jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Suvo Banik | Karthik Balasubramanian | Sukriti Manna | Sybil Derrible | Subramanian Sankaranarayananan
Source: ChemRxiv (preprint, 2023-06-25)
Field: Materials Science — Materials Chemistry

Evaluating Generalized Feature Importance via Performance Assessment of Machine Learning Models for Predicting Elastic Properties of Materials Identifying key descriptors and understanding important features across different classes of materials are crucial for machine learning (ML) tools to both predict material properties and reveal the physics underlying any process of interest. Traditionally, the predictive modeling of elastic properties of materials is limited to only a few classes of materials and a small set of ML tools despite the broad applications of these materials. Users now have a broad choice of ML models ranging from simple regression models to graph neural networks (GNN) for predicting structure-property relationships. While in recent years, GNNs have outshined traditional ML models in terms of predictability, their intensive data requirement and lack of interpretability may limit practical applicability. Here, we develop a domain-segmented feature space using a diverse set of material attributes and perform a predictive analysis using state-of-the-art ML tools using elastic modulus prediction as a representative example. By deducing a model-independent overall ranking based on feature importance learned by each model, the knowledge is then transferred to GNNs. Our findings indicate a threshold limit on the predictability of traditional ML models, but our approach of transferring task-specific feature importance knowledge to the GNNs can enhance their performance by reducing their data requirement while retaining considerable accuracy. Interpretability ; Predictability ; Machine learning ; Computer science ; Artificial intelligence ; Feature (linguistics) ; Ranking (information retrieval) ; Predictive modelling ; Feature engineering ; Property (philosophy) ; Set (abstract data type) ; Data mining ; Deep learning ; Mathematics Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Jiwon Park | Su-Hyeon Kim | Jisu Kim | Byung Joo Kim | Hyun-Seok Cheon | Chang-Seok Oh
Source: Korean Journal of Metals and Materials (article, 2023-11-01)
Field: Materials Science — Metals and Alloys

Predicting the Hardness of Al-Sc-X Alloys with Machine Learning Models, Explainable Artificial Intelligence Analysis and Inverse Design In this study, the Vickers hardness of precipitation-strengthened Al-Sc-X (X = Zr, Si, and Fe) alloys were predicted using machine learning models, depending on the alloys’ compositions, solid-solution treatment and aging conditions. The data used for machine learning were collected from the literature. Among the models, tree-based ensemble models such as extreme gradient boosting and random forest performed well. Then the feature impact on the model output was analyzed with SHarpely Additive eXplanation (SHAP). Based on the SHAP analysis and prior domain knowledge, the process conditions were restricted to narrow down the inverse design search space. Candidate alloys suggested by the optimization using a genetic algorithm showed improved hardness values. The hardness prediction model and the inverse designsuggested candidates were then experimentally validated. The accuracy of the hardness prediction model was 0.994, when the predicted hardness was 85.4 Hv, and the experimentally measured hardness was 84.9 Hv. A specimen whose composition was close to the inverse-designed alloy was cast and heat treated according to the suggested conditions. The inverse design showed an accuracy of 0.965. Exploring the entire combination of possible feature space requires vast effort and time. An efficient search for materials with improved properties can be achieved using an appropriate configuration of well-performing machine learning models and explainable AI techniques guided by domain knowledge. Vickers hardness test ; Inverse ; Materials science ; Machine learning ; Gradient boosting ; Boosting (machine learning) ; Artificial intelligence ; Random forest ; Alloy ; Algorithm ; Extreme learning machine ; Feature (linguistics) ; Computer science ; Metallurgy ; Mathematics ; Artificial neural network ; Microstructure ; Geometry Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and Mechanical Properties of Steels ; Advanced Materials Characterization Techniques
Authors: Zhengliang Lin | Jingze Liu | Kristine Hsiao | Xi Chen
Source: ChemRxiv (preprint, 2023-04-24)
Field: Materials Science — Materials Chemistry

