jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Joshua Staker | Kyle Marshall | Karl Leswing | Tim Robertson | Mathew D. Halls | Alexander Goldberg | Tsuguo Morisato | Hiroyuki Maeshima | T. Ando | Hideyuki Arai | Masaru Sasago | Eiji Fujii | Nobuyuki Matsuzawa
Source: The Journal of Physical Chemistry A (article, 2022-08-19)
Field: Materials Science — Materials Chemistry

<i>De Novo</i>Design of Molecules with Low Hole Reorganization Energy Based on a Quarter-Million Molecule DFT Screen: Part 2 Organic semiconductors have many desirable properties including improved manufacturing and flexible mechanical properties. Due to the vastness of chemical space, it is essential to efficiently explore chemical space when designing new materials, including through the use of generative techniques. New generative machine learning methods for molecular design continue to be published in the literature at a significant rate but successfully adapting methods to new chemistry and problem domains remains difficult. These challenges necessitate continual method evaluation to probe method viability for use in alternative applications not covered in the original works. In continuation of our previous work, we evaluate four additional machine-learning-based de novo methods for generating molecules with high predicted hole mobility for use in semiconductor applications. The four generative methods evaluated here are (1) Molecule Deep Q-Networks (MolDQN), which utilizes Deep-Q learning to directly optimize molecular structure graphs for desired properties instead of generating SMILES, (2) Graph-based Genetic Algorithm (GraphGA), which uses a genetic algorithm for optimization where crossovers and mutations are defined in terms of RDKit's reaction SMILES, (3) Generative Tensorial Reinforcement Learning (GENTRL), which is a variational autoencoder (VAE) with a learned prior distribution and optimized using reinforcement learning, and (4) Monte Carlo tree search exploration of chemical space in conjunction with a recurrent neural network (RNN) decoder (ChemTS). The generated molecules were evaluated using density functional theory (DFT) and we discovered better performing molecules with the GraphGA method compared to the other approaches. Chemical space ; Computer science ; Reinforcement learning ; Artificial intelligence ; Autoencoder ; Generative grammar ; Artificial neural network ; Space (punctuation) ; Machine learning ; Theoretical computer science ; Algorithm ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Molecular Junctions and Nanostructures
Authors: Kan Hatakeyama‐Sato | Hiroki Adachi | Momoka Umeki | Takahiro Kashikawa | Kôichi Kimura | Kenichi Oyaizu
Source: Macromolecular Rapid Communications (article, 2022-06-27)
Field: Materials Science — Materials Chemistry

Automated Design of Li <sup>+</sup> ‐Conducting Polymer by Quantum‐Inspired Annealing Automated molecule design by computers is an essential topic in materials informatics. Still, generating practical structures is not easy because of the difficulty in treating material stability, synthetic difficulty, mechanical properties, and other miscellaneous parameters, often leading to the generation of junk molecules. The problem is tackled by introducing supervised/unsupervised machine learning and quantum-inspired annealing. This autonomous molecular design system can help experimental researchers discover practical materials more efficiently. Like the human design process, new molecules are explored based on knowledge of existing compounds. A new solid-state polymer electrolyte for lithium-ion batteries is designed and synthesized, giving a promising room temperature conductivity of 10<sup>-5</sup> S cm<sup>-1</sup> with reasonable thermal, chemical, and mechanical properties. Polymer ; Molecule ; Computer science ; Annealing (glass) ; Simulated annealing ; Electrolyte ; Materials science ; Nanotechnology ; Conductive polymer ; Chemistry ; Machine learning ; Organic chemistry ; Composite material ; Electrode Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Advanced Memory and Neural Computing
Authors: Fiorella Cravero | Mónica F. Díaz | Ignacio Ponzoni
Source: The Journal of Chemical Physics (article, 2022-05-09)
Field: Materials Science — Materials Chemistry

