jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Guoyan Li | Xiaoning Jin | Yujia Wang | Swastik Kar
Source:  (article, 2023-08-26)
Field: Materials Science — Materials Chemistry

Optimal Parameters Design for Manufacturability under Unknown Feasibility Constraints This paper proposes a novel approach for optimizing the manufacturing process under unknown feasibility constraints. Due to the complex interdependencies among the numerous design of experiment parameters, a trial-and-error approach is impractical. Our approach combines a predictive modeling block that uses two machine learning models and an experimental design component employing multiple sampling strategies. We applied this method to the synthesis of 2D material via thermal chemical vapor deposition and achieved optimal material quality within only 7 batches of experiments, amounting to 61 samples. Additionally, we successfully identified the feasible region of synthesis parameters necessary for producing the desired material. These results not only highlight the effectiveness of our method but also its potential to guide engineers towards the most desirable outcome in manufacturing process optimization. Design for manufacturability ; Computer science ; Component (thermodynamics) ; Process (computing) ; Block (permutation group theory) ; Sampling (signal processing) ; Quality (philosophy) ; Engineering design process ; Reliability engineering ; Design of experiments ; Mathematical optimization ; Engineering ; Mechanical engineering ; Mathematics Machine Learning in Materials Science ; Additive Manufacturing and 3D Printing Technologies ; Advanced Multi-Objective Optimization Algorithms
Authors: Joao Paulo Almeida de Mendonca | Lorenzo A. Mariano | Émilie Devijver | N. Jakse | Roberta Poloni
Source: arXiv (Cornell University) (preprint, 2023-04-16)
Field: Materials Science — Materials Chemistry

An Artificial Neural Network-based Density Functional Approach for Adiabatic Energy Differences in Transition Metal Complexes During the past decades, approximate Kohn-Sham density-functional theory schemes garnered many successes in computational chemistry and physics; yet the performance in the prediction of spin state energetics is often unsatisfactory. By means of a machine-learning approach, an enhanced exchange and correlation functional is developed to describe adiabatic energy differences in transition metal complexes. The functional is based on the computationally efficient revision of the regularized strongly constrained and appropriately normed (R2SCAN) functional and improved by an artificial neural-network correction trained over a small dataset of electronic densities, atomization energies and/or spin state energetics. The training process, performed using a bio-inspired non gradient-based approach adapted for this work from Particle Swarm Optimization, is discussed in detail. The meta-GGA functional is finally shown to outperform most known density functionals in the prediction of adiabatic energy differences for both the validation test and the generality test. Density functional theory ; Adiabatic process ; Hybrid functional ; Artificial neural network ; Energy functional ; Statistical physics ; Generality ; Time-dependent density functional theory ; Spin (aerodynamics) ; Computer science ; Artificial intelligence ; Physics ; Machine learning ; Quantum mechanics ; Thermodynamics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Computational Drug Discovery Methods
Authors: Soohaeng Yoo Willow | Lulu Kang | David D. L. Minh
Source: arXiv (Cornell University) (preprint, 2023-06-24)
Field: Materials Science — Materials Chemistry

Learned Mappings for Targeted Free Energy Perturbation between Peptide Conformations Targeted free energy perturbation uses an invertible mapping to promote configuration space overlap and the convergence of free energy estimates. However, developing suitable mappings can be challenging. Wirnsberger et al. (2020) demonstrated the use of machine learning to train deep neural networks that map between Boltzmann distributions for different thermodynamic states. Here, we adapt their approach to free energy differences of a flexible bonded molecule, deca-alanine, with harmonic biases with different spring centers. When the neural network is trained until ``early stopping'' - when the loss value of the test set increases - we calculate accurate free energy differences between thermodynamic states with spring centers separated by 1 Å and sometimes 2 Å. For more distant thermodynamic states, the mapping does not produce structures representative of the target state and the method does not reproduce reference calculations. Invertible matrix ; Artificial neural network ; Boltzmann constant ; Boltzmann machine ; Free energy perturbation ; Perturbation (astronomy) ; Computer science ; Statistical physics ; Convergence (economics) ; Energy (signal processing) ; Physics ; Mathematics ; Topology (electrical circuits) ; Artificial intelligence ; Molecule ; Thermodynamics ; Quantum mechanics ; Combinatorics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Mass Spectrometry Techniques and Applications
Authors: Manuel Grumet | Clara von Scarpatetti | Tomáš Bučko | David A. Egger
Source: arXiv (Cornell University) (preprint, 2023-07-20)
Field: Chemistry — Analytical Chemistry

