jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kōji Shimizu | Parth Bahuguna | Shigeo Mori | Akitoshi Hayashi | Satoshi Watanabe
Source: arXiv (Cornell University) (preprint, 2023-12-12)
Field: Materials Science — Materials Chemistry

Enhanced ionic conductivity through crystallization of glass-Li$_3$PS$_4$ by machine learning molecular dynamics simulations Understanding the atomistic mechanism of ion conduction in solid electrolytes is critical for the advancement of all-solid-state batteries. Glass-ceramics, which undergo crystallization from a glass state, frequently exhibit unique properties including enhanced ionic conductivities compared to both the original crystalline and glass forms. Despite these distinctive features, specific details regarding the behavior of ion conduction in glass-ceramics, particularly concerning conduction pathways, remain elusive. In this study, we demonstrate the crystallization process of glass-Li$_3$PS$_4$ through molecular dynamics simulations employing machine learning interatomic potentials constructed from first principles calculation data. Our analyses of Li conduction using the obtained partially crystallized structures reveal that the diffusion barriers of Li decrease as the crystallinity in Li$_3$PS$_4$ glass-ceramics increases. Furthermore, Li displacements predominantly occur in the precipitated crystalline portion, suggesting that percolation conduction plays a significant role in enhanced Li conduction. These findings provide valuable insights for the future utilization of glass-ceramic materials. Crystallization ; Materials science ; Thermal conduction ; Ionic conductivity ; Chemical physics ; Glass-ceramic ; Crystallinity ; Percolation (cognitive psychology) ; Ionic bonding ; Ceramic ; Molecular dynamics ; Ion ; Fast ion conductor ; Diffusion ; Conductivity ; Electrolyte ; Thermodynamics ; Physical chemistry ; Composite material ; Chemistry ; Computational chemistry ; Physics Machine Learning in Materials Science
Authors: Luis Cesar de Azevedo | Ronaldo C. Prati
Source: Journal of Chemical Information and Modeling (article, 2023-08-25)
Field: Materials Science — Materials Chemistry

Effect of Flattened Structures of Molecules and Materials on Machine Learning Model Training A key aspect of producing accurate and reliable machine learning models for the prediction of properties of quantum chemistry (QC) data is identifying possible data characteristics that may negatively influence model training. In previous work, we identified that molecules and materials with a low volume of the convex hull (VCH) of atomic positions may be harmful in model training and a source of prediction outliers. In this paper, we extend this analysis further and develop a biased sampling study to evaluate the influence of VCH on the training data of a model using different structures of molecules and materials. Our study confirms that VCH influences model training and shows the importance of using homogeneous geometric characteristics of structures when building new data sets or selecting training sets from larger QC data sets. Outlier ; Computer science ; Convex hull ; Key (lock) ; Training set ; Training (meteorology) ; Machine learning ; Artificial intelligence ; Homogeneous ; Work (physics) ; Regular polygon ; Sampling (signal processing) ; Data mining ; Algorithm ; Mathematics ; Statistical physics ; Geometry ; Mechanical engineering ; Engineering ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Elizabeth S. Thrall | Fernando Martinez Lopez | Thomas Egg | Seung Eun Lee | Joshua Schrier | Yijun Zhao
Source: ChemRxiv (preprint, 2023-08-04)
Field: Chemistry — Physical and Theoretical Chemistry