Polymer informatics for QSPR prediction of tensile mechanical properties. Case study: Strength at break The artificial intelligence-based prediction of the mechanical properties derived from the tensile test plays a key role in assessing the application profile of new polymeric materials, especially in the design stage, prior to synthesis. This strategy saves time and resources when creating new polymers with improved properties that are increasingly demanded by the market. A quantitative structure-property relationship (QSPR) model for tensile strength at break is presented in this work. The QSPR methodology applied here is based on machine learning tools, visual analytics methods, and expert-in-the-loop strategies. From the whole study, a QSPR model composed of five molecular descriptors that achieved a correlation coefficient of 0.9226 is proposed. We applied visual analytics tools at two levels of analysis: a more general one in which models are discarded for redundant information metrics and a deeper one in which a chemistry expert can make decisions on the composition of the model in terms of subsets of molecular descriptors, from a physical-chemical point of view. In this way, with the present work, we close a contribution cycle to polymer informatics, providing QSPR models oriented to the prediction of mechanical properties related to the tensile test. Quantitative structure–activity relationship ; Computer science ; Molecular descriptor ; Ultimate tensile strength ; Informatics ; Machine learning ; Artificial intelligence ; Correlation coefficient ; Work (physics) ; Data mining ; Biochemical engineering ; Materials science ; Mechanical engineering ; Engineering ; Composite material Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Pai Li | Xiongzhi Zeng | Zhenyu Li
Source: JACS Au (article, 2022-01-19)
Field: Materials Science — Materials Chemistry

Understanding High-Temperature Chemical Reactions on Metal Surfaces: A Case Study on Equilibrium Concentration and Diffusivity of C<sub><i>x</i></sub>H<sub><i>y</i></sub> on a Cu(111) Surface Chemical reactions on metal surfaces are important in various processes such as heterogeneous catalysis and nanostructure growth. At moderate or lower temperatures, these reactions generally follow the minimum energy path, and temperature effects can be reasonably described by a harmonic oscillator model. At a high temperature approaching the melting point of the substrate, general behaviors of surface reactions remain elusive. In this study, by taking hydrocarbon species adsorbed on Cu(111) as a model system and performing extensive molecular dynamics simulations powered by machine learning potentials, we identify several important high-temperature effects, including local chemical environment, surface atom mobility, and substrate thermal expansion. They affect different aspects of a high-temperature surface reaction in different ways. These results deepen our understanding of high-temperature reactions. Thermal diffusivity ; Chemical reaction ; Adsorption ; Melting point ; Catalysis ; Chemical physics ; Substrate (aquarium) ; Metal ; Chemistry ; Thermodynamics ; Surface diffusion ; Hydrocarbon ; Physical chemistry ; Materials science ; Organic chemistry ; Physics Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Advanced Chemical Physics Studies
Authors: Takumi Komura | Kosuke Sakano | Yasuhiko Igarashi | Hiromichi Numazawa | Hiroaki Imai | Yuya Oaki
Source: ACS Applied Energy Materials (article, 2022-07-06)
Field: Materials Science — Materials Chemistry

A Capacity-Prediction Model for Exploration of Organic Anodes: Discovery of 5-Formylsalicylic Acid as a High-Performance Anode Active Material Development of high-performance organic energy storage is one of the important challenges in recent materials science. Molecular design and synthesis have potential for enhancement of the performances. Efficient exploration and design of the molecules are required in a wide search space. In the present work, a capacity prediction model for organic anodes was constructed on small experimental data by sparse modeling, a method of machine learning, combined with our chemical insights. The straightforward linear regression model facilitated discovery of a high-performance active material for organic anodes in a limited number of experiments. A recommended compound, 5-formylsalicylic acid (SA-CHO), showed one of the highest performances in recent works, i.e., a specific capacity of 873 mA h g–1 at 100 mA g–1 (sample number: n = 3) with rate performance and cycle stability. The model can be applied to explore organic anode active materials with higher specific capacity. Anode ; Chemical space ; Organic molecules ; Computer science ; Work (physics) ; Materials science ; Linear regression ; Process engineering ; Biological system ; Molecule ; Drug discovery ; Chemistry ; Machine learning ; Electrode ; Mechanical engineering ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Advancements in Battery Materials ; Conducting polymers and applications
Authors: Xiang Fu | Tian Xie | Nathan Rebello | Bradley D. Olsen | Tommi Jaakkola
Source: arXiv (Cornell University) (preprint, 2022-04-21)
Field: Materials Science — Materials Chemistry

