jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shengtong Zhang | Satyajit Mojumder | Wing Kam Liu | Wei Chen | Daniel W. Apley
Source: APL Machine Learning (article, 2023-09-01)
Field: Materials Science — Materials Chemistry

Accelerated and interpretable prediction of local properties in composites The localized stress and strain field simulation results are critical for understanding the mechanical properties of materials, such as strength and toughness. However, applying off-the-shelf machine learning or deep learning methods to a digitized microstructure restricts the image samples to be of a fixed size and also lacks interpretability. Additionally, existing methods that utilize deep learning models to solve boundary value problems require retraining the model for each set of boundary conditions. To address these limitations, we propose a customized Pixel-Wise Convolutional Neural Network (PWCNN) to make fast predictions of stress and strain fields pixel-by-pixel under different loading conditions and for a wide range of composite microstructures of any size (e.g., much larger or smaller than the sample on which the PWCNN is trained). Through numerical experiments, we show that our PWCNN model serves as an alternative approach to numerical solution methods, such as finite element analysis, but is computationally more efficient, and the prediction errors on the test microstructure are around 5%. Moreover, we also propose an interpretable machine learning framework to facilitate the scientific discovery of why certain microstructures have better or worse performance than others, which has important implications in the design of composite microstructures in advanced manufacturing. Interpretability ; Convolutional neural network ; Computer science ; Pixel ; Artificial intelligence ; Microstructure ; Deep learning ; Artificial neural network ; Finite element method ; Machine learning ; Materials science ; Structural engineering ; Composite material ; Engineering Machine Learning in Materials Science ; Composite Material Mechanics ; Non-Destructive Testing Techniques
Authors: Chen Niu | Hans Paul Schreckenbach | Puyan Mojabi
Source:  (article, 2023-07-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep Learning Based Metasurface Synthesis from Far-Field Masks A neural network is developed to infer the required tangential fields on the output face of the metasurface so as to meet mask-based far-field (FF) design constraints. Full-wave simulation is performed to evaluate the performance of omega-bianisotropic metasurfaces designed based on this approach. Computer science ; Near and far field ; Field (mathematics) ; Materials science ; Optics ; Physics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Millimeter-Wave Propagation and Modeling
Authors: Daniel H. Ess | Shusen Chen | Zack Meyer | Brendan Jensen | Alex Kraus | Allison Lambert
Source: ChemRxiv (preprint, 2023-08-17)
Field: Materials Science — Materials Chemistry

ReaLigands: A Ligand Library Cultivated from Experiment and Intended for Molecular Computational Catalyst Design Computational catalyst design requires identification of a metal and ligand that together result in the desired reaction reactivity and/or selectivity. A major impediment to translating computational designs to experiment is evaluating ligands that are likely to be synthesized. Here we provide a solution to this impediment with our ReaLigands library that contains >30,000 monodentate, bidentate (didentate), tridentate, and larger ligands cultivated by dismantling experimentally reported crystal structures. Individual ligands from mononuclear crystal structures were identified using a modified depth-first search algorithm and charge was assigned using a machine learning model based on quantum-chemical calculated features. In the library ligands are sorted based on direct ligand-to-metal atomic connections and on denticity. Representative principal component analysis (PCA) and uniform manifold approximation and projection (UMAP) analyses were used to analyze several tridentate ligand categories, which revealed both the diversity of ligands and connections between ligand categories. We also demonstrated the utility of this library by implementing it with our building and optimization tools, which resulted in the very rapid generation of barriers for 750 bidentate ligands for Rh-hydride ethylene migratory insertion Denticity ; Ligand (biochemistry) ; Reactivity (psychology) ; Chemistry ; Metal ; Catalysis ; Molecule ; Combinatorial chemistry ; Computer science ; Crystal structure ; Computational chemistry ; Crystallography ; Organic chemistry Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; X-ray Diffraction in Crystallography
Authors: Nicholas Gao | Stephan Günnemann
Source: arXiv (Cornell University) (preprint, 2023-02-08)
Field: Materials Science — Materials Chemistry

