jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yuquan Zhu | Tao Xu | Qinghua Wei | Hongxin Yang | Takahiro Shimada | Takayuki Kitamura | Tong‐Yi Zhang
Source: arXiv (Cornell University) (preprint, 2021-02-28)
Field: Materials Science — Materials Chemistry

Accelerated design of linear-superelastic Ti-Nb nanocomposite alloys with ultralow modulus via high-throughput phase-field simulations and machine learning The controlled design of martensitic transformation (MT) to achieve specific properties is crucial for the innovative application of shape memory alloys (SMAs) in advanced technologies. Herein, we explore and design the MT behaviors and the mechanical properties of Ti-Nb nanocomposites by combining high-throughput phase-field simulations and machine learning (ML) approaches. Based on the systematic phase-field simulations, we obtain data sets of the mechanical properties for various nanocomposites constructed by four macroscopic degrees of freedom, which can be employed to design and optimize the microstructures for different applications. To accelerate the phase-field screening of the desired metallic biomaterials, a ML assisted strategy is adopted to perform multi-objective optimization of the mechanical properties, through which promising nanocomposite configurations are pre-screened for the next set of phase-field simulations. With the ML guided simulations, an optimized candidate composed of Nb-rich matrix and Nb-lean nanofillers that exhibits a combination of unprecedented mechanical properties, including ultra-low modulus, linear super-elasticity, and near-hysteresis-free is designed. The exceptional mechanical properties in the nanocomposite originate from optimized continuous MT rather than a sharp first-order transition, which is common in typical SMAs. This work provides a new computational approach and design concept for developing novel functional materials with extraordinary properties. Nanocomposite ; Materials science ; Shape-memory alloy ; Smart material ; Diffusionless transformation ; Phase (matter) ; Mechanical engineering ; Material Design ; Modulus ; Field (mathematics) ; Hysteresis ; Microstructure ; Nanotechnology ; Composite material ; Martensite ; Engineering ; Physics Shape Memory Alloy Transformations ; Titanium Alloys Microstructure and Properties ; High Entropy Alloys Studies
Authors: Ruimin Ma | Hanfeng Zhang | Tengfei Luo
Source: ChemRxiv (preprint, 2021-12-09)
Field: Materials Science — Materials Chemistry

Explore High Thermal Conductivity Amorphous Polymers using Reinforcement Learning Developing amorphous polymers with desirable thermal conductivity has significant implications, as they are ubiquitous in applications where thermal transport is critical. Conventional Edisonian approaches are slow and without guarantee of success in material development. In this work, using a reinforcement learning scheme, we design polymers with thermal conductivity above 0.4 W/m- K. We leverage a machine learning model trained against 469 thermal conductivity data calculated from high-throughput molecular dynamics (MD) simulations as the surrogate for thermal conductivity prediction, and we use a recurrent neural network trained with around one million virtual polymer structures as a polymer generator. For all newly generated polymers with thermal conductivity > 0.400 W/m-K, we have evaluated their synthesizability by calculating the synthesis accessibility score and validated the thermal conductivity of selected polymers using MD simulations. The best thermally conductive polymer designed has a MD-calculated thermal conductivity of 0.693 W/m-K, which is also estimated to be easily synthesizable. Our demonstrated inverse design scheme based on reinforcement learning may advance polymer development with target properties, and the scheme can also be generalized to other materials development tasks for different applications. Thermal conductivity ; Materials science ; Polymer ; Amorphous solid ; Reinforcement learning ; Leverage (statistics) ; Conductivity ; Thermal ; Composite material ; Computer science ; Machine learning ; Thermodynamics ; Physics ; Chemistry ; Organic chemistry ; Physical chemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Thermal properties of materials
Authors: John Ellis | Sivasankaran Rajamanickam | Normand A. Modine | Aidan P. Thompson | John Stephens | Attila Cangi
Source:  (article, 2021-03-01)
Field: Materials Science — Materials Chemistry

Accelerating Multiscale Materials Modeling with Machine Learning. Abstract not provided. Computer science ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Enhanced Oil Recovery Techniques
Authors: Sayan Basak | Forrest Simmons | Pavel Salev | Iván K. Schuller | Lionel Aigouy | A. Zimmers | E. W. Carlson
Source: Bulletin of the American Physical Society (article, 2021-03-18)
Field: Materials Science — Materials Chemistry

Pattern Recognition with Deep Learning in Quantum Materials Quantum ; Computer science ; Artificial intelligence ; Physics ; Quantum mechanics Machine Learning in Materials Science
Authors: Prakriti Kayastha | Raghunathan Ramakrishnan
Source: arXiv (Cornell University) (preprint, 2021-10-22)
Field: Materials Science — Materials Chemistry