Simulate Time-integrated Coarse-grained Molecular Dynamics with Multi-Scale Graph Networks Molecular dynamics (MD) simulation is essential for various scientific domains but computationally expensive. Learning-based force fields have made significant progress in accelerating ab-initio MD simulation but are not fast enough for many real-world applications due to slow inference for large systems and small time steps (femtosecond-level). We aim to address these challenges by learning a multi-scale graph neural network that directly simulates coarse-grained MD with a very large time step (nanosecond-level) and a novel refinement module based on diffusion models to mitigate simulation instability. The effectiveness of our method is demonstrated in two complex systems: single-chain coarse-grained polymers and multi-component Li-ion polymer electrolytes. For evaluation, we simulate trajectories much longer than the training trajectories for systems with different chemical compositions that the model is not trained on. Structural and dynamical properties can be accurately recovered at several orders of magnitude higher speed than classical force fields by getting out of the femtosecond regime. Computer science ; Inference ; Molecular dynamics ; Graph ; Statistical physics ; Computational science ; Artificial intelligence ; Theoretical computer science ; Physics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Topic Modeling
Authors: Natália R. S. Araújo | Rita C. O. Sebastião | Maria Betânia de Freitas Marques | Wagner da Nova Mussel | María Irene Yoshida | Luciano Sindra Virtuoso
Source: Thermal Science and Engineering Progress (article, 2022-09-29)
Field: Materials Science — Materials Chemistry

Multilayer perceptron neural network applied to TG dynamic data of biopolymer chitosan – A robust tool to study the kinetics of solid thermal decomposition Biopolymer ; Chitosan ; Kinetics ; Artificial neural network ; Decomposition ; Materials science ; Thermal decomposition ; Multilayer perceptron ; Biological system ; Chemical engineering ; Computer science ; Artificial intelligence ; Chemistry ; Composite material ; Polymer ; Engineering ; Physics ; Organic chemistry Thermal and Kinetic Analysis ; Chemistry and Chemical Engineering ; Nanocomposite Films for Food Packaging
Authors: Jan Weinreich | Dominik Lemm | Guido Falk von Rudorff | O. Anatole von Lilienfeld
Source: The Journal of Chemical Physics (article, 2022-07-13)
Field: Materials Science — Materials Chemistry

<i>Ab initio</i> machine learning of phase space averages Equilibrium structures determine material properties and biochemical functions. We here propose to machine learn phase space averages, conventionally obtained by ab initio or force-field-based molecular dynamics (MD) or Monte Carlo (MC) simulations. In analogy to ab initio MD, our ab initio machine learning (AIML) model does not require bond topologies and, therefore, enables a general machine learning pathway to obtain ensemble properties throughout the chemical compound space. We demonstrate AIML for predicting Boltzmann averaged structures after training on hundreds of MD trajectories. The AIML output is subsequently used to train machine learning models of free energies of solvation using experimental data and to reach competitive prediction errors (mean absolute error ∼ 0.8 kcal/mol) for out-of-sample molecules—within milliseconds. As such, AIML effectively bypasses the need for MD or MC-based phase space sampling, enabling exploration campaigns of Boltzmann averages throughout the chemical compound space at a much accelerated pace. We contextualize our findings by comparison to state-of-the-art methods resulting in a Pareto plot for the free energy of solvation predictions in terms of accuracy and time. Ab initio ; Statistical physics ; Solvation ; Force field (fiction) ; Artificial intelligence ; Machine learning ; Phase space ; Computer science ; Chemistry ; Physics ; Thermodynamics ; Molecule ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Mohsen Tamtaji | Xuyun Guo | Abhishek Tyagi | Patrick Ryan Galligan | Zhenjing Liu | Alexander Perez Roxas | Hongwei Liu | Yuting Cai | Hoilun Wong | Lun Zeng | Jianbo Xie | Yucong Du | Zhigang Hu | Dong Lu | William A. Goddard | Ye Zhu | Zhengtang Luo
Source: ACS Applied Materials & Interfaces (article, 2022-10-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning-Aided Design of Gold Core–Shell Nanocatalysts toward Enhanced and Selective Photooxygenation We demonstrate the use of the machine learning (ML) tools to rapidly and accurately predict the electric field as a guide for designing core-shell Au-silica nanoparticles to enhance <sup>1</sup>O<sub>2</sub> sensitization and selectivity of organic synthesis. Based on the feature importance analysis, obtained from a deep neural network algorithm, we found a general and linear dependent descriptor (θ ∝ <i>aD</i><sup>0.25</sup><i>t</i><sup>-1</sup>, where <i>a</i>, <i>D</i>, and <i>t</i> are the shape constant, size of metal nanoparticles, and distance from the metal surface) for the electric field around the core-shell plasmonic nanoparticle. Directed by the new descriptor, we synthesized gold-silica nanoparticles and validated their plasmonic intensity using scanning transmission electron microscopy-electron energy loss spectroscopy (STEM-EELS) mapping. The nanoparticles with θ = 0.40 demonstrate an ∼3-fold increase in the reaction rate of photooxygenation of anthracene and 4% increase in the selectivity of photooxygenation of dihydroartemisinic acid (DHAA), a long-standing goal in organic synthesis. In addition, the combination of ML and experimental investigations shows the synergetic effect of plasmonic enhancement and fluorescence quenching, leading to enhancement for <sup>1</sup>O<sub>2</sub> generation. Our results from time-dependent density functional theory (TD-DFT) calculations suggest that the presence of an electric field can favor intersystem crossing (ISC) of methylene blue to enhance <sup>1</sup>O<sub>2</sub> generation. The strategy reported here provides a data-driven catalyst preparation method that can significantly reduce experimental cost while paving the way for designing photocatalysts for organic drug synthesis. Nanomaterial-based catalyst ; Materials science ; Photooxygenation ; Nanoparticle ; Plasmon ; Nanotechnology ; Singlet oxygen ; Optoelectronics ; Organic chemistry ; Chemistry Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Advanced Photocatalysis Techniques
Authors: Srinivas Rangarajan | Huijie Tian
Source: Current Opinion in Chemical Engineering (article, 2022-08-26)
Field: Materials Science — Materials Chemistry

