we devise a new prediction algorithm that employs two consecutive KRR models. The first model predicts molecular orbitals (MOs) from structural variation for each fragment, and coupling is further predicted by using the overlap integral included in a second model. With our two-step procedure, we achieved mean absolute errors of 0.27 meV for an ethylene dimer and 1.99 meV for a naphthalene pair, much improved accuracy amounting to 14-fold and 3-fold error reductions, respectively. In addition, MOs from the first model can also be the starting point to obtain other quantum chemical properties from atomistic structures. This approach is also compatible with a MO predictor with sufficient accuracy. Intermolecular force ; Intramolecular force ; Coupling (piping) ; Atomic orbital ; Charge (physics) ; Orientation (vector space) ; Dimer ; Molecular dynamics ; Materials science ; Chemistry ; Molecular physics ; Physics ; Computational chemistry ; Computer science ; Chemical physics ; Molecule ; Nuclear magnetic resonance ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electrochemical Analysis and Applications
Authors: Florian-Alexandru Brașoveanu | Adrian Burlacu
Source:  (article, 2023-09-12)
Field: Materials Science — Materials Chemistry

Towards independent SMA actuators: A novel analog two-state control system for flexible robots The soft robotics field offers a large variety of actuators and robots, complex structures and designs able to behave in a required way, offering speed and resistance and at the same time reducing the contact damage chance. The control of such structures using mathematical methods requires complex methods which evolved over time in the form of predictive control and even neural networks and genetic algorithms. The current paper presents ongoing research that can be found at the intersection of meta-materials and sensor technology. It approaches a simpler method of controlling a flexible actuator based on a shape memory alloy, the regulator being a two-state switch controller steered by a pressure force sensor. We propose a complete analogical electrical structure and analyze the performance of the outcome, focusing on time response. The present work also contains a brief description of a mechanical design proposal able to optimize the impact of the shape memory alloy used, in order to obtain an amplified actuation. Actuator ; SMA* ; Computer science ; Robot ; Control engineering ; Controller (irrigation) ; Intersection (aeronautics) ; Shape-memory alloy ; Robotics ; Artificial intelligence ; Field (mathematics) ; Control theory (sociology) ; Control (management) ; Engineering Shape Memory Alloy Transformations ; Soft Robotics and Applications
Authors: Bohayra Mortazavi | Fazel Shojaei | Xiaoying Zhuang
Source: Nano Trends (article, 2023-09-24)
Field: Materials Science — Materials Chemistry

A first-principles investigation on the structural, stability and mechanical properties of novel Ag, Au and Cu-graphdiyne magnetic semiconducting monolayers Most recently, silver-metalated graphdiyne (GDY) nanomembranes have been fabricated using the wet-chemistry approach, with highly efficient response for light-driven decomposition of antibiotics (ACS Appl. Nano Mater. 2023, 6, 7395). Inspired by this exciting advance, herein for the first time we theoretically explore the key physical properties of the single-layer M-(N)GDY (M=Cu, Ag, Au) monolayers. Spin-polarized density functional theory (DFT) results reveal that metalated GDY monolayers favor a high-spin ground state, presenting a magnetic moment of 3 µB/unitcell. The sheets are characterized as small band gap magnetic semiconductors, ranging between 0.112 to 0.960 eV, in which only one spin contributes to the band gap. Ab-initio molecular dynamics results confirm the remarkable thermal stability of the constructed graphdiyne nanomembranes at 1000 K. The dynamical stability and mechanical properties at the ground state are furthermore investigated using machine learning interatomic potentials. The ultimate tensile strengths of the metalated graphdiyne lattices are found to be decent, over 6 GPa close to the ground state, but a few folds lower than the native metal-free counterparts. The presented first-principles results confirm remarkable thermal, dynamical and mechanical stability, and appealing electronic characteristics of the metalated graphdiyne nanosheets, attractive to design advanced light-weight energy storage/conversion and spintronic nanosystems. Materials science ; Density functional theory ; Monolayer ; Ground state ; Spintronics ; Thermal stability ; Magnetic moment ; Band gap ; Ab initio ; Ab initio quantum chemistry methods ; Semiconductor ; Structural stability ; Chemical physics ; Nanotechnology ; Condensed matter physics ; Computational chemistry ; Ferromagnetism ; Optoelectronics ; Chemistry ; Molecule ; Atomic physics ; Physics MXene and MAX Phase Materials ; 2D Materials and Applications ; Molecular Junctions and Nanostructures
Authors: Aidan J. O’Donnell | Bowen Li | Srinivas Rangarajan | Chrysanthos E. Gounaris
Source: Chemical Engineering Science (article, 2023-10-20)
Field: Materials Science — Materials Chemistry