Delta Machine Learning for Predicting Dielectric Properties and Raman Spectra Raman spectroscopy is an important characterization tool with diverse applications in many areas of research. We propose a machine learning method for predicting polarizabilities with the goal of providing Raman spectra from molecular dynamics trajectories at reduced computational cost. A linear-response model is used as a first step and symmetry-adapted machine learning is employed for the higher-order contributions as a second step. We investigate the performance of the approach for several systems including molecules and extended solids. The method can reduce training set sizes required for accurate dielectric properties and Raman spectra in comparison to a single-step machine learning approach. Raman spectroscopy ; Dielectric ; Characterization (materials science) ; Machine learning ; Computer science ; Artificial intelligence ; Symmetry (geometry) ; Set (abstract data type) ; Biological system ; Materials science ; Optics ; Physics ; Nanotechnology ; Mathematics ; Optoelectronics Spectroscopy and Chemometric Analyses ; Machine Learning in Materials Science ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Alessandro Troisi
Source:  (article, 2023-01-11)
Field: Materials Science — Materials Chemistry

Digital materials discovery in organic electronics This talk will provide an overview of the three main approaches used within the group to aid the discovery of new materials (i) high-throughput virtual screening (ii) machine learning and (iii) bottom up construction of physical models. The relation between the tree approaches will be discussed including a methodology for selecting the best strategy given time and budget constraints. The examples cover a number of areas related to organic electronics including charge transport in molecular and polymeric materials, singlet fission, temperature activated delayed fluorescence, organic photovoltaics, emissive materials. We also introduce the DIADEM project, that enables identification and testing-in-the-lab of novel materials for organic electronics. Electronics ; Organic electronics ; Identification (biology) ; Organic solar cell ; Computer science ; Throughput ; Cover (algebra) ; Organic semiconductor ; Nanotechnology ; Systems engineering ; Materials science ; Engineering ; Electrical engineering ; Mechanical engineering ; Photovoltaic system ; Voltage ; Telecommunications ; Transistor ; Optoelectronics Machine Learning in Materials Science ; Conducting polymers and applications ; Organic Electronics and Photovoltaics
Authors: Logan Ward | Ben Blaiszik | Cheng‐Wei Lee | Troy Martin | Ian Foster | André Schleife
Source: arXiv (Cornell University) (preprint, 2023-11-01)
Field: Materials Science — Materials Chemistry

Accelerating Electronic Stopping Power Predictions by 10 Million Times with a Combination of Time-Dependent Density Functional Theory and Machine Learning Knowing the rate at which particle radiation releases energy in a material, the stopping power, is key to designing nuclear reactors, medical treatments, semiconductor and quantum materials, and many other technologies. While the nuclear contribution to stopping power, i.e., elastic scattering between atoms, is well understood in the literature, the route for gathering data on the electronic contribution has for decades remained costly and reliant on many simplifying assumptions, including that materials are isotropic. We establish a method that combines time-dependent density functional theory (TDDFT) and machine learning to reduce the time to assess new materials to mere hours on a supercomputer and provides valuable data on how atomic details influence electronic stopping. Our approach uses TDDFT to compute the electronic stopping contributions to stopping power from first principles in several directions and then machine learning to interpolate to other directions at a cost of 10 million times fewer core-hours. We demonstrate the combined approach in a study of proton irradiation in aluminum and employ it to predict how the depth of maximum energy deposition, the "Bragg Peak," varies depending on incident angle -- a quantity otherwise inaccessible to modelers. The lack of any experimental information requirement makes our method applicable to most materials, and its speed makes it a prime candidate for enabling quantum-to-continuum models of radiation damage. The prospect of reusing valuable TDDFT data for training the model make our approach appealing for applications in the age of materials data science. Stopping power ; Time-dependent density functional theory ; Computer science ; Bragg peak ; Isotropy ; Density functional theory ; Proton ; Computational physics ; Statistical physics ; Physics ; Nuclear physics ; Quantum mechanics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Chemical Physics Studies
Authors: Niaz Abdolrahim | Jerardo E. Salgado | Samuel Lerman | Zhaotong Du | Chenliang Xu
Source: Research Square (preprint, 2023-05-08)
Field: Materials Science — Materials Chemistry

