jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Eric M. Collins | Krishnan Raghavachari
Source: Journal of Chemical Theory and Computation (article, 2023-05-03)
Field: Materials Science — Materials Chemistry

Interpretable Graph-Network-Based Machine Learning Models via Molecular Fragmentation Chemists have long benefitted from the ability to understand and interpret the predictions of computational models. With the current shift to more complex deep learning models, in many situations that utility is lost. In this work, we expand on our previously work on computational thermochemistry and propose an interpretable graph network, FragGraph(nodes), that provides decomposed predictions into fragment-wise contributions. We demonstrate the usefulness of our model in predicting a correction to density functional theory (DFT)-calculated atomization energies using Δ-learning. Our model predicts G4(MP2)-quality thermochemistry with an accuracy of <1 kJ mol<sup>-1</sup> for the GDB9 dataset. Besides the high accuracy of our predictions, we observe trends in the fragment corrections which quantitatively describe the deficiencies of B3LYP. Node-wise predictions significantly outperform our previous model predictions from a global state vector. This effect is most pronounced as we explore the generality by predicting on more diverse test sets indicating node-wise predictions are less sensitive to extending machine learning models to larger molecules. Generality ; Thermochemistry ; Computer science ; Machine learning ; Artificial intelligence ; Graph ; Node (physics) ; Fragmentation (computing) ; Theoretical computer science ; Chemistry ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Neveen AlQasas | Daniel Johnson
Source: Surfaces and Interfaces (article, 2023-12-09)
Field: Chemistry — Spectroscopy

The use of neural network modeling for the estimation of the Hansen solubility parameters of polymer films from contact angle measurements Contact angle ; Materials science ; Artificial neural network ; Biological system ; Mean squared error ; Sessile drop technique ; Polymer ; Solvent ; Hildebrand solubility parameter ; Accessible surface area ; Solid surface ; Drop (telecommunication) ; Artificial intelligence ; Computer science ; Composite material ; Organic chemistry ; Chemical physics ; Statistics ; Mathematics ; Chemistry Adsorption, diffusion, and thermodynamic properties of materials ; Surfactants and Colloidal Systems ; Polymer crystallization and properties
Authors: Mahdi Hasanzadeh Azar | Samaneh Aynehband | Habib Abdollahi | Homayoon Alimohammadi | Nooshin Rajabi | Shayan Angizi | Vahid Kamraninejad | Razieh Teimouri | Raheleh Mohammadpour | Abdolreza Simchi
Source: SSRN Electronic Journal (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Scaps Empowered Machine Learning Modelling of Perovskite Solar Cells: Predictive Design of Active Layer and Hole Transport Materials Perovskite (structure) ; Layer (electronics) ; Materials science ; Active layer ; Computer science ; Optoelectronics ; Engineering physics ; Nanotechnology ; Engineering ; Chemical engineering Machine Learning in Materials Science ; Perovskite Materials and Applications
Authors: Wenguang Hu | Lei Zhang
Source: Journal of Molecular Graphics and Modelling (article, 2023-06-03)
Field: Materials Science — Materials Chemistry

First-principles, machine learning and symbolic regression modelling for organic molecule adsorption on two-dimensional CaO surface Symbolic regression ; Machine learning ; Artificial intelligence ; Computer science ; Adsorption ; Density functional theory ; Leverage (statistics) ; Molecular descriptor ; Ranking (information retrieval) ; Genetic programming ; Quantitative structure–activity relationship ; Computational chemistry ; Chemistry ; Physical chemistry Machine Learning in Materials Science ; Crystallization and Solubility Studies ; Catalytic Processes in Materials Science
Authors: Ajin Rajan | Anoop P. Pushkar | Balaji C. Dharmalingam | Jithin John Varghese
Source: iScience (review, 2023-06-07)
Field: Materials Science — Materials Chemistry

