jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Richard J. Sadus
Source: Journal of Chemical & Engineering Data (article, 2023-12-19)
Field: Materials Science — Materials Chemistry

Molecular Simulation Meets Machine Learning Molecular simulation that encompasses both Monte Carlo and molecular dynamics methods, coupled with ever-increasing computing power, has provided very valuable insights linking the nature of intermolecular interactions directly to the macroscopic properties of materials. In contrast, machine learning can be used to predict molecular properties by finding patterns in data rather than directly evaluating molecular interactions. Suitable machine learning approaches for molecules include supervised, unsupervised, reinforcement, and deep learning, with the latter commonly using neural net algorithms. There is considerable overlap in the scope of application of the two approaches, which can be combined for maximum benefit. Careful integration of machine learning with molecular simulation also means that the hypothesis-centered approach of the latter can be both maintained and enhanced. Using machine learning with molecular simulation offers gains in computational efficiency, predictive capabilities, and generalizability. However, the black-box nature of machine learning provides challenges of interpretability and transparency. Data quality, generalizability, and peer review are also issues that require attention. Nonetheless, the combination of the two approaches offers the possibility of greatly expanded predictive capabilities. Interpretability ; Generalizability theory ; Artificial intelligence ; Machine learning ; Computer science ; Artificial neural network ; Transparency (behavior) ; Reinforcement learning ; Monte Carlo method ; Psychology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Caglar Tamur | Shaofan Li | Danielle Zeng
Source: Polymers (article, 2023-10-29)
Field: Materials Science — Materials Chemistry

Artificial Neural Networks for Predicting Mechanical Properties of Crystalline Polyamide12 via Molecular Dynamics Simulations Predicting material properties of 3D printed polymer products is a challenge in additive manufacturing due to the highly localized and complex manufacturing process. The microstructure of such products is fundamentally different from the ones obtained by using conventional manufacturing methods, which makes the task even more difficult. As the first step of a systematic multiscale approach, in this work, we have developed an artificial neural network (ANN) to predict the mechanical properties of the crystalline form of Polyamide12 (PA12) based on data collected from molecular dynamics (MD) simulations. Using the machine learning approach, we are able to predict the stress–strain relations of PA12 once the macroscale deformation gradient is provided as an input to the ANN. We have shown that this is an efficient and accurate approach, which can provide a three-dimensional molecular-level anisotropic stress–strain relation of PA12 for any macroscale mechanics model, such as finite element modeling at arbitrary quadrature points. This work lays the foundation for a multiscale finite element method for simulating semicrystalline polymers, which will be published as a separate study. Artificial neural network ; Materials science ; Finite element method ; Molecular dynamics ; Polymer ; Biological system ; Anisotropy ; Multiscale modeling ; Work (physics) ; Microstructure ; Stress–strain curve ; Computer science ; Deformation (meteorology) ; Process (computing) ; Statistical physics ; Mechanical engineering ; Artificial intelligence ; Composite material ; Structural engineering ; Physics ; Computational chemistry ; Engineering Machine Learning in Materials Science ; Injection Molding Process and Properties ; Polymer crystallization and properties
Authors: Zhao Siyue | Na Li | Guangtong Hai | Zhigang Zhang
Source: AIP Advances (article, 2023-02-01)
Field: Materials Science — Materials Chemistry