Rediscovering the particle in a box: Machine learning regression analysis for hypothesis generation in physical chemistry lab Given the growing prevalence of computational methods in chemistry, it is essential that undergraduate curricula introduce students to these approaches. One such area is the application of machine learning (ML) techniques to chemistry. Here we describe a new activity that applies ML regression analysis to the common physical chemistry laboratory experiment on the electronic absorption spectra of cyanine dyes. In the classic version of this experiment, students collect experimental spectra and interpret them using the Kuhn free electron model, based on the quantum mechanical particle-in-a-box (PIB). Our new computational activity has students train regression models of increasing complexity to predict the wavelength of maximum absorption for different cyanine dyes using a set of 13 molecular features. In addition, the activity introduces methods for evaluating and interpreting regression models. Ultimately, students are prompted to use their regression analysis results to generate hypotheses for what molecular properties underlie cyanine dye absorption, leading them naturally to the PIB model. In this report, we provide a dataset, reference code implementations in Mathematica and Python notebooks, and an example lab protocol with an introduction to cyanine dyes and the ML techniques. This activity can be completed in a single 3-hour lab period by upper-level undergraduate students with relatively little prior programming experience. Although intended to complement the experimental measurement of cyanine dye spectra, this activity can also be performed on its own; alternatively, it can form the basis of more involved projects in a computational chemistry or machine learning course. Cyanine ; Python (programming language) ; Chemistry ; Computer science ; Regression analysis ; Machine learning ; Artificial intelligence ; Physics ; Programming language ; Optics Various Chemistry Research Topics
Authors: Eric R. Beyerle | Ziyue Zou | Pratyush Tiwary
Source: arXiv (Cornell University) (preprint, 2023-04-26)
Field: Materials Science — Materials Chemistry

Recent advances in describing and driving crystal nucleation using machine learning and artificial intelligence With the advent of faster computer processors and especially graphics processing units (GPUs) over the last few decades, the use of data-intensive machine learning (ML) and artificial intelligence (AI) has increased greatly, and the study of crystal nucleation has been one of the beneficiaries. In this review, we outline how ML and AI have been applied to address four outstanding difficulties of crystal nucleation: how to discover better reaction coordinates (RCs) for describing accurately non-classical nucleation situations; the development of more accurate force fields for describing the nucleation of multiple polymorphs or phases for a single system; more robust identification methods for determining crystal phases and structures; and as a method to yield improved course-grained models for studying nucleation. Nucleation ; Crystal (programming language) ; Artificial intelligence ; Computer science ; Identification (biology) ; Graphics ; Machine learning ; Materials science ; Physics ; Computer graphics (images) ; Thermodynamics Crystallization and Solubility Studies ; Machine Learning in Materials Science ; nanoparticles nucleation surface interactions
Authors: Joshua F. Belot | Valentin Taufour | Stefano Sanvito | Gus L. W. Hart
Source: arXiv (Cornell University) (preprint, 2023-07-13)
Field: Materials Science — Materials Chemistry

Machine Learning Predictions of High-Curie-Temperature Materials Technologies that function at room temperature often require magnets with a high Curie temperature, $T_\mathrm{C}$, and can be improved with better materials. Discovering magnetic materials with a substantial $T_\mathrm{C}$ is challenging because of the large number of candidates and the cost of fabricating and testing them. Using the two largest known data sets of experimental Curie temperatures, we develop machine-learning models to make rapid $T_\mathrm{C}$ predictions solely based on the chemical composition of a material. We train a random forest model and a $k$-NN one and predict on an initial dataset of over 2,500 materials and then validate the model on a new dataset containing over 3,000 entries. The accuracy is compared for multiple compounds' representations ("descriptors") and regression approaches. A random forest model provides the most accurate predictions and is not improved by dimensionality reduction or by using more complex descriptors based on atomic properties. A random forest model trained on a combination of both datasets shows that cobalt-rich and iron-rich materials have the highest Curie temperatures for all binary and ternary compounds. An analysis of the model reveals systematic error that causes the model to over-predict low-$T_\mathrm{C}$ materials and under-predict high-$T_\mathrm{C}$ materials. For exhaustive searches to find new high-$T_\mathrm{C}$ materials, analysis of the learning rate suggests either that much more data is needed or that more efficient descriptors are necessary. Curie temperature ; Ternary operation ; Random forest ; Marie curie ; Machine learning ; Dimensionality reduction ; Curse of dimensionality ; Materials science ; Thermodynamics ; Artificial intelligence ; Computer science ; Algorithm ; Condensed matter physics ; Physics ; Ferromagnetism Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Filipi França dos Santos | Kelly Cristine da Silveira | Gesiane Mendonça Ferreira | Daniella Herdi Cariello | Mônica Calixto de Andrade
Source: The Journal of Engineering and Exact Sciences (article, 2023-12-28)
Field: Materials Science — Materials Chemistry