Iterative multiscale and multi-physics computations for operando catalyst nanostructure elucidation and kinetic modeling Modern heterogeneous catalysis has benefitted immensely from computational predictions of catalyst structure and its evolution under reaction conditions, first-principles mechanistic investigations, and detailed kinetic modeling, which are rungs on a multiscale workflow. Establishing connections across these rungs and integration with experiments have been challenging. Here, operando catalyst structure prediction techniques using density functional theory simulations and <i>ab initio</i> thermodynamics calculations, molecular dynamics, and machine learning techniques are presented. Surface structure characterization by computational spectroscopic and machine learning techniques is then discussed. Hierarchical approaches in kinetic parameter estimation involving semi-empirical, data-driven, and first-principles calculations and detailed kinetic modeling via mean-field microkinetic modeling and kinetic Monte Carlo simulations are discussed along with methods and the need for uncertainty quantification. With these as the background, this article proposes a bottom-up hierarchical and closed loop modeling framework incorporating consistency checks and iterative refinements at each level and across levels. Nanostructure ; Computation ; Kinetic energy ; Nanotechnology ; Catalysis ; Chemistry ; Physics ; Materials science ; Computer science ; Organic chemistry ; Classical mechanics ; Algorithm Catalytic Processes in Materials Science ; Zeolite Catalysis and Synthesis ; Catalysis and Oxidation Reactions
Authors: Xiaoliang Pan | Ryan Snyder | Jianing Wang | Chance Lander | Carly Wickizer | Richard Van | Andrew D. Chesney | Yuanfei Xue | Yuezhi Mao | Ye Mei | Jingzhi Pu | Yihan Shao
Source: Journal of Computational Chemistry (article, 2023-12-11)
Field: Materials Science — Materials Chemistry

Training machine learning potentials for reactive systems: A Colab tutorial on basic models In the last several years, there has been a surge in the development of machine learning potential (MLP) models for describing molecular systems. We are interested in a particular area of this field - the training of system-specific MLPs for reactive systems - with the goal of using these MLPs to accelerate free energy simulations of chemical and enzyme reactions. To help new members in our labs become familiar with the basic techniques, we have put together a self-guided Colab tutorial (https://cc-ats.github.io/mlp_tutorial/), which we expect to be also useful to other young researchers in the community. Our tutorial begins with the introduction of simple feedforward neural network (FNN) and kernel-based (using Gaussian process regression, GPR) models by fitting the two-dimensional Müller-Brown potential. Subsequently, two simple descriptors are presented for extracting features of molecular systems: symmetry functions (including the ANI variant) and embedding neural networks (such as DeepPot-SE). Lastly, these features will be fed into FNN and GPR models to reproduce the energies and forces for the molecular configurations in a Claisen rearrangement reaction. Computer science ; Artificial intelligence ; Artificial neural network ; Simple (philosophy) ; Kriging ; Machine learning ; Process (computing) ; Kernel (algebra) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Minoru Kusaba | Yoshihiro Hayashi | Chang Liu | Araki Wakiuchi | Ryo Yoshida
Source: Physical review. B./Physical review. B (article, 2023-10-16)
Field: Materials Science — Materials Chemistry