Quantum Machine Learning Transition Probabilities in Electronic Excitation Spectra across Chemical Space: The Resolution-vs.-Accuracy Dilemma We present a new, high-veracity chemical space dataset -- bigQM7$\omega$ -- with 12,880 molecules containing up to 7 heavy atoms, and highlight the key challenges in quantum machine learning modeling of electronic excitation spectra. We show excited state modeling with global structural representations to suffer from information overload resulting in diminished structure-property mapping. To improve the signal-to-noise ratio in the modeling, we use locally integrated spectral intensities and highlight a resolution-vs.-accuracy dilemma. Intensities derived from transition probabilities enable quantifying the prediction errors through probabilistic confidence scores. Upon changing the basis set at the target density functional theory level, >75% confidence score is obtained only at the expense of the resolution, amounting to increasing uncertainties in peak positions. Compared to this, models with state-of-the-art structural representations trained only on <10% of the data recover the full electronic spectra of the remaining molecules with higher confidence even for sub-nm wavelength resolutions. Excited state ; Excitation ; Probabilistic logic ; Spectral line ; Resolution (logic) ; Quantum ; Dilemma ; Space (punctuation) ; Statistical physics ; Computer science ; Physics ; Atomic physics ; Mathematics ; Artificial intelligence ; Quantum mechanics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Computational Drug Discovery Methods
Authors: Jing Bi | Zhenyuan Gao | Victor Oancea
Source:  (preprint, 2021-09-10)
Field: Materials Science — Materials Chemistry

Artificial Neural Network and Recurrent Neural Network Approaches for Isotropic and Anisotropic Plastic Material Modeling Machine Learning (ML) and Digital Twins (DT) are at the heart of today’s different industries, ranging from advanced manufacturing to biomedical systems to resilient ecosystems, civil infrastructures, smart cities, and healthcare. They have become indispensable for solving complex problems in science, engineering, and technology development. The purpose of the MMLDT-CSET 2021 conference is to facilitate the transition of ML and DT from fundamental research to mainstream fields and technologies through advanced data science, mechanistic methods, and computational technologies. This 3-day conference features technical tracks of emerging ML-DT fields and applications, special public lectures, short courses, and demonstrations. The conference will be held in a hybrid format, featuring both on-site and virtual sessions. Artificial neural network ; Isotropy ; Anisotropy ; Artificial intelligence ; Computer science ; Physics ; Optics Machine Learning in Materials Science ; Manufacturing Process and Optimization ; Additive Manufacturing and 3D Printing Technologies
Authors: Ayana Ghosh | Dennis Trujillo | Subhashis Hazarika | Elizabeth Schiesser | Swamynathan, M. J. | Ghosh, Saurabh | Zhu, Jian-Xin | Nakhmanson, Serge
Source: arXiv (Cornell University) (preprint, 2021-08-27)
Field: Materials Science — Materials Chemistry

Identification of novel organic ferroelectrics: A study combining importance sampling with machine learning Recent advances in the synthesis of polar molecular materials have produced practical alternatives to ferroelectric ceramics, opening up exciting new avenues for their incorporation into modern electronic devices. However, in order to realize the full potential of polar polymer and molecular crystals for modern technological applications, it is paramount to assemble and evaluate all the available data for such compounds, identifying descriptors that could be associated with an emergence of ferroelectricity. In this work, we utilized data-driven approaches to judiciously shortlist candidate materials from a wide chemical space that could possess ferroelectric functionalities. An importance-sampling based method was utilized to address the challenge of having a limited amount of available data on already known organic ferroelectrics. Sets of molecular- and crystal-level descriptors were combined with a Random Forest Regression algorithm in order to predict spontaneous polarization of the shortlisted compounds. First-principles simulations were performed to further validate the predictions obtained from the machine learning model. Ferroelectricity ; Computer science ; Polarization (electrochemistry) ; Identification (biology) ; Polar ; Machine learning ; Organic molecules ; Sampling (signal processing) ; Chemical space ; Artificial intelligence ; Materials science ; Random forest ; Nanotechnology ; Molecule ; Chemistry ; Physics ; Organic chemistry Machine Learning in Materials Science ; Advanced Chemical Sensor Technologies ; Text and Document Classification Technologies
Authors: Dominik Lemm | Guido Falk von Rudorff | O. Anatole von Lilienfeld
Source: arXiv (Cornell University) (preprint, 2021-02-04)
Field: Materials Science — Materials Chemistry

Machine learning based energy-free structure predictions of molecules (closed and open-shell), transition states, and solids The computational prediction of atomistic structure is a long-standing problem in physics, chemistry, materials, and biology. Within conventional force-field or {\em ab initio} calculations, structure is determined through energy minimization, which is either approximate or computationally demanding. Alas, the accuracy-cost trade-off prohibits the generation of synthetic big data records with meaningful energy based conformational search and structure relaxation output. Exploiting implicit correlations among relaxed structures, our kernel ridge regression model, dubbed Graph-To-Structure (G2S), generalizes across chemical compound space, enabling direct predictions of relaxed structures for out-of-sample compounds, and effectively bypassing the energy optimization task. After training on constitutional and compositional isomers (no conformers) G2S infers atomic coordinates relying solely on stoichiometry and bond-network information as input (Our numerical evidence includes closed and open shell molecules, transition states, and solids). For all data considered, G2S learning curves reach mean absolute interatomic distance prediction errors of less than 0.2 Å for less than eight thousand training structures -- on par or better than popular empirical methods. Applicability test of G2S include meaningful structures of molecules for which standard methods require manual intervention, improved initial guesses for subsequent conventional {\em ab initio} based relaxation, and input for structural based representations commonly used in quantum machine learning models, (bridging the gap between graph and structure based models). Ab initio ; Energy minimization ; Relaxation (psychology) ; Computer science ; Open shell ; Force field (fiction) ; Statistical physics ; Algorithm ; Artificial intelligence ; Machine learning ; Computational chemistry ; Chemistry ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: James Dean
Source: D-Scholarship@Pitt (University of Pittsburgh) (article, 2021-06-13)
Field: Materials Science — Materials Chemistry