Generalizing Neural Wave Functions Recent neural network-based wave functions have achieved state-of-the-art accuracies in modeling ab-initio ground-state potential energy surface. However, these networks can only solve different spatial arrangements of the same set of atoms. To overcome this limitation, we present Graph-learned orbital embeddings (Globe), a neural network-based reparametrization method that can adapt neural wave functions to different molecules. Globe learns representations of local electronic structures that generalize across molecules via spatial message passing by connecting molecular orbitals to covalent bonds. Further, we propose a size-consistent wave function Ansatz, the Molecular orbital network (Moon), tailored to jointly solve Schrödinger equations of different molecules. In our experiments, we find Moon converging in 4.5 times fewer steps to similar accuracy as previous methods or to lower energies given the same time. Further, our analysis shows that Moon's energy estimate scales additively with increased system sizes, unlike previous work where we observe divergence. In both computational chemistry and machine learning, we are the first to demonstrate that a single wave function can solve the Schrödinger equation of molecules with different atoms jointly. Ansatz ; Wave function ; Artificial neural network ; Atomic orbital ; Function (biology) ; Computer science ; Graph ; Ground state ; Statistical physics ; Set (abstract data type) ; Ab initio ; Physics ; Algorithm ; Quantum mechanics ; Theoretical computer science ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Molecular spectroscopy and chirality
Authors: Véronique Van Speybroeck
Source: Proceedings of the National Academy of Sciences (article, 2023-12-27)
Field: Materials Science — Materials Chemistry

Following the dynamics of industrial catalysts under operando conditions Catalytic reactions taking place in industrial processes are often performed under extreme conditions of temperatures and pressures.A typical example is the Haber-Bosch process to industrially synthesize ammonia from nitrogen and hydrogen which operates under reaction pressures from 10 to 15 MPa and temperatures higher than 400 C (1).Following the dynamics of heterogeneous catalysts under such extreme conditions is highly challenging both from experimental and theoretical point of view.In their paper, Bonati et al. give unique molecular insights into the dynamics, adsorption, and bond breakage of the N 2 molecule when interacting with the (111) iron surface at high temperatures relevant for the Haber-Bosch catalytic system (2).The simulations reveal that the surface is much more dynamic than anticipated from low-temperature experiments or simulations.Active sites are continuously formed and disrupted, and this behavior is instrumental for driving the catalytic process.To follow the weakening of the nitrogen-nitrogen bond, the degree of charge transfer from the metallic surface to the triple bond was followed during various steps of the catalytic process.The study of Bonati et al. is an important proof-of-concept study, showcasing that reaction mechanisms may be highly dependent on the reaction conditions and that an evaluation of the dynamics of the system at industrially relevant conditions is of utmost importance to obtain molecular insights.Such insights can not be obtained from low-temperature investigations.It is notoriously difficult to simulate the dynamics of industrially relevant catalytic reactions under operating conditions.Hence, Bonati et al. had to combine various innovations in the field of machine learning and enhanced sampling molecular dynamics to follow the adsorption and reaction on the fly at operating conditions during sufficiently long time scales.A summary of some essential ingredients of their workflow is schematically shown in Fig. 1 and discussed further below.The impact of the methodological advances presented in their study is of much greater importance than the specific case study discussed in the PNAS paper and opens perspectives to follow industrially relevant catalytic reactions on the fly at the conditions where the catalyst does the work.In their study, Bonati et al. studied the impact of temperature on a crucial step of the Haber-Bosch process (1).In the broader field of heterogeneous catalysis, also other process Catalysis ; Dynamics (music) ; Chemistry ; Environmental science ; Biochemical engineering ; Biochemistry ; Engineering ; Physics Catalytic Processes in Materials Science ; Ammonia Synthesis and Nitrogen Reduction ; Catalysis and Oxidation Reactions
Authors: Maosheng 茂生 Hao 郝 | Pengfei 鹏飞 Guan 管
Source: Chinese Physics B (article, 2023-05-25)
Field: Materials Science — Materials Chemistry