Automated Classification of Big X-ray Diffraction Data Using Deep Learning Models Abstract Current in-situ X-ray diffraction (XRD) techniques generate data over human analytical capabilities – leading to the loss of novel insights. Automated techniques require human intervention, and lack the performance and adaptability needed for material exploration. With the critical need for high-throughput automated XRD pattern analysis of novel materials, we developed a generalized deep learning model to classify a diverse set of materials’ crystal systems and space groups. In our approach, we generated training data with a holistic representation of patterns that emerge from varying experimental conditions and crystal properties. We then used an expedited learning technique to refine our model’s expertise to experimental conditions. Additionally, we used evaluation data to interpret our model’s decision-making and optimized model architecture to elicit classification based on Bragg’s Law. We evaluated our models on experimental data, novel materials, and altered cubic crystals, where we observed state-of-the-art performance and even greater advances in space group classification. Deep learning ; Artificial intelligence ; Diffraction ; Computer science ; Big data ; X-ray crystallography ; Data mining ; Optics ; Physics X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Radiomics and Machine Learning in Medical Imaging
Authors: Mohamed Bilal Shakeel | Samir Brahim Belhaouari | Fedwa El‐Mellouhi
Source: arXiv (Cornell University) (preprint, 2023-11-23)
Field: Materials Science — Materials Chemistry

Automated Model Training (AMT) GUI: An Opportunity for integrating AI in the Laboratory Experiment In the field of materials science, comprehending material properties is often hindered by the complexity of datasets originating from various sources. This study introduces the Automated Model Training (AMT) Graphical User Interface (GUI), specifically crafted for the use of researchers and scientists without a programming background to design the next set of experiments either in a chemistry lab or on the computer. The GUI integrates diverse machine learning models, such as XG-Boost, Random Forest, Support Vector Regression, Linear Regression, Generalized Additive Model (GAM), and Stack Regressors, offering a robust toolkit for data analysis. It facilitates the exploration of complex relationships, non-linear patterns, and predictive accuracy optimization. To further enhance its utility, the GUI integrates the Particle Swarm Optimization (PSO) technique, allowing researchers to systematically explore vast parameter spaces and identify optimal experimental conditions. This synergy between machine learning and PSO empowers material scientists through a user-friendly platform for data-driven discovery. The AMT GUI bridges the gap between traditional experimentation and machine learning, enabling precise and efficient exploration of the materials research space. Computer science ; Graphical user interface ; Machine learning ; Particle swarm optimization ; Artificial intelligence ; Support vector machine ; Set (abstract data type) ; Interface (matter) ; Field (mathematics) ; Random forest ; Data mining ; Human–computer interaction ; Programming language Machine Learning in Materials Science
Authors: 勇貴 菅原 | Yuuki Sugawara
Source: Institutional Repositories DataBase (IRDB) (article, 2023-09-01)
Field: Materials Science — Materials Chemistry

Machine Learning-Aided Prediction and Construction of a Descriptor for Polymer Properties: A Case Study on the Lower Critical Solution Temperature of Copolymerized N-Isopropylacrylamides Polymer ; Lower critical solution temperature ; Work (physics) ; Polymer solution ; Copolymer Machine Learning in Materials Science ; Hydrogels: synthesis, properties, applications ; Advanced Polymer Synthesis and Characterization
Authors: Franck Polewczyk | Jean‐Marc Leyssale | Philippe Aurel | Nicolas Pineau | Christophe Denoual | Gérard L. Vignoles | Paul Lafourcade
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Elasticity of Dense Anisotropic Carbons: A Machine Learning Model of the Structure-Property Relationship Fed by Large Scale Molecular Dynamics Data Anisotropy ; Elasticity (physics) ; Property (philosophy) ; Statistical physics ; Molecular dynamics ; Scale (ratio) ; Materials science ; Econometrics ; Computer science ; Biological system ; Artificial intelligence ; Mathematics ; Physics ; Chemistry ; Composite material ; Optics ; Computational chemistry Machine Learning in Materials Science ; Carbon Nanotubes in Composites ; X-ray Diffraction in Crystallography
Authors: Longkun Xu | Wei Shao | Haishun Jin | Qiang Wang
Source: ChemRxiv (preprint, 2023-08-16)
Field: Materials Science — Materials Chemistry