An improved composition design method for high-performance copper alloys based on various machine learning models The preparation of high-performance copper alloys generally considers alloying approaches to solve the conflicting problems of high strength and high electrical conductivity. The traditional “trial and error” research model is complicated and time-consuming. With the continuous accumulation of material databases and the advent of the “big data” era, machine learning has rapidly become a powerful tool for material design and development. In this paper, a total of 407 copper alloy data were collected. In the multi-objective prediction problem, the many-to-many prediction using back propagation neural network alone is improved to a many-to-one prediction. This improvement is based on back propagation neural network, tree model and support vector machine model. Through comparative analysis, an improved composition to property model was developed to predict the tensile strength and electrical conductivity of copper alloys, and the overall coefficient of determination reached 0.98; an improved property to composition model was developed to predict the composition of copper alloys, and the overall coefficient of determination reached 0.78. By combining these two models and the particle swarm optimization algorithm, an improved machine learning design system (MLDS) model was developed to achieve the composition prediction of copper alloy. The overall coefficient of determination reached 0.87, the prediction effect was better than the original MLDS model and with stronger stability. This method is of guiding significance for the alloy composition design of high-performance copper alloys. In addition, it also has certain reference value for the alloy composition design of other alloys. Copper ; Particle swarm optimization ; Artificial neural network ; Backpropagation ; Alloy ; Materials science ; Ultimate tensile strength ; Electrical resistivity and conductivity ; Computer science ; Stability (learning theory) ; Composition (language) ; Machine learning ; Artificial intelligence ; Algorithm ; Biological system ; Metallurgy ; Engineering Corrosion Behavior and Inhibition ; Machine Learning in Materials Science ; Aluminum Alloy Microstructure Properties
Authors: Kunal M. Lodaya | Nathan D. Ricke | Kelly Chen | Troy Van Voorhis
Source: The Journal of Physical Chemistry C (article, 2023-01-26)
Field: Materials Science — Materials Chemistry

Machine Learning Identification of Active Sites in Graphite-Conjugated Catalysts Graphite-conjugated catalysts (GCCs) are a promising class of materials that combine many of the advantages of heterogeneous and homogeneous catalysts. In particular, GCCs containing an aryl-pyridinium active site appear to be effective nonmetal catalysts for the oxygen reduction reaction (ORR). In this study, we analyzed both structural and electronic properties of a dataset of molecules containing nitrogen atoms embedded in aromatic molecules in order to understand which properties enable a particular site to bind O2, which is a necessary step for the initiation of ORR. We found that carbon atoms ortho or para to nitrogen and at the edge of aromatic systems are especially likely to be active. Using both structural and electronic features to describe the individual atoms in each catalyst, we trained machine learning models capable of identifying catalyst active sites. Although permutation importance of the features used to train these models indicates that several key electronic features have the greatest impact on model performance, the model trained only on structural features still proved effective in simulated catalyst discovery scenarios where the objective is affected more by false positives than false negatives. Catalysis ; Pyridinium ; Conjugated system ; Molecule ; Aryl ; Active site ; Combinatorial chemistry ; Chemistry ; Graphite ; Materials science ; Computer science ; Computational chemistry ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Haley N. Scolati | Anthony J. Remijan | Eric Herbst | Brett A. McGuire | Kin Long Kelvin Lee
Source: The Astrophysical Journal (article, 2023-12-01)
Field: Chemistry — Spectroscopy

Explaining the Chemical Inventory of Orion KL through Machine Learning Abstract The interplay of the chemistry and physics that exists within astrochemically relevant sources can only be fully appreciated if we can gain a holistic understanding of their chemical inventories. Previous work by Lee et al. demonstrated the capabilities of simple regression models to reproduce the abundances of the chemical inventory of the Taurus Molecular Cloud 1 (TMC-1), as well as to provide abundance predictions for new candidate molecules. It remains to be seen, however, to what degree TMC-1 is a “unicorn” in astrochemistry, where the simplicity of its chemistry and physics readily facilitates characterization with simple machine learning models. Here we present an extension in chemical complexity to a heavily studied high-mass star-forming region: the Orion Kleinmann–Low (Orion KL) nebula. Unlike TMC-1, Orion KL is composed of several structurally distinct environments that differ chemically and kinematically, wherein the column densities of molecules between these components can have nonlinear correlations that cause the unexpected appearance or even lack of likely species in various environments. This proof-of-concept study used similar regression models sampled by Lee et al. to accurately reproduce the column densities from the XCLASS fitting program presented by Crockett et al. Orion Nebula ; Physics ; Astrochemistry ; Molecular cloud ; Astrophysics ; Simple (philosophy) ; Stars ; Abundance (ecology) ; Interstellar medium Molecular Spectroscopy and Structure ; Atmospheric Ozone and Climate ; Astrophysics and Star Formation Studies
Authors: Andrij Vasylenko | Dmytro Antypov | Vladimir V. Gusev | Michael W. Gaultois | Matthew S. Dyer | Matthew J. Rosseinsky
Source: npj Computational Materials (article, 2023-09-04)
Field: Materials Science — Materials Chemistry