Representation of materials by kernel mean embedding For using machine learning to predict material properties, the feature representation of the materials given to the model plays a fundamental role. A model describes material properties as a function of any given material system expressed as a fixed-length numeric vector, often called a descriptor. However, in most cases, the variables of interest are nontrivial for encoding their compositional or structural features, such as molecules, crystal systems, chemical compositions, and composite materials, into a fixed-length vector. Conventionally, to translate such a multicomponent system into a fixed-length vector, the distribution of predefined component features is summarized into a few summary statistics. The disadvantage of this reduction operation is that some distributional information, such as multimodality, is lost in the vectorization process. Here, we present a general class of material descriptors motivated by the machine-learning theory of kernel mean embedding. Unlike conventional descriptors, kernel mean embedding can retain all information regarding the distribution of component features in the vectorization process. Furthermore, the kernel mean descriptor uniquely determines the inverse map to the original material space. We demonstrate the expressive power and versatility of the kernel mean descriptor in various applications, including the prediction of the formation energy of inorganic compounds, prediction of the chemical composition to form quasicrystalline materials, and the use of force-field parameters to characterize polymeric materials. Kernel (algebra) ; Embedding ; Representation (politics) ; Kernel method ; Computer science ; Component (thermodynamics) ; Feature vector ; Feature (linguistics) ; String kernel ; Pattern recognition (psychology) ; Artificial intelligence ; Support vector machine ; Mathematics ; Algorithm ; Radial basis function kernel ; Pure mathematics ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Entao Yang | James Pressly | Bharath Natarajan | Robert Colby | Karen I. Winey | Robert A. Riggleman
Source: Soft Matter (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Understanding creep suppression mechanisms in polymer nanocomposites through machine learning While recent efforts have shown how local structure plays an essential role in the dynamic heterogeneity of homogeneous glass-forming materials, systems containing interfaces such as thin films or composite materials remain poorly understood. It is known that interfaces perturb the molecular packing nearby, however, numerous studies show the dynamics are modified over a much larger range. Here, we examine the dynamics in polymer nanocomposites (PNCs) using a combination of simulations and experiments and quantitatively separate the role of polymer packing from other effects on the dynamics, as a function of distance from the nanoparticle surfaces. After showing good qualitative agreement between the simulations and experiments in glassy structure and creep compliance, we use a machine-learned structure indicator, softness, to decompose polymer dynamics in our simulated PNCs into structure-dependent and structure-independent processes. With this decomposition, the free energy barrier for polymer rearrangement can be described as a combination of packing-dependent and packing-independent barriers. We find both barriers are higher near nanoparticles and decrease with applied stress, quantitatively demonstrating that the slow interfacial dynamics is not solely due to polymer packing differences, but also the change of structure-dynamics relationships. Finally, we present how this decomposition can be used to accurately predict strain-time creep curves for PNCs from their static configuration, providing additional insights into the effects of polymer-nanoparticle interfaces on creep suppression in PNCs. Materials science ; Polymer ; Creep ; Molecular dynamics ; Chemical physics ; Nanocomposite ; Polymer nanocomposite ; Nanoparticle ; Dynamics (music) ; Decomposition ; Nanotechnology ; Composite material ; Computational chemistry ; Chemistry ; Physics Material Dynamics and Properties ; Polymer Nanocomposites and Properties ; Polymer crystallization and properties
Authors: N. M. Chtchelkatchev | R. E. Ryltsev | M. V. Magnitskaya | S. M. Gorbunov | Kirill A. Cherednichenko | Vladimir L. Solozhenko | В. В. Бражкин
Source: The Journal of Chemical Physics (article, 2023-08-08)
Field: Materials Science — Materials Chemistry

Local structure, thermodynamics, and melting of boron phosphide at high pressures by deep learning-driven <i>ab initio</i> simulations Boron phosphide (BP) is a (super)hard semiconductor constituted of light elements, which is promising for high demand applications at extreme conditions. The behavior of BP at high temperatures and pressures is of special interest but is also poorly understood because both experimental and conventional ab initio methods are restricted to studying refractory covalent materials. The use of machine learning interatomic potentials is a revolutionary trend that gives a unique opportunity for high-temperature study of materials with ab initio accuracy. We develop a deep machine learning potential (DP) for accurate atomistic simulations of the solid and liquid phases of BP as well as their transformations near the melting line. Our DP provides quantitative agreement with experimental and ab initio molecular dynamics data for structural and dynamic properties. DP-based simulations reveal that at ambient pressure, a tetrahedrally bonded cubic BP crystal melts into an open structure consisting of two interpenetrating sub-networks of boron and phosphorous with different structures. Structure transformations of BP melt under compressing are reflected by the evolution of low-pressure tetrahedral coordination to high-pressure octahedral coordination. The main contributions to structural changes at low pressures are made by the evolution of medium-range order in the B-subnetwork and, at high pressures, by the change of short-range order in the P-subnetwork. Such transformations exhibit an anomalous behavior of structural characteristics in the range of 12-15 GPa. DP-based simulations reveal that the Tm(P) curve develops a maximum at P ≈ 13 GPa, whereas experimental studies provide two separate branches of the melting curve, which demonstrate the opposite behavior. Analysis of the results obtained raises open issues in developing machine learning potentials for covalent materials and stimulates further experimental and theoretical studies of melting behavior in BP. Ab initio ; Materials science ; Thermodynamics ; Ab initio quantum chemistry methods ; Molecular dynamics ; Chemical physics ; Chemistry ; Computational chemistry ; Physics ; Molecule Machine Learning in Materials Science ; High-pressure geophysics and materials ; Boron and Carbon Nanomaterials Research
Authors: Yu Zhang | Zhichun Zheng | Fengqing Chen | Minjie Li | Peng Ding | Wencong Lu
Source: Journal of Molecular Structure (article, 2023-10-14)
Field: Materials Science — Materials Chemistry