Improving the predictive power of microkinetic models via machine learning Computer science ; Field (mathematics) ; Reliability (semiconductor) ; Machine learning ; Artificial intelligence ; Power (physics) ; Mathematics ; Thermodynamics ; Physics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Priya Selvamany | Gowri Sree Varadarajan | Chillu Naresh | R. Sarathi
Source: Polymers (article, 2022-04-15)
Field: Materials Science — Materials Chemistry

Investigation of XLPE Cable Insulation Using Electrical, Thermal and Mechanical Properties, and Aging Level Adopting Machine Learning Techniques Hydrothermal and chemical aging tests on a 230 kV cross-linked polyethylene (XLPE) insulation cable were carried out in the present study to evaluate the degradation and aging levels qualitatively. The samples were subjected to water aging at a temperature of 80 °C, and in an aqueous ionic solution of CuSO<sub>4</sub> at room temperature. The diffusion coefficient results indicated that the ion migration was not at the same rate in the aging conditions. The diffusion coefficient-D-of the sample immersed in an aqueous CuSO<sub>4</sub> solution was lower than the hydrothermally aged specimens. The hydrophobicity of aged specimens decreased considerably compared to unaged samples. The distribution of trapped charges was quantitatively characterized. The presence of shallow trap energy states were observed in unaged XLPE, whereas the deep trap sites were noticed in aged specimens. In addition, the charge trap characteristics were different for positive and negative charge deposition. Various material characterization techniques, viz. dynamic mechanical analysis (DMA), tensile, contact angle, and LIBS, were further employed on the aged and virgin specimens. The tensile behavior of the hydrothermally aged specimen was degraded due to the oxidised regions, which had formed a weak spot against the mechanical stress. Reduced glass transition temperature and increased loss tangent measurements were noticed for aged specimens over their unaged counterparts. Machine learning techniques, such as the principal component analysis (PCA) and the artificial neural network (ANN) analysis, were performed on LIBS spectral data of the samples to classify the aging mechanisms qualitatively. Materials science ; Composite material ; Ultimate tensile strength ; Polyethylene ; Accelerated aging ; Aqueous solution ; Analytical Chemistry (journal) ; Chemistry ; Chromatography High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrical Fault Detection and Protection
Authors: Wenfei Li | Qi Ou | Yixiao Chen | Yu Cao | Renxi Liu | Chunyi Zhang | Daye Zheng | Chun Cai | Xifan Wu | Handong Wang | Mohan Chen | Linfeng Zhang
Source: The Journal of Physical Chemistry A (article, 2022-12-01)
Field: Materials Science — Materials Chemistry