Data Efficient and Stability Indicated Samplingfor Developing Reactive Machine LearningPotential to Achieve Ultra-long Simulation inLithium Metal Batteries Modelling the formation of solid-liquid interphase (SEI) is challenging as its strict requirement with both simulation accuracy and length. Machine learning potential (MLP) based molecular dynamics (MD) simulation is expected to play a role in this field while currently its use is hindered by sampling efficiency and simulation stability. In this work, we tackle the two challenges together. We propose the stability-indicatedsampling (SIS) algorithm for efficiently sampling training data using physical information (temperature). Unlike previous strategies, our method does not need prior knowledge of reaction networks or training multiple MLPs for uncertainty estimation. Compared with the recent proposed methods HAIR and DP-GEN, our approach gives significant improvement of sampling efficiency with less requirements with the initial training data, to realize > 10 ns MLPMD simulation using ab initio MD (AIMD) trajectory of just a few ps. We introduce the concept underlying instability consistency by showing the accuracy of reaction mechanisms and radial distribution function (RDF) can be improved by SIS-MLPMD, although their information is not explicitly used in our sampling decision. Furthermore, we show that long-time MLPMD simulation of Lithium metal battery (LMB) can not only reproduce some well-known SEI components including LiF, Li2O, LiOH, LiS and the incomplete N-S breaking in highconcentration systems, but also ionic aggregation structures of LiF, which is not shown in our AIMD training data but matches previous results of electrochemical impedance spectroscopy. Our work is expected to help accelerate future investigations, especially for studying long-time (≥ ns scale) reaction dynamics in interfacial problems. Stability (learning theory) ; Computer science ; Sampling (signal processing) ; Lithium (medication) ; Battery (electricity) ; SPARK (programming language) ; Work (physics) ; Field (mathematics) ; Molecular dynamics ; Machine learning ; Power (physics) ; Chemistry ; Computational chemistry ; Engineering ; Mathematics ; Mechanical engineering ; Physics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Advanced Battery Technologies Research
Authors: Qi Gao | Michihiko Sugawara | Paul D. Nation | Takao Kobayashi | Yu‐ya Ohnishi | Hiroyuki Tezuka | Naoki Yamamoto
Source: ArXiv.org (preprint, 2023-10-06)
Field: Materials Science — Materials Chemistry