Accelerating the discovery of N-annulated perylene organic sensitizers via an interpretable machine learning model Electronegativity ; Chemistry ; Density functional theory ; Dye-sensitized solar cell ; Perylene ; Chemical space ; Energy conversion efficiency ; Electrolyte ; Band gap ; Time-dependent density functional theory ; Computational chemistry ; Artificial intelligence ; Biological system ; Molecule ; Organic chemistry ; Computer science ; Physical chemistry ; Thermodynamics ; Optoelectronics ; Drug discovery Machine Learning in Materials Science ; Conducting polymers and applications ; TiO2 Photocatalysis and Solar Cells
Authors: Jonathan Schmidt | Hai‐Chen Wang | G. Schmidt | Miguel A. L. Marques
Source: npj Computational Materials (article, 2023-04-17)
Field: Materials Science — Materials Chemistry

Machine learning guided high-throughput search of non-oxide garnets Abstract Garnets have found important applications in modern technologies including magnetorestriction, spintronics, lithium batteries, etc. The overwhelming majority of experimentally known garnets are oxides, while explorations (experimental or theoretical) for the rest of the chemical space have been limited in scope. A key issue is that the garnet structure has a large primitive unit cell, requiring a substantial amount of computational resources. To perform a comprehensive search of the complete chemical space for new garnets, we combine recent progress in graph neural networks with high-throughput calculations. We apply the machine learning model to identify the potentially (meta-)stable garnet systems before performing systematic density-functional calculations to validate the predictions. We discover more than 600 ternary garnets with distances to the convex hull below 100 meV ⋅ atom −1 . This includes sulfide, nitride, and halide garnets. We analyze their electronic structure and discuss the connection between the value of the electronic band gap and charge balance. Computer science ; Ternary operation ; Throughput ; Materials science ; Electronic structure ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Inorganic Chemistry and Materials
Authors: Challenger Mishra | Niklas von Wolff | Abhinav Tripathi | Claire N. Brodie | Neil D. Lawrence | Aditya Ravuri | Éric Brémond | Annika Preiss | Amit Kumar
Source: Digital Discovery (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Predicting ruthenium catalysed hydrogenation of esters using machine learning The report describes the application of machine learning tools to predict hydrogenation of esters using molecular catalysts based on ruthenium. Ruthenium ; Chemistry ; Noyori asymmetric hydrogenation ; Combinatorial chemistry ; Computer science ; Catalysis ; Organic chemistry Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Catalysis and Hydrodesulfurization Studies
Authors: Junxi Chen | Sheng D. Chao
Source: Molecules (article, 2023-12-01)
Field: Materials Science — Materials Chemistry

