jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yang Liu | Hua Guo
Source: The Journal of Physical Chemistry A (article, 2023-10-10)
Field: Materials Science — Materials Chemistry

A Gaussian Process Based Δ-Machine Learning Approach to Reactive Potential Energy Surfaces The Gaussian process (GP) is an efficient nonparametric machine learning (ML) method. A distinct advantage of the GP is its ability to provide an estimate of statistical uncertainties. This is particularly useful in constructing high-dimensional potential energy surfaces (PESs) from ab initio data as it offers an optimal way to add new geometries to reduce the overall error. In this work, GP is employed in the context of Δ-machine learning (Δ-ML), in which a correction PES to an inaccurate low-level PES is constructed using a small number of high-level ab initio calculations. This new method is tested in three prototypical reactive systems, namely, the H + H<sub>2</sub> → H + H<sub>2</sub>, OH + H<sub>2</sub> → H<sub>2</sub>O + H, and H + CH<sub>4</sub> → H<sub>2</sub> + CH<sub>3</sub> reactions. The results show that the GP-based Δ-ML approach is more efficient than its direct application in constructing high-level PESs. We also compare the new method to a previously proposed neural-network-based Δ-ML approach [Liu and Li <i>J. Phys. Chem. Lett.</i> 2022, 13, 4729-4738]. The results indicate that the two Δ-ML methods have comparable efficiencies in constructing accurate PESs. Ab initio ; Gaussian process ; Context (archaeology) ; Artificial neural network ; Computer science ; Gaussian ; Process (computing) ; Potential energy ; Ab initio quantum chemistry methods ; Nonparametric statistics ; Energy (signal processing) ; Machine learning ; Work (physics) ; Artificial intelligence ; Algorithm ; Mathematical optimization ; Computational chemistry ; Chemistry ; Mathematics ; Physics ; Quantum mechanics ; Statistics ; Molecule Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Akshay Krishna Ammothum Kandy | Kevin Rossi | Alexis Raulin-Foissac | Gaétan Laurens | Julien Lam
Source: Physical review. B./Physical review. B (article, 2023-05-10)
Field: Materials Science — Materials Chemistry

Comparing transferability in neural network approaches and linear models for machine-learning interaction potentials Atomic simulations using machine learning interatomic potential (MLIP) have gained a lot of popularity owing to their accuracy in comparison to conventional empirical potentials. However, the transferability of MLIP to systems outside the training set poses a significant challenge. Here, we compare the transferability of three MLIP approaches: (i) neural network potentials (NNP), (ii) physical LassoLars interactions potential (PLIP) and (iii) linear potentials with Belher-Parrinello descriptors, trained over a small but diverse configuration of zinc oxide polymorphs. We compared the obtained models with density functional theory reference results for physical properties including bulk lattice parameters, surface energies, and vibrational density of states and showed the superiority of both NNP and PLIP models. However, the NNP model performed poorly when compared to the other two linear models for the structural optimization of nanoparticles and molecular dynamics simulation of liquid phases, which are systems outside the training set. While providing less accurate prediction for solid Zinc Oxides phases, both linear models appear more transferable than NNP when testing for nanoscale systems and liquid phases. Our results are finally rationalized by a combination of different statistical analysis including spread in force evaluation, information imbalance, convex hull calculation, and density in descriptor space. Interatomic potential ; Transferability ; Artificial neural network ; Convex hull ; Computer science ; Density functional theory ; Lattice (music) ; Set (abstract data type) ; Statistical physics ; Machine learning ; Artificial intelligence ; Molecular dynamics ; Chemistry ; Computational chemistry ; Regular polygon ; Physics ; Mathematics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Computational Drug Discovery Methods
Authors: Marcellus G.F. de Moraes | Fernando Arrais Romero Dias Lima | Paulo L.C. Lage | Maurício B. de Souza | Amaro Gomes Barreto | Argimiro R. Secchi
Source: Industrial & Engineering Chemistry Research (article, 2023-06-09)
Field: Materials Science — Materials Chemistry