An artificial neural network potential for uranium metal at low pressures Based on machine learning, the high-dimensional fitting of potential energy surfaces under the framework of first principles provides density-functional accuracy of atomic interaction potential for high-precision and large-scale simulation of alloy materials. In this paper, we obtained the high-dimensional neural network (NN) potential function of uranium metal by training a large amount of first-principles calculated data. The lattice constants of uranium metal with different crystal structures, the elastic constants, and the anisotropy of lattice expansion of alpha-uranium obtained based on this potential function are highly consistent with first-principles calculation or experimental data. In addition, the calculated formation energy of vacancies in alpha- and beta-uranium also matches the first-principles calculation. The calculated site of the most stable self-interstitial and its formation energy is in good agreement with the findings from density functional theory (DFT) calculations. These results show that our potential function can be used for further large-scale molecular dynamics simulation studies of uranium metal at low pressures, and provides the basis for further construction of potential model suitable for a wide range of pressures. Uranium ; Anisotropy ; Density functional theory ; Materials science ; Lattice constant ; Lattice (music) ; Metal ; Embedded atom model ; Potential energy ; Atomic units ; Molecular dynamics ; Thermodynamics ; Atomic physics ; Physics ; Computational chemistry ; Chemistry ; Metallurgy ; Quantum mechanics Nuclear Materials and Properties ; Machine Learning in Materials Science ; Nuclear Physics and Applications
Authors: Daisuke Sugizaki | Hiromasa Kaneko
Source: Industrial & Engineering Chemistry Research (article, 2023-10-17)
Field: Materials Science — Materials Chemistry

Design of Ammonia Borane Dehydrogenation Catalysts Using Previous Study Data, Public Data, and Machine Learning Owing to the hazards caused by CO2 emissions from fossil fuels, hydrogen has garnered attention as a next-generation clean energy source, as it does not emit CO2. However, handling hydrogen is challenging, and hydrogen storage materials are required for its use as an energy source. Although, ammonia borane (NH3BH3) has attracted attention as a hydrogen storage material owing to its ease of synthesis, remarkably high H2 storage capacity, etc., issues such as improving the amount and rate of hydrogen production (HP) and developing low-cost and high-efficiency catalysts remain. Herein, two NH3BH3 dehydrogenation reactions are investigated, and mathematical models are constructed to predict the amount and rate of HP using information about the catalyst, support, and experimental conditions for each reaction. Finally, catalysts with higher amounts and rates of HP than existing catalysts are successfully designed based on inverse analyses of the constructed models, which facilitates catalyst development. Ammonia borane ; Dehydrogenation ; Hydrogen storage ; Catalysis ; Hydrogen ; Hydrogen production ; Process engineering ; Ammonia ; Hydrogen fuel ; Ammonia production ; Renewable energy ; Chemical engineering ; Chemistry ; Environmental science ; Materials science ; Organic chemistry ; Engineering Hydrogen Storage and Materials ; MXene and MAX Phase Materials ; Ammonia Synthesis and Nitrogen Reduction
Authors: Konstantin Karandashev | Jan Weinreich | Stefan Heinen | Daniel Jose Arismendi Arrieta | Guido Falk von Rudorff | Kersti Hermansson | O. Anatole von Lilienfeld
Source: Journal of Chemical Theory and Computation (article, 2023-11-27)
Field: Materials Science — Materials Chemistry