Perovskite Solar Cell: Chemical Composition and Bandgap Energy via Machine Learning The exponential growth in publications and applications of perovskite photovoltaic cells highlights their significance in energy conversion and carbon emissions mitigation. From 2009 to 2023, the efficiency of these cells has significantly increased from 3.9% to 25.7%. The adaptive capacity of perovskite structures for solar spectrum absorption and current displacement is strongly influenced by the bandgap energy, ideally situated between 1.3 and 1.7 eV. Although various perovskite compositions can potentially attain this energy range, the synthesis methodologies remain empirically driven, presenting challenges to experimental viability. In this context, leveraging experimental databases provided by global researchers emerges as an effective approach to expedite and enable research on perovskite structures for photovoltaic cells. This study utilized the comprehensive MaterialsZone database to feed machine learning algorithms, focusing on Support Vector Machine (SVM) and Random Forest (RF) methodologies to predict the bandgap energy in a targeted perovskite composition. By conducting synthesis experiments towards specific compositions guided by model predictions, it becomes feasible to efficiently achieve the desired bandgap energy. Such a strategy not only accelerates research progress but also serves to curtail costs associated with the synthesis of perovskite materials. The RF model exhibited an average percentage error of 5.13%, a standard deviation of the percentage error of 6.99%, and a Root Mean Square Error (RMSE) of 0.119. In contrast, the SVM model recorded an average percentage error of 4.05%, a standard deviation of the percentage error of 6.45%, and RMSE of 0.881. These developed models not only demonstrate high predictive capacity but also contribute substantively to the comprehension of the intricate relationship between the chemical composition and bandgap energy values of perovskites. By deploying machine learning algorithms, this work paves the way for targeted optimizations and considerable strides in the manufacturing of perovskite-based photovoltaic cells. Band gap ; Photovoltaic system ; Mean squared error ; Perovskite (structure) ; Computer science ; Context (archaeology) ; Support vector machine ; Standard deviation ; Materials science ; Energy conversion efficiency ; Machine learning ; Algorithm ; Artificial intelligence ; Nanotechnology ; Mathematics ; Optoelectronics ; Statistics ; Engineering ; Electrical engineering ; Chemical engineering ; Geology Machine Learning in Materials Science ; Perovskite Materials and Applications ; Chalcogenide Semiconductor Thin Films
Authors: Xingyi Guan | Joseph P. Heindel | Taehee Ko | Chao Yang | Teresa Head‐Gordon
Source: arXiv (Cornell University) (preprint, 2023-06-14)
Field: Materials Science — Materials Chemistry

Beyond potential energy surface benchmarking: a complete application of machine learning to chemical reactivity We train an equivariant machine learning model to predict energies and forces for a real-world study of hydrogen combustion under conditions of finite temperature and pressure. This challenging case for reactive chemistry illustrates that ML learned potential energy surfaces (PESs) are always incomplete as they are overly reliant on chemical intuition of what data is important for training, i.e. stable or metastable energy states. Instead we show here that a negative design data acquisition strategy is necessary to create a more complete ML model of the PES, since it must also learn avoidance of unforeseen high energy intermediates or even unphysical energy configurations. Because this type of data is unintuitive to create, we introduce an active learning workflow based on metadynamics that samples a lower dimensional manifold within collective variables that efficiently creates highly variable energy configurations for further ML training. This strategy more rapidly completes the ML PES such that deviations among query by committee ML models helps to now signal occasional calls to the external ab initio data source to further molecular dynamics in time without need for retraining the ML model. With the hybrid ML-physics model we predict the change in transition state and/or reaction mechanism at finite temperature and pressure for hydrogen combustion, thereby delivering on the promise of real application work using ML trained models of an ab initio PES with two orders of magnitude reduction in cost. Metadynamics ; Potential energy surface ; Ab initio ; Metastability ; Computer science ; Transition state ; Artificial intelligence ; Machine learning ; Chemistry ; Molecular dynamics ; Computational chemistry ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Yi-Ming Tseng | Pei-Te Wang | Nan-Yow Chen | An-Cheng Yang | Nien‐Ti Tsou
Source: Computational Materials Science (article, 2023-08-13)
Field: Materials Science — Materials Chemistry