Element selection for functional materials discovery by integrated machine learning of elemental contributions to properties Abstract The unique nature of constituent chemical elements gives rise to fundamental differences in materials. Assessing materials based on their phase fields, defined as sets of constituent elements, before specific differences emerge due to composition and structure can reduce combinatorial complexity and accelerate screening, exploiting the distinction from composition-level approaches. Discrimination and evaluation of novelty of materials classes align with the experimental challenge of identifying new areas of chemistry. To address this, we present PhaseSelect, an end-to-end machine learning model that combines representation, classification, regression and novelty ranking of phase fields. PhaseSelect leverages elemental characteristics derived from computational and experimental materials data and employs attention mechanisms to reflect the individual element contributions when evaluating functional performance of phase fields. We demonstrate this approach for high-temperature superconductivity, high-temperature magnetism, and targeted bandgap energy applications, showcasing its versatility and potential for accelerating materials exploration. Novelty ; Ranking (information retrieval) ; Computer science ; Selection (genetic algorithm) ; Representation (politics) ; Artificial intelligence ; Material selection ; Field (mathematics) ; Machine learning ; Materials science ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Inorganic Chemistry and Materials
Authors: Yang Yang | Bo Xu | Hongxiang Zong
Source: Journal of Materials Informatics (article, 2023-11-02)
Field: Materials Science — Materials Chemistry

Physics infused machine learning force fields for 2D materials monolayers Large-scale atomistic simulations of two-dimensional (2D) materials rely on highly accurate and efficient force fields. Here, we present a physics-infused machine learning framework that enables the efficient development and interpretability of interatomic interaction models for 2D materials. By considering the characteristics of chemical bonds and structural topology, we have devised a set of efficient descriptors. This enables accurate force field training using a small dataset. The machine learning force fields show great success in describing the phase transformation and domain switching behaviors of monolayer Group IV monochalcogenides, e.g., GeSe and PbTe. Notably, this type of force field can be readily extended to other non-transition 2D systems, such as hexagonal boron nitride (h BN), leveraging their structural similarity. Our work provides a straightforward but accurate extension of simulation time and length scales for 2D materials. Force field (fiction) ; Interpretability ; Monolayer ; Field (mathematics) ; Computer science ; Materials science ; Topology (electrical circuits) ; Similarity (geometry) ; Boron nitride ; Artificial intelligence ; Statistical physics ; Machine learning ; Nanotechnology ; Physics ; Mathematics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Ferroelectric and Negative Capacitance Devices
Authors: Jérôme Rey | Céline Chizallet | Dario Rocca | Tomáš Bučko | Michaël Badawi
Source: Angewandte Chemie International Edition (article, 2023-12-07)
Field: Materials Science — Materials Chemistry