Evolutionary Monte Carlo of QM Properties in Chemical Space: Electrolyte Design Optimizing a target function over the space of organic molecules is an important problem appearing in many fields of applied science but also a very difficult one due to the vast number of possible molecular systems. We propose an evolutionary Monte Carlo algorithm for solving such problems which is capable of straightforwardly tuning both exploration and exploitation characteristics of an optimization procedure while retaining favorable properties of genetic algorithms. The method, dubbed MOSAiCS (<b>M</b>etropolis <b>O</b>ptimization by <b>S</b>ampling <b>A</b>daptively <b>i</b>n <b>C</b>hemical <b>S</b>pace), is tested on problems related to optimizing components of battery electrolytes, namely, minimizing solvation energy in water or maximizing dipole moment while enforcing a lower bound on the HOMO-LUMO gap; optimization was carried out over sets of molecular graphs inspired by QM9 and Electrolyte Genome Project (EGP) data sets. MOSAiCS reliably generated molecular candidates with good target quantity values, which were in most cases better than the ones found in QM9 or EGP. While the optimization results presented in this work sometimes required up to 10<sup>6</sup> QM calculations and were thus feasible only thanks to computationally efficient <i>ab initio</i> approximations of properties of interest, we discuss possible strategies for accelerating MOSAiCS using machine learning approaches. Monte Carlo method ; Computer science ; MNIST database ; Chemical space ; Space (punctuation) ; Genetic algorithm ; Solvation ; Mathematical optimization ; Artificial intelligence ; Chemistry ; Physics ; Molecule ; Machine learning ; Mathematics ; Artificial neural network ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Janice Lan | Aini Palizhati | Muhammed Shuaibi | Brandon C. Wood | Brook Wander | Abhishek Das | Matt Uyttendaele | C. Zitnick | Zachary W. Ulissi
Source: Research Square (preprint, 2023-04-17)
Field: Materials Science — Materials Chemistry

AdsorbML: A Leap in Efficiency for Adsorption Energy Calculations using Generalizable Machine Learning Potentials Abstract Computational catalysis is playing an increasingly significant role in the design of catalysts across a wide range of applications. A common task for many computational methods is the need to accurately compute the minimum binding energy — the adsorption energy — for an adsorbate and a catalyst surface of interest. Traditionally, the identification of low energy adsorbate-surface configurations relies on heuristic methods and researcher intuition. As the desire to perform high-throughput screening increases, it becomes challenging to use heuristics and intuition alone. In this paper, we demonstrate machine learning potentials can be leveraged to more accurately and efficiently identify low energy adsorbate-surface configurations. Our DFT verified algorithm provides a spectrum of performance trade-offs, with one balanced option finding the lowest energy configuration, within a 0.1 eV threshold, 86.33% of the time, while achieving a 1331x speedup in computation. To standardize benchmarking, we introduce the Open Catalyst Dense dataset containing nearly 1,000 diverse surfaces and 85,658 unique configurations. Heuristics ; Intuition ; Benchmarking ; Computation ; Speedup ; Computer science ; Adsorption ; Heuristic ; Potential energy surface ; Imperfect ; Efficient energy use ; Artificial intelligence ; Algorithm ; Chemistry ; Physics ; Parallel computing ; Molecule ; Engineering ; Quantum mechanics ; Physical chemistry Machine Learning in Materials Science ; Topic Modeling
Authors: Dávid Péter Kovács | Ilyes Batatia | Eszter Sára Arany | Gábor Csányi
Source: arXiv (Cornell University) (preprint, 2023-05-23)
Field: Materials Science — Materials Chemistry

Evaluation of the MACE Force Field Architecture: from Medicinal Chemistry to Materials Science The MACE architecture represents the state of the art in the field of machine learning force fields for a variety of in-domain, extrapolation and low-data regime tasks. In this paper, we further evaluate MACE by fitting models for published benchmark datasets. We show that MACE generally outperforms alternatives for a wide range of systems from amorphous carbon, universal materials modelling, and general small molecule organic chemistry to large molecules and liquid water. We demonstrate the capabilities of the model on tasks ranging from constrained geometry optimisation to molecular dynamics simulations and find excellent performance across all tested domains. We show that MACE is very data efficient, and can reproduce experimental molecular vibrational spectra when trained on as few as 50 randomly selected reference configurations. We further demonstrate that the strictly local atom-centered model is sufficient for such tasks even in the case of large molecules and weakly interacting molecular assemblies. Extrapolation ; Mace ; Ranging ; Computer science ; Force field (fiction) ; Benchmark (surveying) ; Molecule ; Field (mathematics) ; Amorphous solid ; Molecular dynamics ; Statistical physics ; Chemical physics ; Chemistry ; Physics ; Computational chemistry ; Artificial intelligence ; Mathematics ; Organic chemistry Machine Learning in Materials Science ; Mass Spectrometry Techniques and Applications ; Various Chemistry Research Topics
Authors: Shuyan Zhang | Jie Gong | Sharon Chu | Daniel Z. Xiao | B. Reeja‐Jayan | Alan J. H. McGaughey
Source: APL Machine Learning (article, 2023-06-01)
Field: Materials Science — Materials Chemistry