Machine-learning prediction of exciton binding energies in two-dimensional materials for photonics application Density functional theory (DFT) is a ubiquitous first-principles method, but the approximate nature of the exchange-correlation functional poses an inherent limitation for the accuracy of various computed properties. In this context, surrogate models based on machine learning have the potential to provide a more efficient and physically meaningful understanding of electronic properties, such as the exciton binding energy. Here, we construct a regression model based on gradient boosting on decision trees (CatBoost) for prediction of the exciton binding energy of 2D materials from simple physical descriptors, using the Computational 2D Materials Database (C2DB). Out of 22 atomic features, the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energy levels exhibit the highest importance scores, with HOMO having a much more important role compared to other features. We obtain a R2 value of 0.8023 and mean absolute error of 0.2138 eV using the top 9 features. Our work presents a rapid and interpretable prediction model for exciton biding energy with high fidelity to DFT and can be extended beyond the C2DB dataset considered in this study. Exciton ; Density functional theory ; Binding energy ; Context (archaeology) ; HOMO/LUMO ; Computer science ; Boosting (machine learning) ; Artificial intelligence ; Machine learning ; Statistical physics ; Physics ; Computational chemistry ; Chemistry ; Quantum mechanics ; Molecule Machine Learning in Materials Science ; 2D Materials and Applications ; Computational Drug Discovery Methods
Authors: Zhengliang Lin | Jingze Liu | Sijing Zhong | Kristine Hsiao | Xi Chen
Source: ChemRxiv (preprint, 2023-12-17)
Field: Materials Science — Materials Chemistry

Machine-learning prediction of exciton binding energies in two-dimensional materials for photonics application Density functional theory (DFT) is a ubiquitous first-principles method, but the approximate nature of the exchange-correlation functional poses an inherent limitation for the accuracy of various computed properties. In this context, surrogate models based on machine learning have the potential to provide a more efficient and physically meaningful understanding of electronic properties, such as the exciton binding energy. Here, we construct a regression model based on gradient boosting on decision trees (CatBoost) for prediction of the exciton binding energy of 2D materials from simple physical descriptors, using the Computational 2D Materials Database (C2DB). Out of 22 atomic features, the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energy levels exhibit the highest importance scores, with HOMO having a much more important role compared to other features. We obtain a R2 value of 0.8023 and mean absolute error of 0.2138 eV using the top 9 features. Our work presents a rapid and interpretable prediction model for exciton biding energy with high fidelity to DFT and can be extended beyond the C2DB dataset considered in this study. Exciton ; Density functional theory ; Binding energy ; Context (archaeology) ; HOMO/LUMO ; Computer science ; Boosting (machine learning) ; Artificial intelligence ; Machine learning ; Statistical physics ; Physics ; Computational chemistry ; Chemistry ; Quantum mechanics ; Molecule Machine Learning in Materials Science ; 2D Materials and Applications ; Computational Drug Discovery Methods
Authors: Chonghyo Joo | Jeongdong Kim | Hyungtae Cho | Jaewon Lee | Sungho Suh | Junghwan Kim
Source:  (article, 2023-07-18)
Field: Chemistry — Analytical Chemistry