DeePKS + ABACUS as a Bridge between Expensive Quantum Mechanical Models and Machine Learning Potentials Recently, the development of machine learning (ML) potentials has made it possible to perform large-scale and long-time molecular simulations with the accuracy of quantum mechanical (QM) models. However, for different levels of QM methods, such as density functional theory (DFT) at the meta-GGA level and/or with exact exchange, quantum Monte Carlo, etc., generating a sufficient amount of data for training an ML potential has remained computationally challenging due to their high cost. In this work, we demonstrate that this issue can be largely alleviated with Deep Kohn-Sham (DeePKS), an ML-based DFT model. DeePKS employs a computationally efficient neural network-based functional model to construct a correction term added upon a cheap DFT model. Upon training, DeePKS offers closely matched energies and forces compared with high-level QM method, but the number of training data required is orders of magnitude less than that required for training a reliable ML potential. As such, DeePKS can serve as a bridge between expensive QM models and ML potentials: one can generate a decent amount of high-accuracy QM data to train a DeePKS model and then use the DeePKS model to label a much larger amount of configurations to train an ML potential. This scheme for periodic systems is implemented in a DFT package ABACUS, which is open source and ready for use in various applications. Computer science ; Bridge (graph theory) ; Density functional theory ; Quantum ; Abacus (architecture) ; Monte Carlo method ; Quantum Monte Carlo ; Construct (python library) ; Artificial intelligence ; Algorithm ; Chemistry ; Mathematics ; Computational chemistry ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Advanced Chemical Physics Studies
Authors: Huaijun Sun | Chao Zhang | Weiyi Xia | Ling Tang | Renhai Wang | Georgiy Akopov | Nethmi W. Hewage | Kai‐Ming Ho | Kirill Kovnir | Cai‐Zhuang Wang
Source: Inorganic Chemistry (article, 2022-10-11)
Field: Materials Science — Materials Chemistry

Machine Learning-Guided Discovery of Ternary Compounds Containing La, P, and Group 14 Elements We integrate a deep machine learning (ML) method with first-principles calculations to efficiently search for the energetically favorable ternary compounds. Using La-Si-P as a prototype system, we demonstrate that ML-guided first-principles calculations can efficiently explore crystal structures and their relative energetic stabilities, thus greatly accelerate the pace of material discovery. A number of new La-Si-P ternary compounds with formation energies less than 30 meV/atom above the known ternary convex hull are discovered. Among them, the formation energies of La<sub>5</sub>SiP<sub>3</sub> and La<sub>2</sub>SiP phases are only 2 and 10 meV/atom, respectively, above the convex hull. These two compounds are dynamically stable with no imaginary phonon modes. Moreover, by replacing Si with heavier-group 14 elements in the eight lowest-energy La-Si-P structures from our ML-guided predictions, a number of low-energy La-X<i>-</i>P phases (X = Ge, Sn, Pb) are predicted. Ternary operation ; Chemistry ; Atom (system on chip) ; Convex hull ; Group (periodic table) ; Crystallography ; Regular polygon ; Energy (signal processing) ; Computational chemistry ; Atomic physics ; Geometry ; Quantum mechanics ; Physics ; Parallel computing ; Organic chemistry Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Boron and Carbon Nanomaterials Research
Authors: Zhenze Yang | Yu-Chuan Hsu | Markus J. Buehler
Source: Journal of the Mechanics and Physics of Solids (article, 2022-10-12)
Field: Materials Science — Materials Chemistry

Generative multiscale analysis of de novo proteome-inspired molecular structures and nanomechanical optimization using a VoxelPerceiver transformer model Transformer ; Computer science ; Artificial neural network ; von Mises yield criterion ; Bayesian optimization ; Materials science ; Generative grammar ; Biological system ; Artificial intelligence ; Structural engineering ; Finite element method ; Engineering Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Calcium Carbonate Crystallization and Inhibition