Pair distribution function analysis for oxide defect identification through feature extraction and supervised learning Feature extraction and a neural network model are applied to predict defect types and concentrations in experimental anatase TiO2 samples. A dataset of TiO2 structures with vacancies and interstitials of oxygen and titanium is built, and the structures are relaxed using energy minimization. The features of the calculated pair distribution functions (PDFs) of these defected structures are extracted using linear methods (principal component analysis and non-negative matrix factorization) and non-linear methods (autoencoder and convolutional neural network). The extracted features are used as inputs to a neural network that maps feature weights to the concentration of each defect type. The performance of this machine learning pipeline is validated by predicting defect concentrations based on experimentally measured TiO2 PDFs and comparing the results to brute-force predictions. A physics-based initialization of the autoencoder has the highest accuracy in predicting defect concentrations. This model incorporates physical interpretability and predictability of material structures, enabling a more efficient characterization process with scattering data. Autoencoder ; Artificial neural network ; Convolutional neural network ; Feature extraction ; Pattern recognition (psychology) ; Principal component analysis ; Artificial intelligence ; Computer science ; Feature (linguistics) ; Materials science ; Biological system X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Metallurgical Processes and Thermodynamics
Authors: Sarah Constantin | Matthew Putman | Valerie Bordelanne
Source:  (article, 2023-03-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Artificial intelligence process control: deep reinforcement learning for Ga2O3 wafer production Process improvement for the manufacture of effective high performance gallium oxide (Ga2O3) based semiconductor devices is imperative in consolidating Ga2O3 as a singularly promising material for cost effective, mass-producible, and robust manufacturing. Other wide bandgap silicon alternatives (i.e., SiC and GaN) are impeded by high costs and complicated, time-consuming adjustments. Beginning with a bandgap of 4.7eV, Ga2O3 offers an unparalleled solution when growth parameters are tuned and controlled using deep reinforcement learning agents. Ga2O3 wafer production employs (non-exclusively) the scalable and cost-effective Czochralski method for ingot growth and MOCVD process for epitaxy growth, making it a viable candidate for high volume commercial radiofrequency device manufacture. As crystal quality and electron transport depend on reactor temperature, vertical gas and precursors flows, chamber pressure, and a host of kinetic parameters during growth, it follows that the configuration space for Ga2O3 deposition is expansive and costly to explore. Enhancing growth rate of Ga2O3 films without compromising crystal quality can be accomplished through implementing insights offered by ancillary deep learning models. Artificial intelligence techniques that take programmed reactor settings, sensor-read environmental conditions, resulting crystallographic defectivity, and overall outgoing quality as inputs can infer processing improvements upstream using neural networks trained by skilled engineers. My stipulations call for a hardware accelerated deep learning controller (DLC) that digests the multimedia output of reactors, MOCVD systems, and metrology tools to optimize Ga2O3 crystal quality and ultimately increase die yields, reduce waste, accelerate product development, decrease time to market, and eliminate need for labor-exhaustive testing. Wafer ; Materials science ; Process engineering ; Computer science ; Process control ; Metalorganic vapour phase epitaxy ; Nanotechnology ; Artificial intelligence ; Process (computing) ; Epitaxy ; Engineering Ga2O3 and related materials ; ZnO doping and properties ; Electronic and Structural Properties of Oxides