Martensite Variant Identification Method for shape memory alloys by using graph neural network Martensite ; Shape-memory alloy ; Monoclinic crystal system ; Molecular dynamics ; Orthorhombic crystal system ; Materials science ; Diffusionless transformation ; Artificial neural network ; Computer science ; Nanoindentation ; Microstructure ; Artificial intelligence ; Statistical physics ; Crystal structure ; Crystallography ; Chemistry ; Physics ; Metallurgy ; Computational chemistry Machine Learning in Materials Science ; Shape Memory Alloy Transformations ; Advanced Memory and Neural Computing
Authors: Daniel Hedman | Ben McLean | Christophe Bichara | Shigeo Maruyama | J. Andreas Larsson | Feng Ding
Source: arXiv (Cornell University) (preprint, 2023-02-19)
Field: Materials Science — Materials Chemistry

Dynamics of growing carbon nanotube interfaces probed by machine learning-enabled molecular simulations Carbon nanotubes (CNTs) are currently considered a successor to silicon in future nanoelectronic devices. To realize this, controlled growth of defect-free nanotubes is required. Until now, the understanding of atomic-scale CNT growth mechanisms provided by molecular dynamics simulations has been hampered by their short timescales. Here, we develop an efficient and accurate machine learning force field, DeepCNT-22, to simulate the complete growth of defect-free single-walled CNTs (SWCNTs) on iron catalysts at near-microsecond timescales. We provide atomic-level insight into the nucleation and growth processes of SWCNTs, including the evolution of the tube-catalyst interface and the mechanisms underlying defect formation and healing. Our simulations highlight the maximization of SWCNT-edge configurational entropy during growth and how defect-free CNTs can grow ultralong if carbon supply and temperature are carefully controlled. Carbon nanotube ; Nanotechnology ; Nucleation ; Materials science ; Molecular dynamics ; Atomic units ; Nanotube ; Silicon ; Chemical physics ; Chemistry ; Optoelectronics ; Computational chemistry ; Physics Machine Learning in Materials Science ; Carbon Nanotubes in Composites ; Chemical and Physical Properties of Materials
Authors: Rafael Felipe da Costa Vieira | Kally Alves de Sousa | Ian Castro‐Gamboa
Source: SSRN Electronic Journal (article, 2023-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Lumios - Label Using Machine in Organic Samples&amp;nbsp;- A Software for Dereplication, Molecular Docking, And Combined Machine and Deep Learning Docking (animal) ; Software ; Computer science ; Artificial intelligence ; Chemistry ; Machine learning ; Operating system ; Medicine Various Chemistry Research Topics ; Computational Drug Discovery Methods
Authors: Junbao Hu | Liyang Zhou | Jian Jiang
Source: arXiv (Cornell University) (preprint, 2023-12-15)
Field: Materials Science — Materials Chemistry

Efficient Machine Learning Force Field for Large-Scale Molecular Simulations of Organic Systems To address the computational challenges of ab initio molecular dynamics and the accuracy limitations of empirical force fields, the introduction of machine learning force fields has proven effective in various systems including metals and inorganic materials. However, in large-scale organic systems, the application of machine learning force fields is often hindered by impediments such as the complexity of long-range intermolecular interactions and molecular conformations, as well as the instability in long-time molecular simulations. Therefore, we propose a universal multiscale higher-order equivariant model combined with active learning techniques, efficiently capturing the complex long-range intermolecular interactions and molecular conformations. Compared to existing equivariant models, our model achieves the highest predictive accuracy, and magnitude-level improvements in computational speed and memory efficiency. In addition, a bond length stretching method is designed to improve the stability of long-time molecular simulations. Utilizing only 901 samples from a dataset with 120 atoms, our model successfully extends high precision to systems with hundreds of thousands of atoms. These achievements guarantee high predictive accuracy, fast simulation speed, minimal memory consumption, and robust simulation stability, satisfying the requirements for high-precision and long-time molecular simulations in large-scale organic systems. Force field (fiction) ; Molecular dynamics ; Computer science ; Intermolecular force ; Stability (learning theory) ; Field (mathematics) ; Scale (ratio) ; Range (aeronautics) ; Artificial intelligence ; Machine learning ; Materials science ; Computational chemistry ; Molecule ; Chemistry ; Physics ; Mathematics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Hongchen Ji | Anita Rágyanszki | René Fournier
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning Estimation of Reaction Energy Barriers Estimation ; Energy (signal processing) ; Computer science ; Artificial intelligence ; Economics ; Statistics ; Mathematics ; Management Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Mass Spectrometry Techniques and Applications
Authors: Takefumi Yamashita | Naoaki Miyamura | Shinnosuke Kawai
Source: The Journal of Chemical Physics (article, 2023-09-28)
Field: Materials Science — Materials Chemistry