Modeling and Predictive Control of Cooling Crystallization of Potassium Sulfate by Dynamic Image Analysis: Exploring Phenomenological and Machine Learning Approaches Representative mathematical modeling is essential for understanding the batch cooling crystallization processes. Efficient process design and operation are relevant to achieving high-quality criteria and minimizing variation between batches. This work first presents the modeling of batch cooling crystallization based on online dynamic image analysis. A flow-through microscope was used to track the temporal evolution of the crystal population. A population balance modeling (PBM) approach, parameter estimation, and validation were obtained for the batch cooling crystallization of potassium sulfate in water. The performed experiments provided new experimental data, giving dynamic information about the crystal size throughout each run. The kinetic model parameters for crystal nucleation and growth were estimated using a hybrid optimization algorithm, followed by the confidence region construction using a more exploratory particle swarm algorithm. In the parameter estimation framework, in addition to solute concentration, the first fourth-order moments computed throughout all experiments were included in the objective function. A linear size-dependent growth rate was found to capture well the dynamics of the potassium sulfate crystal size distribution. The experimental results evidenced that the crystal shape of potassium sulfate is predominantly constant, allowing the adequacy of the developed model. The validated PBM was also employed as a digital twin of the crystallization process to develop a machine-learning-based control for the process. Then, a surrogate model based on a recurrent neural network, called an echo state network (ESN), was applied in a nonlinear model predictive controller approach (ESN-NMPC). The ESN model could predict the moments of the population balance model up to five steps (5 min) forward. The ESN-NMPC achieved the desired control scenarios for the crystal size and its coefficient of variation. Its performance was comparable to the controller that uses the PBM as the internal model (PB-NMPC). Crystallization ; Population ; Particle swarm optimization ; Model predictive control ; Nucleation ; Computer science ; Biological system ; Artificial intelligence ; Control theory (sociology) ; Machine learning ; Chemistry ; Control (management) Crystallization and Solubility Studies ; Advanced Control Systems Optimization ; Neural Networks and Applications
Authors: G. Fotos | Andrew Campbell | Paul Murray | Evgenia Yakushina
Source: Journal of Materials Science (article, 2023-09-01)
Field: Materials Science — Materials Chemistry

Deep learning enhanced Watershed for microstructural analysis using a boundary class semantic segmentation Abstract The mechanical properties of the materials are determined by the size and morphology of fine microscopic features. Quantitative microstructural analysis is a key factor to establish the correlation between the mechanical properties and the thermomechanical treatment under which material condition has been achieved. As such, microstructural analysis is a very important and complex task within the manufacturing sector. Published standards are used for metallographic analysis but typically involve extensive manual interpretation of grain boundaries, resulting in measurements that are slow to produce, difficult to repeat and highly subjective. Computer vision and the evolution of artificial intelligence in the past decade can offer solutions to such problems. Deep learning and digital image processing techniques allow digital microstructural analysis to be automated using a fast and repeatable method. This paper proposes a novel boundary class semantic segmentation approach ( BCSS ) to identify each phase of the microstructure and additionally estimate the location of the grain boundaries. The BCSS is then combined with more traditional segmentation techniques based on the Watershed Transform to improve the identification and measurement of each feature within the microstructure using a new, hybrid automated digital microstructure analysis approach. The new method is validated on a published dataset of two-phase titanium alloy microstructure pictures captured using a scanning electron microscope. Measurements match the level of accuracy of accepted manual standards, and the method is demonstrated to be more reliable than other automated approaches. The influence of the subjective nature of manual labelling, required to train the proposed network, is also evaluated. Microstructure ; Segmentation ; Materials science ; Artificial intelligence ; Computer science ; Grain boundary ; Image processing ; Pattern recognition (psychology) ; Metallurgy ; Image (mathematics) Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Non-Destructive Testing Techniques
Authors: Damien B. Wilburn | Ariana E. Shannon | Vic Spicer | Alicia Richards | Darien Yeung | Danielle L. Swaney | Oleg V. Krokhin | Brian C. Searle
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-06-02)
Field: Chemistry — Spectroscopy