Intermolecular Non-Bonded Interactions from Machine Learning Datasets Accurate determination of intermolecular non-covalent-bonded or non-bonded interactions is the key to potentially useful molecular dynamics simulations of polymer systems. However, it is challenging to balance both the accuracy and computational cost in force field modelling. One of the main difficulties is properly representing the calculated energy data as a continuous force function. In this paper, we employ well-developed machine learning techniques to construct a general purpose intermolecular non-bonded interaction force field for organic polymers. The original ab initio dataset SOFG-31 was calculated by us and has been well documented, and here we use it as our training set. The CLIFF kernel type machine learning scheme is used for predicting the interaction energies of heterodimers selected from the SOFG-31 dataset. Our test results show that the overall errors are well below the chemical accuracy of about 1 kcal/mol, thus demonstrating the promising feasibility of machine learning techniques in force field modelling. Intermolecular force ; Force field (fiction) ; Computer science ; Kernel (algebra) ; Machine learning ; Artificial intelligence ; Construct (python library) ; Field (mathematics) ; Ab initio ; Chemical physics ; Biological system ; Molecule ; Chemistry ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Linfeng Yu | Zhicheng Shang | Xichang Lin | Mengxuan Li | Yuanbo Liu | Longtao Ren | Shasa Deng | Fan Zhang | Liang Luo | Haohong Duan | Xiaoming Sun
Source: Advanced Materials Interfaces (article, 2023-10-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Bio‐Derived Wood‐Based Gas Diffusion Electrode for High‐Performance Aluminum–Air Batteries: Insights into Pore Structure Abstract Gas reactant transport plays a crucial role in various gas‐consumption reaction‐based electrochemical devices, but the pivotal performance limitation still centers on gas diffusion electrodes (GDEs). To this end, natural cross‐cut wood to prepare GDEs for aluminum–air batteries is introduced by utilizing the ordered structure with microchannels. With cobalt–nitrogen co‐doped carbon nanotubes as the oxygen reduction reaction catalysts grown inside the channels of carbonized wood slice and wettability gradient modification, cherry‐based GDE achieves higher power density (267 vs. 236 mW cm −2 ) than the commercial carbon fiber paper‐based electrode. By bridging the identified characteristics of pore structure via deep‐learning image recognition technology with the permeability of other three typical kinds of (ash, pine, and oak) wood‐based GDEs, it is revealed that the ratio of effective porosity to the average pore size is key to the performance. This work demonstrates the feasibility of bio‐derived wood‐based materials for fabricating high‐performance GDEs and provides insights into pore structure for the rational design of structured electrodes. Materials science ; Electrode ; Air permeability specific surface ; Porosity ; Gaseous diffusion ; Carbonization ; Chemical engineering ; Current collector ; Wetting ; Electrochemistry ; Nanotechnology ; Composite material ; Electrolyte ; Fuel cells Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; Advanced Battery Materials and Technologies
Authors: Robin Ruff | Patrick Reiser | Jan Stühmer | Pascal Friederich
Source: arXiv (Cornell University) (preprint, 2023-02-27)
Field: Materials Science — Materials Chemistry

Connectivity Optimized Nested Graph Networks for Crystal Structures Graph neural networks (GNNs) have been applied to a large variety of applications in materials science and chemistry. Here, we recapitulate the graph construction for crystalline (periodic) materials and investigate its impact on the GNNs model performance. We suggest the asymmetric unit cell as a representation to reduce the number of atoms by using all symmetries of the system. This substantially reduced the computational cost and thus time needed to train large graph neural networks without any loss in accuracy. Furthermore, with a simple but systematically built GNN architecture based on message passing and line graph templates, we introduce a general architecture (Nested Graph Network, NGN) that is applicable to a wide range of tasks. We show that our suggested models systematically improve state-of-the-art results across all tasks within the MatBench benchmark. Further analysis shows that optimized connectivity and deeper message functions are responsible for the improvement. Asymmetric unit cells and connectivity optimization can be generally applied to (crystal) graph networks, while our suggested nested graph framework will open new ways of systematic comparison of GNN architectures. Computer science ; Graph ; Theoretical computer science ; Benchmark (surveying) ; Template ; Power graph analysis Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Advanced Memory and Neural Computing
Authors: Shanghong Zeng | Yuan Zhang | Junbiao Li | Zhipeng Liu | Suling Shen | Zongxian Ou | Pengxiang Song | Ronghua Yuan | Dehua Dong | Heping Xie | Meng Ni | Zongping Shao | Bin Chen
Source: International Journal of Hydrogen Energy (article, 2023-10-29)
Field: Materials Science — Materials Chemistry

Electrochemical promoted dry methane reforming for power and syngas co-generation in solid oxide fuel cells: Experiments, modelling and optimizations Syngas ; Methane ; Carbon dioxide reforming ; Solid oxide fuel cell ; Oxide ; Materials science ; Particle swarm optimization ; Process engineering ; Catalysis ; Chemical engineering ; Chemistry ; Computer science ; Algorithm ; Electrode ; Physical chemistry ; Engineering Advancements in Solid Oxide Fuel Cells ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science