Classification of the HCN isomerization reaction dynamics in Ar buffer gas via machine learning The effect of the presence of Ar on the isomerization reaction HCN ⇄ CNH is investigated via machine learning. After the potential energy surface function is developed based on the CCSD(T)/aug-cc-pVQZ level ab initio calculations, classical trajectory simulations are performed. Subsequently, with the aim of extracting insights into the reaction dynamics, the obtained reactivity, that is, whether the reaction occurs or not under a given initial condition, is learned as a function of the initial positions and momenta of all the atoms in the system. The prediction accuracy of the trained model is greater than 95%, indicating that machine learning captures the features of the phase space that affect reactivity. Machine learning models are shown to successfully reproduce reactivity boundaries without any prior knowledge of classical reaction dynamics theory. Subsequent analyses reveal that the Ar atom affects the reaction by displacing the effective saddle point. When the Ar atom is positioned close to the N atom (resp. the C atom), the saddle point shifts to the CNH (HCN) region, which disfavors the forward (backward) reaction. The results imply that analyses aided by machine learning are promising tools for enhancing the understanding of reaction dynamics. Saddle point ; Isomerization ; Reactivity (psychology) ; Reaction dynamics ; Atom (system on chip) ; Chemistry ; Potential energy surface ; Reaction mechanism ; Phase space ; Ab initio ; Computational chemistry ; Atomic physics ; Chemical physics ; Thermodynamics ; Physics ; Molecule ; Catalysis ; Computer science ; Mathematics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalytic Processes in Materials Science
Authors: Sina Stocker | Hyunwook Jung | Gábor Cśanyi | C. Franklin Goldsmith | Karsten Reuter | Johannes T. Margraf
Source: ChemRxiv (preprint, 2023-05-30)
Field: Materials Science — Materials Chemistry

Estimating Free Energy Barriers for Heterogeneous Catalytic Reactions with Machine Learning Potentials and Umbrella Integration Predicting the rate constants of elementary reaction steps is key for the computational modelling of catalytic processes. Within transition state theory (TST), this requires an accurate estimation of the corresponding free energy barriers. While sophisticated methods for estimating free energy differences exist, these typically require extensive (biased) molecular dynamics simulations that are computationally prohibitive with the first-principles electronic structure methods that are typically used in catalysis research. In this contribution, we show that machine-learning (ML) interatomic potentials can be trained in an automated iterative workflow to perform such free energy calculations at a much reduced computational cost as compared to a direct density-functional theory (DFT) based evaluation. For the decomposition of CHO on Rh(111), we find that thermal effects are substantial and lead to a decrease in the free energy barrier, which can be vanishingly small depending on the DFT functional used. This is in stark contrast to previously reported estimates based on a harmonic TST approximation, which predicted an increase of the barrier at elevated temperatures. Since CHO is the educt of the putative rate limiting reaction step in syngas conversion on Rh(111) and essential for the selectivity towards $\mathrm{C}_\mathrm{2+}$ oxygenates, our results call into question the reported mechanism established by microkinetic models. Density functional theory ; Oxygenate ; Transition state theory ; Catalysis ; Molecular dynamics ; Chemistry ; Transition state ; Computational chemistry ; Energy (signal processing) ; Statistical physics ; Reaction rate constant ; Chemical physics ; Thermodynamics ; Computer science ; Physics ; Quantum mechanics ; Kinetics Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Advanced Chemical Physics Studies