Deep learning from harmonized peptide libraries enables retention time prediction of diverse post translational modifications In proteomics experiments, peptide retention time (RT) is an orthogonal property to fragmentation when assessing detection confidence. Advances in deep learning enable accurate RT prediction for any peptide from sequence alone, including those yet to be experimentally observed. Here we present Chronologer, an open-source software tool for rapid and accurate peptide RT prediction. Using new approaches to harmonize and false-discovery correct across independently collected datasets, Chronologer is built on a massive database with >2.2 million peptides including 10 common post-translational modification (PTM) types. By linking knowledge learned across diverse peptide chemistries, Chronologer predicts RTs with less than two-thirds the error of other deep learning tools. We show how RT for rare PTMs, such as OGlcNAc, can be learned with high accuracy using as few as 10-100 example peptides in newly harmonized datasets. This iteratively updatable workflow enables Chronologer to comprehensively predict RTs for PTM-marked peptides across entire proteomes. Workflow ; Computer science ; Deep learning ; Proteome ; Peptide ; Fragmentation (computing) ; Artificial intelligence ; Proteomics ; Computational biology ; Machine learning ; Data mining ; Bioinformatics ; Chemistry ; Database ; Biology ; Biochemistry Advanced Proteomics Techniques and Applications ; vaccines and immunoinformatics approaches ; Machine Learning in Bioinformatics
Authors: Daniele Rapetti | Massimo Delle Piane | Matteo Cioni | Daniela Polino | Riccardo Ferrando | Giovanni M. Pavan
Source: Communications Chemistry (article, 2023-07-05)
Field: Materials Science — Materials Chemistry

Machine learning of atomic dynamics and statistical surface identities in gold nanoparticles It is known that metal nanoparticles (NPs) may be dynamic and atoms may move within them even at fairly low temperatures. Characterizing such complex dynamics is key for understanding NPs' properties in realistic regimes, but detailed information on, e.g., the stability, survival, and interconversion rates of the atomic environments (AEs) populating them are non-trivial to attain. In this study, we decode the intricate atomic dynamics of metal NPs by using a machine learning approach analyzing high-dimensional data obtained from molecular dynamics simulations. Using different-shape gold NPs as a representative example, an AEs' dictionary allows us to label step-by-step the individual atoms in the NPs, identifying the native and non-native AEs and populating them along the MD simulations at various temperatures. By tracking the emergence, annihilation, lifetime, and dynamic interconversion of the AEs, our approach permits estimating a "statistical equivalent identity" for metal NPs, providing a comprehensive picture of the intrinsic atomic dynamics that shape their properties. Colloidal gold ; Dynamics (music) ; Statistical learning ; Nanotechnology ; Nanoparticle ; Surface (topology) ; Materials science ; Computer science ; Artificial intelligence ; Physics ; Mathematics ; Geometry Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Louai Mahdi Maghrabi | Nirpendra Singh | Kyriaki Polychronopoulou
Source: International Journal of Hydrogen Energy (article, 2023-10-23)
Field: Materials Science — Materials Chemistry

A mini-review on the MXenes capacity to act as electrocatalysts for the hydrogen evolution reaction MXenes ; Density functional theory ; Materials science ; Gibbs free energy ; Nanotechnology ; Hydrogen ; Hydrogen economy ; Hydrogen fuel ; Chemistry ; Computational chemistry ; Thermodynamics ; Physics MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques ; 2D Materials and Applications
Authors: Brian Tsui | Evan Calabrese | Greg Zaharchuk | Andreas M. Rauschecker
Source: Journal of Magnetic Resonance Imaging (review, 2023-10-31)
Field: Materials Science — Materials Chemistry