A combined quantum-classical method applied to material design: optimization and discovery of photochromic materials for photopharmacology applications Integration of quantum chemistry simulations, machine learning techniques, and optimization calculations is expected to accelerate material discovery by making large chemical spaces amenable to computational study; a challenging task for classical computers. In this work, we develop a combined quantum-classical computing scheme involving the computational-basis Variational Quantum Deflation (cVQD) method for calculating excited states of a general classical Hamiltonian, such as Ising Hamiltonian. We apply this scheme to the practical use case of generating photochromic diarylethene (DAE) derivatives for photopharmacology applications. Using a data set of 384 DAE derivatives quantum chemistry calculation results, we show that a factorization-machine-based model can construct an Ising Hamiltonian to accurately predict the wavelength of maximum absorbance of the derivatives, $λ_{\rm max}$, for a larger set of 4096 DAE derivatives. A 12-qubit cVQD calculation for the constructed Ising Hamiltonian provides the ground and first four excited states corresponding to five DAE candidates possessing large $λ_{\rm max}$. On a quantum simulator, results are found to be in excellent agreement with those obtained by an exact eigensolver. Utilizing error suppression and mitigation techniques, cVQD on a real quantum device produces results with accuracy comparable to the ideal calculations on a simulator. Finally, we show that quantum chemistry calculations for the five DAE candidates provides a path to achieving large $λ_{\rm max}$ and oscillator strengths by molecular engineering of DAE derivatives. These findings pave the way for future work on applying hybrid quantum-classical approaches to large system optimization and the discovery of novel materials. Photochromism ; Quantum ; Nanotechnology ; Computer science ; Materials science ; Physics ; Quantum mechanics Photochromic and Fluorescence Chemistry ; Chemistry and Chemical Engineering
Authors: Xiaonan Wang | Jinfeng Yang | Penghua Ying | Zheyong Fan | Jin Zhang | Huarui Sun
Source: arXiv (Cornell University) (preprint, 2023-11-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Dissimilar thermal transport properties in $κ$-Ga$_2$O$_3$ and $β$-Ga$_2$O$_3$ revealed by machine-learning homogeneous nonequilibrium molecular dynamics simulations The lattice thermal conductivity (LTC) of Ga$_2$O$_3$ is an important property due to the challenge in the thermal management of high-power devices. We develop machine-learned neuroevolution potentials for single-crystalline $β$-Ga$_2$O$_3$ and $κ$-Ga$_2$O$_3$, and apply them to perform homogeneous nonequilibrium molecular dynamics simulations to predict their LTCs. The LTC of $β$-Ga$_2$O$_3$ was determined to be 10.3 $\pm$ 0.2 W/(m K), 19.9 $\pm$ 0.2 W/(m K), and 12.6 $\pm$ 0.2 W/(m K) along [100], [010], and [001], respectively, aligning with previous experimental measurements. For the first time, we predict the LTC of $κ$-Ga$_2$O$_3$ along [100], [010], and [001] to be 4.5 $\pm$ 0.0 W/(m K), 3.9 $\pm$ 0.0 W/(m K), and 4.0 $\pm$ 0.1 W/(m K), respectively, showing a nearly isotropic thermal transport property. The reduced LTC of $κ$-Ga$_2$O$_3$ versus $β$-Ga$_2$O$_3$ stems from its restricted low-frequency phonons up to 5 THz. Furthermore, we find that the $β$ phase exhibits a typical temperature dependence slightly stronger than $\sim T^{-1}$, whereas the $κ$ phase shows a weaker temperature dependence, ranging from $\sim T^{-0.5}$ to $\sim T^{-0.7}$. Isotropy ; Phonon ; Thermal conductivity ; Lattice (music) ; Homogeneous ; Molecular dynamics ; Thermal ; Non-equilibrium thermodynamics ; Materials science ; Physics ; Condensed matter physics ; Thermodynamics ; Optics ; Quantum mechanics Ga2O3 and related materials ; Electronic and Structural Properties of Oxides ; ZnO doping and properties
Authors: Zeliang Shen | Jie Li | Jingang Wang
Source:  (article, 2023-06-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Improving the Accuracy of Jiles-Atherton Hysteresis Loop Cluster Simulations in Low Saturation Regions with Neural Networks Hysteresis is a fundamental phenomenon observed in ferromagnetic materials, characterized by the lagging of the magnetization response behind the applied magnetic field. The Jiles-Atherton (JA) model has gained popularity due to its ability to reproduce major and minor loops with relatively few parameters and its ease of implementation. However, the JA model exhibits limitations in accurately simulating hysteresis loops in low-saturation regions. Therefore, this paper proposes the use of neural networks to enhance the accuracy of hysteresis loop simulations in low-saturation regions. Neural networks are trained on experimental data and are capable of capturing the underlying nonlinear relationships between the applied field and magnetization response. The integration of neural networks into the JA model significantly improves the simulation accuracy in low saturation regions, making it more suitable for the simulation of transformer low-frequency transients. This study demonstrates the potential of leveraging advanced machine learning techniques to refine classical models and expand their applicability. Lagging ; Artificial neural network ; Saturation (graph theory) ; Magnetic hysteresis ; Hysteresis ; Computer science ; Nonlinear system ; Transformer ; Magnetization ; Control theory (sociology) ; Magnetic field ; Biological system ; Electronic engineering ; Artificial intelligence ; Physics ; Engineering ; Condensed matter physics ; Voltage ; Mathematics ; Electrical engineering Magnetic Properties and Applications ; Magnetic properties of thin films ; Non-Destructive Testing Techniques