Reference‐Quality Free Energy Barriers in Catalysis from Machine Learning Thermodynamic Perturbation Theory For the first time, we report calculations of the free energies of activation of cracking and isomerization reactions of alkenes that combine several different electronic structure methods with molecular dynamics simulations. We demonstrate that the use of a high level of theory (here Random Phase Approximation-RPA) is necessary to bridge the gap between experimental and computed values. These transformations, catalyzed by zeolites and proceeding via cationic intermediates and transition states, are building blocks of many chemical transformations for valorization of long chain paraffins originating, e.g., from plastic waste, vegetable oils, Fischer-Tropsch waxes or crude oils. Compared with the free energy barriers computed at the PBE+D2 production level of theory via constrained ab initio molecular dynamics, the barriers computed at the RPA level by the application of Machine Learning thermodynamic Perturbation Theory (MLPT) show a significant decrease for isomerization reaction and an increase of a similar magnitude for cracking, yielding an unprecedented agreement with the results obtained by experiments and kinetic modeling. Isomerization ; Ab initio ; Cracking ; Transition state ; Transition state theory ; Chemistry ; Molecular dynamics ; Cationic polymerization ; Computational chemistry ; Catalysis ; Perturbation theory (quantum mechanics) ; Thermodynamics ; Physical chemistry ; Reaction rate constant ; Physics ; Kinetics ; Organic chemistry ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Zeolite Catalysis and Synthesis
Authors: Shogo Takasuka | Shunto Oikawa | Takayoshi Yoshimura | Shô Itô | Yosuke Harashima | Tomoaki Takayama | Shigehito Asano | Akira Kurosawa | Tetsunori Sugawara | Miho Hatanaka | Tomoyuki Miyao | Takamitsu Matsubara | Yu‐ya Ohnishi | Hiroharu Ajiro | Mikiya Fujii
Source: Digital Discovery (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Extrapolation performance improvement by quantum chemical calculations for machine-learning-based predictions of flow-synthesized binary copolymers The study utilized machine learning to predict highly accurate polymer properties, mainly when quantum chemical calculation values were included as variables, thus suggesting a promising tool for accelerating polymer development with new monomers. Extrapolation ; Copolymer ; Binary number ; Quantum chemical ; Polymer ; Monomer ; Flow (mathematics) ; Quantum ; Computer science ; Materials science ; Statistical physics ; Thermodynamics ; Applied mathematics ; Algorithm ; Mathematics ; Physics ; Mechanics ; Quantum mechanics ; Molecule ; Mathematical analysis ; Composite material ; Arithmetic Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Fuel Cells and Related Materials
Authors: Alec Lamens | Jürgen Bajorath
Source: ChemMedChem (article, 2023-11-21)
Field: Materials Science — Materials Chemistry

Generation of Molecular Counterfactuals for Explainable Machine Learning Based on Core‐Substituent Recombination The use of black box machine learning models whose decisions cannot be understood limits the acceptance of predictions in interdisciplinary research and camouflages artificial learning characteristics leading to predictions for other than anticipated reasons. Consequently, there is increasing interest in explainable artificial intelligence to rationalize predictions and uncover potential pitfalls. Among others, relevant approaches include feature attribution methods to identify molecular structures determining predictions and counterfactuals (CFs) or contrastive explanations. CFs are defined as variants of test instances with minimal modifications leading to opposing predictions. In medicinal chemistry, CFs have thus far only been little investigated although they are particularly intuitive from a chemical perspective. We introduce a new methodology for the systematic generation of CFs that is centered on well-defined structural analogues of test compounds. The approach is transparent, computationally straightforward, and shown to provide a wealth of CFs for test sets. The method is made freely available. Counterfactual conditional ; Artificial intelligence ; Machine learning ; Perspective (graphical) ; Computer science ; Core (optical fiber) ; Black box ; Cognitive science ; Psychology ; Social psychology ; Counterfactual thinking Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Hongchen Ji | Anita Rágyanszki | René Fournier
Source: Computational and Theoretical Chemistry (article, 2023-09-16)
Field: Materials Science — Materials Chemistry

Machine learning estimation of reaction energy barriers Chemistry ; Mean squared error ; Kernel (algebra) ; Regression ; Density functional theory ; Laplace operator ; Computational chemistry ; Statistics ; Combinatorics ; Quantum mechanics ; Physics ; Mathematics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Computational Drug Discovery Methods
Authors: Yuanyuan Yuan | Junqiang Ren | Hongtao Xue | Junchen Li | Fuling Tang | Peiqing La | Xuefeng Lu
Source: ACS Applied Materials & Interfaces (article, 2023-02-24)
Field: Materials Science — Materials Chemistry