Reducing Gadolinium Contrast With Artificial Intelligence Gadolinium contrast is an important agent in magnetic resonance imaging (MRI), particularly in neuroimaging where it can help identify blood-brain barrier breakdown from an inflammatory, infectious, or neoplastic process. However, gadolinium contrast has several drawbacks, including nephrogenic systemic fibrosis, gadolinium deposition in the brain and bones, and allergic-like reactions. As computer hardware and technology continues to evolve, machine learning has become a possible solution for eliminating or reducing the dose of gadolinium contrast. This review summarizes the clinical uses of gadolinium contrast, the risks of gadolinium contrast, and state-of-the-art machine learning methods that have been applied to reduce or eliminate gadolinium contrast administration, as well as their current limitations, with a focus on neuroimaging applications. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 1. Gadolinium ; Nephrogenic systemic fibrosis ; Magnetic resonance imaging ; Neuroimaging ; Medicine ; Contrast (vision) ; Radiology ; Computer science ; Artificial intelligence ; Materials science Lanthanide and Transition Metal Complexes ; Medical Imaging Techniques and Applications ; Advanced MRI Techniques and Applications
Authors: Qiyu Zeng | Bo Chen | Shen Zhang | Dongdong Kang | Handong Wang | Xiaoxiang Yu | Jiayu Dai
Source: npj Computational Materials (article, 2023-11-25)
Field: Materials Science — Materials Chemistry

Full-scale ab initio simulations of laser-driven atomistic dynamics Abstract The coupling of excited states and ionic dynamics is the basic and challenging point for the materials response at extreme conditions. In the laboratory, the intense laser produces transient nature and complexity with highly nonequilibrium states, making it extremely difficult and interesting for both experimental measurements and theoretical methods. With the inclusion of laser-excited states, we extend an ab initio method into the direct simulations of whole laser-driven microscopic dynamics from solid to liquid. We construct the framework of combining the electron-temperature-dependent deep neural-network potential energy surface with a hybrid atomistic-continuum approach, controlling non-adiabatic energy exchange and atomistic dynamics, which enables consistent interpretation of experimental data. By large-scale ab initio simulations, we demonstrate that the nonthermal effects introduced by hot electrons play a dominant role in modulating the lattice dynamics, thermodynamic pathway, and structural transformation. We highlight that the present work provides a path to realistic computational studies of laser-driven processes, thus bridging the gap between experiments and simulations. Ab initio ; Excited state ; Adiabatic process ; Non-equilibrium thermodynamics ; Molecular dynamics ; Laser ; Statistical physics ; Materials science ; Physics ; Atomic physics ; Thermodynamics ; Quantum mechanics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; High-pressure geophysics and materials
Authors: C. Hansen | Gary Whelan | Jacob Hochhalter
Source: International Journal of Fatigue (article, 2023-10-31)
Field: Materials Science — Metals and Alloys

Interpretable machine learning for microstructure-dependent models of fatigue indicator parameters Microscale chemistry ; Workflow ; Microstructure ; Representation (politics) ; Symbolic regression ; Finite element method ; Computer science ; Artificial intelligence ; Machine learning ; Limit (mathematics) ; Genetic programming ; Structural engineering ; Materials science ; Engineering ; Mathematics Hydrogen embrittlement and corrosion behaviors in metals ; Metallurgy and Material Forming ; High Temperature Alloys and Creep
Authors: Nicholas S. M. Herringer | Siva Dasetty | Diya Gandhi | Junhee Lee | Andrew L. Ferguson
Source: Journal of Chemical Theory and Computation (article, 2023-12-27)
Field: Materials Science — Materials Chemistry

Permutationally Invariant Networks for Enhanced Sampling (PINES): Discovery of Multimolecular and Solvent-Inclusive Collective Variables The typically rugged nature of molecular free-energy landscapes can frustrate efficient sampling of the thermodynamically relevant phase space due to the presence of high free-energy barriers. Enhanced sampling techniques can improve phase space exploration by accelerating sampling along particular collective variables (CVs). A number of techniques exist for the data-driven discovery of CVs parametrizing the important large-scale motions of the system. A challenge to CV discovery is learning CVs invariant to the symmetries of the molecular system, frequently rigid translation, rigid rotation, and permutational relabeling of identical particles. Of these, permutational invariance has proved a persistent challenge in frustrating the data-driven discovery of multimolecular CVs in systems of self-assembling particles and solvent-inclusive CVs for solvated systems. In this work, we integrate permutation invariant vector (PIV) featurizations with autoencoding neural networks to learn nonlinear CVs invariant to translation, rotation, and permutation and perform interleaved rounds of CV discovery and enhanced sampling to iteratively expand the sampling of configurational phase space and obtain converged CVs and free-energy landscapes. We demonstrate the permutationally invariant network for enhanced sampling (PINES) approach in applications to the self-assembly of a 13-atom argon cluster, association/dissociation of a NaCl ion pair in water, and hydrophobic collapse of a C<sub>45</sub>H<sub>92</sub> <i>n</i>-pentatetracontane polymer chain. We make the approach freely available as a new module within the PLUMED2 enhanced sampling libraries. Invariant (physics) ; Computer science ; Statistical physics ; Sampling (signal processing) ; Energy landscape ; Scale invariance ; Phase space ; Homogeneous space ; Chemical physics ; Physics ; Mathematics ; Quantum mechanics ; Thermodynamics ; Geometry ; Computer vision Machine Learning in Materials Science ; Protein Structure and Dynamics ; Hydrocarbon exploration and reservoir analysis
Authors: Kohei Sase | Yasushi Shibuta
Source: Acta Materialia (article, 2023-08-25)
Field: Materials Science — Materials Chemistry