Chemical Property-Guided Neural Networks for Naphtha Composition Prediction The naphtha cracking process heavily relies on the composition of naphtha, which is a complex blend of different hydrocarbons. Predicting the naphtha composition accurately is crucial for efficiently controlling the cracking process and achieving maximum performance. Traditional methods, such as gas chromatography and true boiling curve, are not feasible due to the need for pilot-plant-scale experiments or cost constraints. In this paper, we propose a neural network framework that utilizes chemical property information to improve the performance of naphtha composition prediction. Our proposed framework comprises two parts: a Watson K factor estimation network and a naphtha composition prediction network. Both networks share a feature extraction network based on Convolutional Neural Network (CNN) architecture, while the output layers use Multi-Layer Perceptron (MLP) based networks to generate two different outputs - Watson K factor and naphtha composition. The naphtha composition is expressed in percentages, and its sum should be 100%. To enhance the naphtha composition prediction, we utilize a distillation simulator to obtain the distillation curve from the naphtha composition, which is dependent on its chemical properties. By designing a loss function between the estimated and simulated Watson K factors, we improve the performance of both Watson K estimation and naphtha composition prediction. The experimental results show that our proposed framework can predict the naphtha composition accurately while reflecting real naphtha chemical properties. Naphtha ; Distillation ; Artificial neural network ; Cracking ; Process engineering ; Computer science ; Artificial intelligence ; Chemistry ; Engineering ; Chromatography ; Organic chemistry Petroleum Processing and Analysis ; Fault Detection and Control Systems ; Crystallization and Solubility Studies
Authors: Naveen K. Dandu | Logan Ward | Rajeev S. Assary | Paul C. Redfern | Larry A. Curtiss
Source: The Journal of Physical Chemistry A (article, 2023-07-05)
Field: Materials Science — Materials Chemistry

Accurate Prediction of Adiabatic Ionization Potentials of Organic Molecules using Quantum Chemistry Assisted Machine Learning In previous work (Dandu et al., <i>J</i>. <i>Phys</i>. <i>Chem</i>. <i>A</i>, <b>2022</b>, <i>126</i>, 4528-4536), we were successful in predicting accurate atomization energies of organic molecules using machine learning (ML) models, obtaining an accuracy as low as 0.1 kcal/mol compared to the G4MP2 method. In this work, we extend the use of these ML models to adiabatic ionization potentials on data sets of energies generated using quantum chemical calculations. Atomic specific corrections that were found to improve atomization energies from quantum chemical calculations have also been used in this study to improve ionization potentials. The quantum chemical calculations were performed on 3405 molecules containing eight or fewer non-hydrogen atoms derived from the QM9 data set, using the B3LYP functional with the 6-31G(2df,p) basis set for optimization. Low-fidelity IPs for these structures were obtained using two density functional methods: B3LYP/6-31+G(2df,p) and ωB97XD/6-311+G(3df,2p). Highly accurate G4MP2 calculations were performed on these optimized structures to obtain high-fidelity IPs to use in ML models based on the low-fidelity IPs. Our best performing ML methods gave IPs of organic molecules within a mean absolute deviation of 0.035 eV from the G4MP2 IPs for the whole data set. This work demonstrates that ML predictions assisted by quantum chemical calculations can be used to successfully predict IPs of organic molecules for use in high throughput screening. Basis set ; Ionization ; Molecule ; Ionization energy ; Adiabatic process ; Density functional theory ; Quantum chemistry ; Chemistry ; Quantum ; Work (physics) ; Computational chemistry ; Atomic physics ; Physics ; Thermodynamics ; Quantum mechanics ; Ion ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Mass Spectrometry Techniques and Applications
Authors: Anubhab Haldar | Quentin Clark | Marios Zacharias | Feliciano Giustino | Sahar Sharifzadeh
Source: Research Square (preprint, 2023-08-22)
Field: Materials Science — Materials Chemistry

Machine learning electron-phonon interactions in 2D materials Phonon ; Condensed matter physics ; Electron ; Materials science ; Computer science ; Chemical physics ; Physics ; Engineering physics ; Quantum mechanics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Electron and X-Ray Spectroscopy Techniques
Authors: Haoyang Xu | Boyuan Zhang | Yuanda Tao | Xu Weijia | Bo Hu | Yan Feng | Jin Wen
Source: ChemRxiv (preprint, 2023-06-12)
Field: Materials Science — Materials Chemistry

Ultrafast Photo-Controlled Rotation in Molecular Motor Investigated by Machine Learning-Based Nonadiabatic Dynamics Simulations The thermal helix inversion (THI) of the overcrowded-alkene-based molecular motors determines the speed of the unidirectional rotation due to the high reaction barrier in the ground state, in comparison with the ultrafast photo-reaction process. Recently, a phosphine-based motor has achieved all-photochemical rotation experimentally, promising to be controlled without thermal step. However, the mechanism of this photochemical reaction has not been fully revealed. The comprehensive computational studies on photoisomerization still resort to nonadiabatic molecular dynamics (NAMD) simulations based on electronic structure calculations, which remains a high computational cost for large systems like molecular motors. Machine learning (ML) has become an accelerating tool in NAMD simulations recently, where excited-state potential energy surfaces are constructed analytically with high accuracy, providing an efficient approach for simulations in photochemistry. Herein the reaction pathway is explored by a spinflip time-dependent density functional theory (SF-TDDFT) approach in combination with ML-based NAMD simulations. According to our computational simulations, we notice one of the key factors of fulfilling all-photochemical rotation in the phosphinebased motor is that the excitation energies of four isomers are similar. Additionally, a shortcut photo-induced transformation between unstable isomers replaces the THI step, which shares the conical intersection (CI) with photoisomerization. In this study, we provide a practical approach to speed up the NAMD simulations in photochemical reactions for a large system, which could be extended to other complex systems. Conical intersection ; Photoisomerization ; Time-dependent density functional theory ; Molecular dynamics ; Density functional theory ; Excited state ; Chemistry ; Ultrashort pulse ; Potential energy ; Isomerization ; Computational chemistry ; Molecular physics ; Photochemistry ; Computer science ; Physics ; Atomic physics ; Optics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Ryan Sayko | Michael Jacobs | Marissa Dominijanni | Andrey V. Dobrynin
Source: ACS Applied Polymer Materials (article, 2023-09-05)
Field: Materials Science — Materials Chemistry

Deep Learning for Determination of Properties of Semidilute Polymer Solutions The ability to predict polymer solution viscosity is essential for polymer characterization and processing. Here, we use a synergistic approach combining the scaling theory of polymer solutions and convolutional neural network (CNN) models to obtain system-specific parameters and describe semidilute solution viscosity in the unentangled and entangled solution regimes. The scaling approach relies on the existence of a characteristic microscopic length scale─the solution correlation length (correlation blob size) ξ─which uniquely defines macroscopic solution properties. It is based on a relationship between the solution correlation length ξ = lgν/B and the number of monomers g per correlation volume for polymers with the monomer projection length l. The system-specific set of parameters Bg, Bth, and 1 in the corresponding solution regime with the scaling exponent ν = 0.588, 0.5, and 1, respectively. Applying two CNN models, we obtained the sets Bg and Bth from the solution specific viscosity, ηsp, as a function of concentration, c, and weight-average degree of polymerization, Nw. The CNN was trained on theoretically generated datasets converted to sparse images representing the normalized specific viscosity ηsp/Nw (cl3)1/(3ν–1) in the unentangled Rouse regime. The trained CNN was utilized in automated data analysis of the solution viscosity of polystyrene, poly(ethylene oxide), poly(methyl methacrylate), poly(acrylonitrile-co-itaconic acid), cellulose, sodium hyaluronate, hydroxypropyl methyl cellulose, methyl cellulose, hydroxypropyl cellulose, cellulose tris(phenyl carbamate), xanthan gum, galactomannan, and sodium κ-carrageenan in water, organic solvents, and ionic liquids. This approach produced values of the B-parameters with mean absolute percentage differences of less than 6% from the corresponding values determined by the manual data analysis. The B-parameters are then used to obtain the packing number Pe defining the onset of entanglements in polymer solutions and to describe semidilute solution viscosity as a function of concentration and Nw. Polymer ; Intrinsic viscosity ; Viscosity ; Polystyrene ; Polymer chemistry ; Monomer ; Materials science ; Scaling ; Thermodynamics ; Chemistry ; Mathematics ; Composite material ; Physics ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Ionic liquids properties and applications
Authors: Ethan Berger | Hannu‐Pekka Komsa
Source: arXiv (Cornell University) (preprint, 2023-10-20)
Field: Materials Science — Materials Chemistry

Polarizability Models for Simulations of Finite Temperature Raman Spectra from Machine Learning Molecular Dynamics Raman spectroscopy is a powerful and nondestructive method that is widely used to study the vibrational properties of solids or molecules. Simulations of finite-temperature Raman spectra rely on obtaining polarizabilities along molecular dynamics trajectories, which is computationally highly demanding if calculated from first principles. Machine learning force fields (MLFF) are becoming widely used for accelerating molecular dynamics simulations, but machine-learning models for polarizability are still rare. In this work, we present and compare three polarizability models for obtaining Raman spectra in conjunction with MLFF molecular dynamics trajectories: (i) model based on projection to primitive cell eigenmodes, (ii) bond polarizability model, and (iii) symmetry-adapted Gaussian process regression (SA-GPR) using smooth overlap of atomic positions. In particular, we investigate the accuracy of these models for different systems and how much training data is required. Models are first applied to boron arsenide, where the first- and second-order Raman spectra are studied as well as the effect of boron isotopes. With MoS$_2$ we study the applicability of the models for highly anisotropic systems and for simulating resonant Raman spectra. Finally, inorganic halide perovskites CsPbBr$_3$ and CsSnBr$_3$ are studied with a particular interest in simulating the spectra across phase transitions and the evolution of the central peak. All models can be used to efficiently predict polarizabilities and are applicable to large systems and long simulation times, and while all three models were found to perform similarly for BAs and MoS$_2$, only SA-GPR offers sufficient flexibility to accurately describe complex anharmonic materials like the perovskites. Polarizability ; Raman spectroscopy ; Molecular dynamics ; Anharmonicity ; Statistical physics ; Spectral line ; Molecular physics ; Projection (relational algebra) ; Chemistry ; Chemical physics ; Materials science ; Computational physics ; Computational chemistry ; Physics ; Computer science ; Molecule ; Algorithm ; Optics ; Condensed matter physics ; Quantum mechanics Machine Learning in Materials Science ; Perovskite Materials and Applications ; 2D Materials and Applications
Authors: Matthew Witman | Anuj Goyal | Tadashi Ogitsu | Anthony H. McDaniel | Stephan Lany
Source: ChemRxiv (preprint, 2023-01-05)
Field: Materials Science — Materials Chemistry

Materials discovery for high-temperature, clean-energy applications using graph neural network models of vacancy defects and free-energy calculations We present a graph neural network modeling approach that fully automates the prediction of the DFT-relaxed vacancy formation enthalpy of any crystallographic site from its DFT-relaxed host structure. Applicable to arbitrary structures with an accuracy limited principally by the amount/diversity of the data on which it is trained, this model accelerates the screening of vacancy defects by many orders of magnitude by replacing the DFT supercell relaxations required for each symmetrically unique crystal site. It can thus be used off-the-shelf to rapidly screen 10,000s of crystal structures (which can contain millions of unique defects) from existing databases of DFT-relaxed crystal structures. This modeling approach therefore provides a significant screening and discovery capability for a plethora of applications in which vacancy defects are the primary driver of a material's utility. For example, by high-throughput screening the Materials Project's metal oxides, we rapidly "re-discover" and identify new high potential candidate materials for hydrogen generation via solar thermochemical water splitting and energy storage, for CO2 conversion via reverse water gas shift chemical looping, and for cathodes in solid oxide fuel cells. Thermodynamic modeling on the basis of the high-throughput screening results allows us to connect the predicted defect energies to high temperature process conditions relevant to the different application areas, and we extract the reduction entropies as an additional selection criterion for high-performance materials. Further model development and accumulation of additional training data will only serve to expand the significant utility of this generalizable defect model to solving materials discovery problems in clean energy applications and beyond. Vacancy defect ; Materials science ; Supercell ; Energy transformation ; Crystal (programming language) ; Computer science ; Nanotechnology ; Chemistry ; Physics ; Thermodynamics ; Crystallography Machine Learning in Materials Science ; Advancements in Solid Oxide Fuel Cells ; X-ray Diffraction in Crystallography