Insight into the Electronic Properties of Semiconductor Heterostructure Based on Machine Learning and First-Principles A first-principles approach is a powerful means of gaining insight into the intrinsic structure and properties of materials. However, with the implementation of material genetic engineering, it is still a challenging road to discover materials with high satisfaction. One alternative is to employ machine-learning techniques to mine data and predict performance. In this present contribution, the method is taken to predict the band gap opening value of graphene in a heterostructure. First, the data of 2076 binary compounds in the Materials Project library are used to achieve visual dimensionality reduction of the data set through a t-distributed stochastic neighbor embedding (t-SNE) algorithm in unsupervised learning. Then, a series of semiconductor components are screened out and form heterostructures with graphene. Second, by means of the ensemble learning EXtreme Gradient Boost (XGBoost) algorithm and support vector machine (SVM) technology, two prediction frameworks are built to predict the band gap opening value of the graphene in the system. Finally, density functional theory (DFT) is used to calculate the energy band and density of states for comparison. Analysis shows that the prediction model has an accuracy rate of 88.3%, and there is little difference between prediction results and calculation results. We anticipate that this framework model would have fascinating applications in predicting the electronic properties of various multiphase materials. Computer science ; Support vector machine ; Graphene ; Artificial intelligence ; Heterojunction ; Machine learning ; Semiconductor ; Materials science ; Band gap ; Curse of dimensionality ; Dimensionality reduction ; Embedding ; Nanotechnology Machine Learning in Materials Science ; Graphene research and applications ; 2D Materials and Applications
Authors: Chin-Fei Chang | Srinivas Rangarajan
Source: The Journal of Physical Chemistry A (article, 2023-03-28)
Field: Materials Science — Materials Chemistry

Machine Learning and Informatics Based Elucidation of Reaction Pathways for Upcycling Model Polyolefin to Aromatics Catalytic upcycling of plastics results in a complex network of potentially thousands of reactions and intermediates. Manual analysis of such a network using ab initio methods to identify plausible reaction pathways and rate-controlling steps is intractable. Here, we combine informatics-based reaction network generation and machine learning based thermochemistry calculation to identify plausible (nonelementary step) pathways involved in dehydroaromatization of a model polyolefin, <i>n</i>-decane, to form aromatic products. All 78 aromatic molecules found involve a sequence comprising dehydrogenation, β-scission, and cyclization steps (in slightly different order). The plausible flux-carrying pathway depends on the family of reactions that is rate-controlling while the thermodynamic bottleneck is the first dehydrogenation step of <i>n</i>-decane. The adopted workflow is system agnostic and can be applied to understand the overall thermochemistry of other upcycling systems. Polyolefin ; Dehydrogenation ; Decane ; Thermochemistry ; Bottleneck ; Ab initio ; Petrochemical ; Chemistry ; Computer science ; Catalysis ; Computational chemistry ; Organic chemistry ; Physical chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Hossein Abedi | Anwar Algamal | Mohammad Abdollahzadeh | Mohammadreza Nematollahi | Ala’aldin Alafaghani | Parisa Bayati | Mohammad Elahinia | Ala Qattawi
Source: JOM (article, 2023-09-19)
Field: Materials Science — Materials Chemistry

Predicting Transformation Temperatures of Additively Manufactured NiTiHf Shape Memory Alloy Using Neural Network Modeling Shape-memory alloy ; Artificial neural network ; Mean squared error ; Pseudoelasticity ; Transformation (genetics) ; Root mean square ; Materials science ; Work (physics) ; Computer science ; Artificial intelligence ; Machine learning ; Mechanical engineering ; Mathematics ; Engineering ; Metallurgy ; Statistics Shape Memory Alloy Transformations ; Additive Manufacturing Materials and Processes ; High Entropy Alloys Studies