Prediction of microstructure evolution at the atomic scale by deep generative model in combination with recurrent neural networks A novel method to predict multi-atom cooperative phenomena at atomic scale is proposed based on a deep generative model in combination with recurrent neural network. The variational autoencoder (VAE) model successfully identifies three different crystal orientations in the polycrystalline nickel by using 10-dimensional latent variables and restores the image of atomic configurations reflecting the crystal orientation of each grain. Moreover, microstructure evolution of the polycrystalline iron is successfully predicted through three steps: dimensionality reduction of atomic coordinates from the MD simulation using the encoder, time evolution of latent variables using the long short-term memory (LSTM) model, and data restoration using the decoder. We successfully predict the microstructure that cannot be reproduced on the time scale of MD simulations by decoding latent variables in the future time from the LSTM model. This is a new attempt of acceleration of the MD simulation that differs significantly from conventional acceleration methods. Autoencoder ; Artificial neural network ; Materials science ; Microstructure ; Crystallite ; Artificial intelligence ; Acceleration ; Dimensionality reduction ; Encoder ; Recurrent neural network ; Statistical physics ; Algorithm ; Computer science ; Physics ; Metallurgy ; Classical mechanics Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Emmanuel Ren | François‐Xavier Coudert
Source: Chemistry of Materials (article, 2023-08-30)
Field: Chemistry — Inorganic Chemistry

Enhancing Gas Separation Selectivity Prediction through Geometrical and Chemical Descriptors Adsorption-based techniques for gas separation using nanoporous materials are widely used and hold a promising future, but systematic identification of the best-performing materials for a given application is still an open problem. For that task, we need to estimate selectivity at different operating conditions (temperature and pressure) on a large set of nanoporous structures. To this aim, we have developed a machine-learning-assisted screening process based on a fast grid calculation of interaction energies, in addition to newly designed geometrical descriptors to predict ambient-pressure selectivity. As a proof of concept, we tested our methodology for the separation of a 20:80 xenon/krypton mixture at 298 K and 1 atm in the nanoporous materials of the CoRE MOF 2019 database. Based on a train/test split of the data set, our model is promising with an RMSE of 2.5 on the ambient-pressure selectivity values of the test set and 0.06 on the log10 of the selectivity. This method can thence be used to preselect the best performing materials for a more thorough investigation. Nanoporous ; Selectivity ; Test set ; Set (abstract data type) ; Computer science ; Adsorption ; Chemistry ; Materials science ; Machine learning ; Nanotechnology ; Physical chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Catalytic Processes in Materials Science
Authors: Hongjian Chen | Dingwang Yuan | Hua-Yun Geng | Wangyu Hu | Bowen Huang
Source: Computational Materials Science (article, 2023-07-27)
Field: Materials Science — Materials Chemistry

Development of a machine-learning interatomic potential for uranium under the moment tensor potential framework Density functional theory ; Uranium ; Interatomic potential ; Tensor (intrinsic definition) ; Molecular dynamics ; Moment (physics) ; Lattice (music) ; Materials science ; Statistical physics ; Chemistry ; Thermodynamics ; Physics ; Computational chemistry ; Quantum mechanics ; Mathematics Nuclear Materials and Properties ; Machine Learning in Materials Science ; Radioactive element chemistry and processing