Automated identification of isofragmented reactions and application in correcting molecular property models Machine learning techniques are increasingly being employed to predict molecular properties. Such models are often trained on large computationally derived datasets, and are only as accurate as the underlying data. We exploit the well-known error cancelling effect of isodesmic and homodesmotic reactions to develop a multi-fidelity data-driven molecular property prediction method. First, we propose an optimization-based scheme to quickly and automatically identify all isofragmented reactions for a target molecule u, i.e., balanced reactions involving u and one or more molecules from a given set of molecules (M), which conserve a predefined set of fragments of arbitrary size. Second, we show that such isofragmented reactions can be leveraged to improve the predictive accuracy of a data-driven model by infusing a small high-accuracy dataset comprising molecules in M. We applied this method with a high-accuracy subset of the NIST thermochemistry database and a simple additive data-driven model trained on a QM9 subset. Our results show that the heats of formation using our method were ∼4.4kcal/mol more accurate on average than the data-driven model. NIST ; Isodesmic reaction ; Thermochemistry ; Computer science ; Set (abstract data type) ; Property (philosophy) ; Training set ; Experimental data ; Data set ; Simple (philosophy) ; Data mining ; Identification (biology) ; Algorithm ; Biological system ; Chemistry ; Artificial intelligence ; Molecule ; Mathematics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Process Optimization and Integration
Authors: Rayan Chakraborty | Volker Blüm
Source: Trends in Chemistry (article, 2023-09-15)
Field: Materials Science — Materials Chemistry

Curated materials data of hybrid perovskites: approaches and potential usage Data science ; Computer science ; Perovskite (structure) ; Property (philosophy) ; Point (geometry) ; Nanotechnology ; Engineering ; Materials science Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Perovskite Materials and Applications
Authors: Zekun Chen | Margaret L. Berrens | Kam-Tung Chan | Zheyong Fan | Davide Donadio
Source: ChemRxiv (preprint, 2023-10-18)
Field: Materials Science — Materials Chemistry

Thermodynamics of Water and Ice from a Fast and Scalable First-Principles Neuroevolution Potential Machine learning potentials enable molecular dynamics simulations to exceed the size and time scales that can be accessed by first-principles methods like density functional theory, while still maintaining the accuracy of the underlying training dataset. However, accurate machine learning potentials come with relatively high computational costs that limit their ability to predict properties requiring extensive sampling, large simulation cells, or long runs to converge. Here, we have developed and tested a neuroevolution-potential model for water trained to hybrid dispersion-corrected density functional calculations. This model exhibits accuracy and transferability comparable to state-of-the-art machine learning potentials but at a much lower computational cost. As a result, it enabled us to compute well-converged thermodynamics averages and fluctuations. This allowed us to assess the ability of our model to reproduce several thermodynamic properties of water and ice, as well as the anomalous behavior of water density, heat capacity, and compressibility. The efficient GPU acceleration of our model and its capability to reproduce water thermodynamics in good agreement with experiments make it suitable for simulating phase transitions and slow dynamical processes in aqueous systems. Computer science ; Statistical physics ; Scalability ; Limit (mathematics) ; Artificial neural network ; Artificial intelligence ; Machine learning ; Physics ; Mathematics Machine Learning in Materials Science ; Quantum, superfluid, helium dynamics ; Quantum many-body systems
Authors: Sina Hossein Zadeh | Amir Behbahanian | John Broucek | Mingzhou Fan | Guillermo Vázquez Tovar | Mohammad Noroozi | William Trehern | Xiaoning Qian | İbrahim Karaman | Raymundo Arróyave
Source: Preprints.org (preprint, 2023-02-06)
Field: Materials Science — Materials Chemistry

An Interpretable Boosting-based Predictive Model for Transformation Temperatures of Shape Memory Alloys In this study, we demonstrate how the incorporation of appropriate feature engineering together with the selection of a Machine Learning (ML) algorithm that best suits the available dataset, leads to the development of a predictive model for transformation temperatures that can be applied to a wide range of shape memory alloys. We develop a gradient boosting ML surrogate model capable of predicting Martensite Start, Martensite Finish, Austenite Start, and Austenite Finish transformation temperatures with an average accuracy of more than 95% by explicitly taking care of potential distribution changes when modeling different alloy systems. We included heat treatment, rolling, extrusion processing parameters, and alloy system categorical features in the model input features to achieve more accurate and realistic results. In addition, using Shapley values, which are calculated based on the average marginal contribution of features to all possible coalitions, this study was able to gain insights into the governing features and their effect on predicted transformation temperatures, providing a unique opportunity to examine the critical parameters and features in martensite transformation temperatures. Categorical variable ; Austenite ; Shape-memory alloy ; Martensite ; Transformation (genetics) ; Diffusionless transformation ; Alloy ; Boosting (machine learning) ; Materials science ; Computer science ; Artificial intelligence ; Algorithm ; Machine learning ; Metallurgy ; Microstructure Shape Memory Alloy Transformations ; Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels