jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Carsten G. Staacke | Simon Wengert | Christian Künkel | Gábor Cśanyi | Karsten Reuter | Johannes T. Margraf
Source: ChemRxiv (preprint, 2021-11-24)
Field: Materials Science — Materials Chemistry

Kernel Charge Equilibration: Efficient and Accurate Prediction of Molecular Dipole Moments with a Machine-Learning Enhanced Electron Density Model State-of-the-art machine learning (ML) interatomic potentials use local representations of atomic environments to ensure linear scaling and size-extensivity. This implies a neglect of long-range interactions, most prominently related to electrostatics. To overcome this limitation, we herein present a ML framework for predicting charge distributions and their interactions termed kernel Charge Equilibration (kQEq). This model is based on classical charge equilibration models like QEq, expanded with an environment dependent electronegativity. In contrast to previously reported neural network models with a similar concept, kQEq takes advantage of the linearity of both QEq and Kernel Ridge Regression to obtain a closed-form linear algebra expression for training the models. Furthermore, we avoid the ambiguity of charge partitioning schemes by using dipole moments as reference data. As a first application, we show that kQEq can be used to generate accurate and highly data-efficient models for molecular dipole moments. Dipole ; Kernel (algebra) ; Statistical physics ; Electronegativity ; Charge (physics) ; Range (aeronautics) ; Chemistry ; Computer science ; Artificial intelligence ; Machine learning ; Algorithm ; Computational chemistry ; Physics ; Quantum mechanics ; Mathematics ; Materials science Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Surface Chemistry and Catalysis
Authors: Kevin P. Greenman | William H. Green | Rafael Gómez‐Bombarelli
Source: ChemRxiv (preprint, 2021-10-15)
Field: Materials Science — Materials Chemistry

Multi-fidelity prediction of molecular optical peaks with deep learning Optical properties are central to molecular design for many applications, including solar cells and biomedical imaging. A variety of ab initio and statistical methods have been developed for their prediction, each with a trade-off between accuracy, generality, and cost. Existing theoretical methods such as time-dependent density functional theory (TD-DFT) are generalizable across chemical space because of their robust physics-based foundations but still exhibit random and systematic errors with respect to experiment despite their high computational cost. Statistical methods can achieve high accuracy at a lower cost, but data sparsity and unoptimized molecule and solvent representations often limit their ability to generalize. Here, we utilize directed message passing neural networks (D-MPNNs) to represent both dye molecules and solvents for predictions of molecular absorption peaks in solution. Additionally, we demonstrate a multi-fidelity approach based on an auxiliary model trained on over 28,000 TD-DFT calculations that further improves accuracy and generalizability, as shown through rigorous splitting strategies. Combining several openly-available experimental datasets, we benchmark these methods against a state-of-the-art regression tree algorithm and compare the D-MPNN solvent representation to several alternatives. Finally, we explore the interpretability of the learned representations using dimensionality reduction and evaluate the use of ensemble variance as an estimator of the epistemic uncertainty in our predictions of molecular peak absorption in solution. The prediction methods proposed herein can be integrated with active learning, generative modeling, and experimental workflows to enable the more rapid design of molecules with targeted optical properties. Computer science ; Curse of dimensionality ; Interpretability ; Benchmark (surveying) ; Artificial intelligence ; Machine learning ; Dimensionality reduction ; Uncertainty quantification ; Algorithm Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Yoshinori Shiihara | Ryosuke Kanazawa | Daisuke MATSUNAKA | Ivan Lobzenko | Tomohito Tsuru | Masanori Kohyama | Hideki Mori
Source: arXiv (Cornell University) (preprint, 2021-06-24)
Field: Materials Science — Metals and Alloys

Artificial neural network molecular mechanics of iron grain boundaries This study reports grain boundary (GB) energy calculations for 46 symmetric-tilt GBs in alpha-iron using molecular mechanics based on an artificial neural network (ANN) potential and compares the results with calculations based on the density functional theory (DFT), the embedded atom method (EAM), and the modified EAM (MEAM). The results by the ANN potential are in excellent agreement with those of the DFT (5% on average), while the EAM and MEAM significantly differ from the DFT results (about 27% on average). In a uniaxial tensile calculation of Sigma 3 (1-12) GB, the ANN potential reproduced the brittle fracture tendency of the GB observed in the DFT while the EAM and MEAM showed mistakenly showed ductile behaviors. These results demonstrate the effectiveness of the ANN potential in grain boundary calculations of iron as a fast and accurate simulation highly in demand in the modern industrial world. Grain boundary ; Density functional theory ; Brittleness ; Tilt (camera) ; Artificial neural network ; Materials science ; Embedded atom model ; Molecular dynamics ; Atom (system on chip) ; Thermodynamics ; Statistical physics ; Condensed matter physics ; Physics ; Mathematics ; Computer science ; Metallurgy ; Artificial intelligence ; Geometry ; Quantum mechanics ; Microstructure Hydrogen embrittlement and corrosion behaviors in metals ; Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels
Authors: Zi-kai Hao | Haifeng Lv | D. Wang | Xiaojun Wu
Source: Chinese Journal of Chemical Physics (article, 2021-07-06)
Field: Chemistry — Inorganic Chemistry

High-performance chemical information database towards accelerating discovery of metal-organic frameworks for gas adsorption with machine learning Chemical structure searching based on databases and machine learning has attracted great attention recently for fast screening materials with target functionalities. To this end, we established a high-performance chemical structure database based on MYSQL engines, named MYDB. More than 160000 metal-organic frameworks (MOFs) have been collected and stored by using new retrieval algorithms for efficient searching and recommendation. The evaluations results show that MYDB could realize fast and efficient key-word searching against millions of records and provide real-time recommendations for similar structures. Combining machine learning method and materials database, we developed an adsorption model to determine the adsorption capacitor of metal-organic frameworks toward argon and hydrogen under certain conditions. We expect that MYDB together with the developed machine learning techniques could support large-scale, low-cost, and highly convenient structural research towards accelerating discovery of materials with target functionalities in the field of computational materials research. Computer science ; Database ; Adsorption ; Metal-organic framework ; Key (lock) ; Chemical database ; Machine learning ; Chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Morteza Adineh | Hamid Doostmohammadi
Source: SN Applied Sciences (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

A hybrid approach based on artificial neural network and cuckoo algorithm for optimization of the main cutting force during turning of Si brass alloys Abstract As an innovative approach in this study, artificial neural network and cuckoo algorithm have been applied to estimate and optimize the main cutting forces of various Si brass alloys during turning operation due to economic reasons. Accordingly, the chemical composition (Cu, Zn and Si contents) and process parameters (cutting speed, feed rate and depth of cut) are simultaneously implemented as input variables and the main cutting force is adjusted as an output variable. Moreover, the genetic algorithm is used to determine the optimum condition of the input parameters to obtain the lowest amounts of the main cutting force. Coupling of the hybrid cuckoo algorithm with artificial neural network has resulted in decreasing the mean absolute percentage error of the optimum structure (6-10-7-1) from 9.025 to 1.59E–6%. The validation of the proposed model has been done by performing the new set of experimental tests. The measured and predicted main cutting forces are in good agreement. The Si brass alloys including Zn equivalent about 44.97 wt% has the lowest main cutting force due to the formation of the Widmanstäetten morphologies in the microstructure. The outcome of this study may be useful for machining industry of the free-cutting Si brasses. Brass ; Cuckoo search ; Artificial neural network ; Machining ; Microstructure ; Coupling (piping) ; Materials science ; Genetic algorithm ; Algorithm ; Metallurgy ; Mechanical engineering ; Computer science ; Engineering ; Mathematics ; Mathematical optimization ; Artificial intelligence ; Copper Metallurgy and Material Science ; Aluminum Alloy Microstructure Properties ; Materials Engineering and Processing
Authors: Chang Liu | Erina Fujita | Yukari Katsura | Yuki Inada | Asuka Ishikawa | Ryuji Tamura | Kaoru Kimura | Ryo Yoshida
Source: Advanced Materials (article, 2021-09-01)
Field: Materials Science — Materials Chemistry

Machine Learning to Predict Quasicrystals from Chemical Compositions (Adv. Mater. 36/2021) Quasicrystals In article number 2102507, Kaoru Kimura, Ryo Yoshida, and co-workers demonstrate that machine-learning algorithms can predict the chemical composition of new quasicrystals. Furthermore, analyzing the input–output relationships black-boxed into the machine-learning model, they successfully identify nontrivial empirical equations interpretable by humans that describe the conditions necessary for stable quasicrystal formation. This is the first step toward understanding the formation mechanism of quasicrystals, which has been long sought in quasicrystal research. Quasicrystal ; Materials science ; Mechanism (biology) ; Artificial intelligence ; Machine learning ; Statistical physics ; Condensed matter physics ; Computer science ; Physics ; Quantum mechanics Quasicrystal Structures and Properties
Authors: Xinyu Chen | Yufeng Xie | Yaochen Sheng | Hongwei Tang | Zeming Wang | Yu Wang | Yin Wang | Fuyou Liao | Jingyi Ma | Xiaojiao Guo | Ling Tong | Hanqi Liu | Hao Liu | Tianxiang Wu | Jiaxin Cao | Sitong Bu | Hui Shen | Fuyu Bai | Huang Da-Ming | Jianan Deng | Antoine Riaud | Zihan Xu | Chenjian Wu | Shiwei Xing | Ye Lu | Shunli Ma | Zhengzong Sun | Zhongying Xue | Zengfeng Di | Xiao Gong | Wei Zhang | Peng Zhou | Jing Wan | Wenzhong Bao
Source: Research Square (preprint, 2021-01-27)
Field: Materials Science — Materials Chemistry

Wafer-Scale Functional Circuits Based on Two Dimensional Semiconductors with Fabrication Optimized by Machine Learning Wafer ; Fabrication ; Semiconductor ; Electronic circuit ; Scale (ratio) ; Wafer fabrication ; Semiconductor device fabrication ; Wafer-scale integration ; Materials science ; Integrated circuit ; Computer science ; Electronic engineering ; Optoelectronics ; Electrical engineering ; Engineering ; Physics 2D Materials and Applications ; Nanowire Synthesis and Applications ; Advanced Memory and Neural Computing
Authors: Lorenzo Ferretti | Andrea Cini | Georgios Zacharopoulos | Cesare Alippi | Laura Pozzi
Source: arXiv (Cornell University) (preprint, 2021-11-29)
Field: Materials Science — Materials Chemistry

A Graph Deep Learning Framework for High-Level Synthesis Design Space Exploration The design of efficient hardware accelerators for high-throughput data-processing applications, e.g., deep neural networks, is a challenging task in computer architecture design. In this regard, High-Level Synthesis (HLS) emerges as a solution for fast prototyping application-specific hardware starting from a behavioural description of the application computational flow. This Design-Space Exploration (DSE) aims at identifying Pareto optimal synthesis configurations whose exhaustive search is often unfeasible due to the design-space dimensionality and the prohibitive computational cost of the synthesis process. Within this framework, we effectively and efficiently address the design problem by proposing, for the first time in the literature, graph neural networks that jointly predict acceleration performance and hardware costs of a synthesized behavioral specification given optimization directives. The learned model can be used to rapidly approach the Pareto curve by guiding the DSE, taking into account performance and cost estimates. The proposed method outperforms traditional HLS-driven DSE approaches, by accounting for arbitrary length of computer programs and the invariant properties of the input. We propose a novel hybrid control and data flow graph representation that enables training the graph neural network on specifications of different hardware accelerators; the methodology naturally transfers to unseen data-processing applications too. Moreover, we show that our approach achieves prediction accuracy comparable with that of commonly used simulators without having access to analytical models of the HLS compiler and the target FPGA, while being orders of magnitude faster. Finally, the learned representation can be exploited for DSE in unexplored configuration spaces by fine-tuning on a small number of samples from the new target domain. Computer science ; Design space exploration ; High-level synthesis ; Compiler ; Data-flow analysis ; Design flow ; Field-programmable gate array ; Artificial neural network ; Graph ; Computer engineering ; Computer architecture ; Engineering design process ; Curse of dimensionality ; Data flow diagram ; Artificial intelligence ; Theoretical computer science ; Embedded system ; Programming language Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Advanced Memory and Neural Computing
Authors: JingChun Wang | DaDi Zhang | Rui–Xue Xu | ChiYung Yam | GuanHua Chen | Xiao Zheng
Source: ChemRxiv (preprint, 2021-05-17)
Field: Materials Science — Materials Chemistry

Improving Density Functional Prediction of Molecular Thermochemical Properties with a Machine-Learning-Corrected Generalized Gradient Approximation The past decade has seen an increasing interest in designing sophisticated density functional approximations (DFAs) by integrating the power of machine learning (ML) techniques. However, application of the ML-based DFAs is often confined to simple model systems. In this work, we construct an ML correction to the widely used Perdew-Burke-Ernzerhof (PBE) functional by establishing a semilocal mapping from the electron density and reduced gradient to the exchange-correlation energy density. The resulting ML-corrected PBE is immediately applicable to any real molecule, and yields significantly improved heats of formation while preserving the accuracy for other thermochemical and kinetic properties. This work highlights the prospect of combining the power of data-driven ML methods with physics-inspired derivations for reaching the heaven of chemical accuracy. Density functional theory ; Computer science ; Construct (python library) ; Work (physics) ; Kinetic energy ; Simple (philosophy) ; Statistical physics ; Algorithm ; Thermodynamics ; Chemistry ; Computational chemistry ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Thermodynamics and Molecular Structure
Authors: Jingchun Wang | DaDi Zhang | Rui–Xue Xu | ChiYung Yam | GuanHua Chen | Xiao Zheng
Source: ChemRxiv (preprint, 2021-05-17)
Field: Materials Science — Materials Chemistry

Improving Density Functional Prediction of Molecular Thermochemical Properties with a Machine-Learning-Corrected Generalized Gradient Approximation The past decade has seen an increasing interest in designing sophisticated density functional approximations (DFAs) by integrating the power of machine learning (ML) techniques. However, application of the ML-based DFAs is often confined to simple model systems. In this work, we construct an ML correction to the widely used Perdew-Burke-Ernzerhof (PBE) functional by establishing a semilocal mapping from the electron density and reduced gradient to the exchange-correlation energy density. The resulting ML-corrected PBE is immediately applicable to any real molecule, and yields significantly improved heats of formation while preserving the accuracy for other thermochemical and kinetic properties. This work highlights the prospect of combining the power of data-driven ML methods with physics-inspired derivations for reaching the heaven of chemical accuracy. Density functional theory ; Construct (python library) ; Work (physics) ; Kinetic energy ; Computer science ; Simple (philosophy) ; Statistical physics ; Algorithm ; Thermodynamics ; Chemistry ; Computational chemistry ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Thermodynamics and Molecular Structure
Authors: Leif D. Jacobson | James Stevenson | Farhad Ramezanghorbani | Delaram Ghoreishi | Karl Leswing | Ed Harder | Robert Abel
Source:  (preprint, 2021-12-09)
Field: Materials Science — Materials Chemistry

Transferable Neural Network Potential Energy Surfaces for Closed-Shell Organic Molecules: Extension to Ions Transferable high dimensional neural network potentials (HDNNP) have shown great promise as an avenue to increase the accuracy and domain of applicability of existing atomistic force fields for organic systems relevant to life science. We have previously reported such a potential (Schrödinger-ANI) that has broad coverage of druglike molecules. We extend that work here to cover ionic and zwitterionic druglike molecules expected to be relevant to drug discovery research activities. We report a novel HDNNP architechture, which we call QRNN, that predicts atomic charges and uses these charges as descriptors in an energy model which delivers conformational energies within chemical accuracy when measured against the reference theory it is trained to. Further, we find that delta learning based on a semi-empirical level of theory approximately halves the errors. We test the models on torsion energy profiles, relative conformational energies, geometric parameters and relative tautomer errors. Artificial neural network ; Molecule ; Potential energy ; Organic molecules ; Ion ; Torsion (gastropod) ; Extension (predicate logic) ; Ionic bonding ; Work (physics) ; Energy (signal processing) ; Computer science ; Chemistry ; Physics ; Statistical physics ; Chemical physics ; Artificial intelligence ; Atomic physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Hongbin Zhang | Teng Long | Yixuan Zhang | Nuno M. Fortunato | Chen Shen | Oliver Gutfleisch
Source: Acta Crystallographica Section A Foundations and Advances (article, 2021-08-14)
Field: Materials Science — Materials Chemistry

CCDCGAN: deep learning prediction of crystal structures Autonomous materials discovery with desired properties is one of the ultimate goals for materials science [1]. In this work, we have developed constrained crystal deep convolutional generative adversarial networks (CCDCGAN, Figure In particular, physical properties can be optimized in the latent space, where the formation energy is considered in the current model so that stable structures are predicted directly. We have successfully applied the approach on a randomly chosen binary Bi-Se system and observed that most known phases can be validated with quite a few distinct structures predicted [3]. Furthermore, trained using more than 50,000 compounds in the Materials Project database, we recently extended the algorithm to multicomponent systems coving most elements in the periodic table. As shown in Figure Detailed analysis reveals that the approach can be used to predict novel crystal structures for various materials systems, and the generation efficiency can be further improved by considering a larger training set. It is expected that the other physical properties (such as band gaps) can be optimized in the latent space as well, giving us the chance to perform multi-objective optimization in the future. Figure 1. (a) Workflow of CCDCGAN; (b) Convex hull of the Cd-Li system highlighting the predicted structures. Materials science ; Crystal structure prediction ; Artificial intelligence ; Crystallography ; Crystal structure ; Computer science ; Chemistry X-ray Diffraction in Crystallography ; Machine Learning in Materials Science
Authors: Noushin Omidvar | Hongliang Xin
Source: Trends in Chemistry (article, 2021-10-12)
Field: Materials Science — Materials Chemistry

Algorithm-derived feature representations for explainable AI in catalysis Machine learning (ML) has emerged as a critical tool in catalysis, attributed to its capability of finding complex patterns in high dimensional and heterogeneous data. A recently published article in Chem Catalysis (Esterhuizen et al.) used unsupervised ML for uncovering electronic and geometric descriptors of the surface reactivity of metal alloys and oxides. Machine learning (ML) has emerged as a critical tool in catalysis, attributed to its capability of finding complex patterns in high dimensional and heterogeneous data. A recently published article in Chem Catalysis (Esterhuizen et al.) used unsupervised ML for uncovering electronic and geometric descriptors of the surface reactivity of metal alloys and oxides. Reactivity (psychology) ; Catalysis ; Feature (linguistics) ; Surface (topology) ; Computer science ; Artificial intelligence ; Materials science ; Chemistry ; Mathematics ; Organic chemistry ; Geometry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Topic Modeling
Authors: Elizabeth Lee | Thomas Ludwig | Boyuan Yu | Aayush R. Singh | François Gygi | Jens K. Nørskov | Juan Pablo
Source: ChemRxiv (preprint, 2021-03-04)
Field: Materials Science — Materials Chemistry

Neural Network Sampling of the Free Energy Landscape for Nitrogen Dissociation on Ruthenium In heterogeneous catalysis, free energy profiles of reactions govern the mechanisms, rates, and equilibria. Energetics are conventionally computed using the harmonic approximation (HA), which requires determination of critical states a priori. Here, we use neural networks to efficiently sample and directly calculate the free energy surface (FES) of a prototypical heterogeneous catalysis reaction—the dissociation of molecular nitrogen on ruthenium—at density functional theory-level accuracy. We find vibrational entropy of surface atoms, often neglected in HA for transition metal catalysts, contributes significantly to the reaction barrier. The minimum free energy path for dissociation reveals an “on-top” adsorbed molecular state prior to the transition state. While a previously reported flat-lying molecular metastable state can be identified in the potential energy surface, it is absent in the FES at relevant reaction temperatures. These findings demonstrate the importance of identifying critical points self-consistently on the FES for reactions that involve considerable entropic effects. Metastability ; Catalysis ; Dissociation (chemistry) ; Ruthenium ; Potential energy surface ; Chemical physics ; Energy landscape ; Chemistry ; Transition state ; Density functional theory ; Potential energy ; Thermodynamics ; Molecule ; Computational chemistry ; Atomic physics ; Physical chemistry ; Physics ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Chemical Physics Studies