Computational Design of Optimal Bimetallic Nanoparticles: Bridging Stability with Adsorption In the 20th century, advancements in computational power and chemical theory revolutionized catalyst discovery. Now in the 21st century, machine learning techniques have started making their way into the toolbox of computational chemistry, accelerating material design and discovery. In this dissertation, we take a two-pronged approach to advance the current state-of-the-art when it comes to rational catalyst design. We begin by illuminating the fundamental physical properties relevant to the adsorption of small molecules to nanoparticles, arriving at a set of universal adsorption descriptors with the use of Density-Functional Theory calculations and machine learning. We then develop CE Expansion, a new open-source genetic algorithm for the rapid optimization of the bimetallic mixing behavior in nanoparticles of any size, shape, or composition, and use this to create the MetalNanoDB, a database of over 5,400 low-energy nanoparticles. Finally, we bring together these two approaches, and demonstrate a new workflow for the high-throughput screening of potential nanocatalysts for their physical properties, targeting CO2 adsorption as a proof-of-concept (relevant to mitigating the greenhouse effect). Overall, this work accelerates catalyst design by developing tools that rapidly and accurately model bimetallic nanocatalysts, and efficiently sieve the tremendously large nanomaterials space for targeted catalytic applications. Nanomaterial-based catalyst ; Bimetallic strip ; Nanotechnology ; Computer science ; Workflow ; Nanoparticle ; Rational design ; Adsorption ; Materials science ; Catalysis ; Chemistry Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Ryan Mohr | A. Avila | Soham Ghosh | Ananta Bhattarai | Muqiao Yang | Xintian Feng | Martin Head‐Gordon | Ruslan Salakhutdinov | Maria Fonoberova | Igor Mezić
Source: National Conference on Artificial Intelligence (article, 2021-01-01)
Field: Chemistry — Inorganic Chemistry

Combining Programmable Potentials and Neural Networks for Materials Problems. Computer science ; Artificial neural network ; Artificial intelligence Zeolite Catalysis and Synthesis ; Advanced Physical and Chemical Molecular Interactions ; X-ray Diffraction in Crystallography
Authors: B. O. Mukhamedov
Source: Data Archiving and Networked Services (DANS) (article, 2021-07-15)
Field: Materials Science — Materials Chemistry

Multicomponent Fe-Cr-based alloys. Machine learning We predicted the mixing enthalpy, Young’s modulus and Pugh’s ratio for over 0.5 million multicomponent Fe-Cr-based alloys. Artificial intelligence ; Metallurgy ; Computer science ; Materials science Metal Alloys Wear and Properties ; Advanced Materials Characterization Techniques ; Microstructure and Mechanical Properties of Steels
Authors: An S | Haotian Xue | Yuanbin She | Krishna Rajan
Source:  (preprint, 2021-09-24)
Field: Materials Science — Materials Chemistry

AI Informed Toxicity Screening of Amine Chemistries used in the Synthesis of Hybrid Organic-Inorganic Perovskites This paper describes a machine learning guided framework for screening the potential toxicity impact of amine chemistries used in the synthesis of hybrid organic-inorganic perovskites. Using a combination of a probabilistic molecular fingerprint technique that encodes bond connectivity (MinHash) coupled to non-linear data dimensionality reduction methods (UMAP), we develop an “Amine Atlas’. We show how the Amine Atlas can be used to rapidly screen the relative toxicity levels of amine molecules used in the synthesis of 2D and 3D perovskites and help identify safer alternatives. Our work also serves as a framework for rapidly identifying molecular similarity guided, structure-function relationships for safer materials chemistries that also incorporate sustainability/ toxicity concerns. Amine gas treating ; Chemistry ; SAFER ; Molecule ; Organic molecules ; Computer science ; Nanotechnology ; Combinatorial chemistry ; Biochemical engineering ; Materials science ; Organic chemistry ; Engineering Machine Learning in Materials Science ; Perovskite Materials and Applications ; Conducting polymers and applications
Authors: Onur Çaylak | Björn Baumeier
Source: ChemRxiv (preprint, 2021-05-24)
Field: Materials Science — Materials Chemistry

Machine Learning of Quasiparticle Energies in Molecules and Clusters &lt;div&gt; &lt;div&gt; &lt;div&gt; &lt;p&gt;We present a ∆-Machine Learning approach for the prediction of GW quasiparticle energies (∆MLQP) and photoelectron spectra of molecules and clusters, using orbital-sensitive graph-based representations in kernel ridge regression based supervised learning. Coulomb matrix, Bag-of-Bonds, and Bonds-Angles-Torsions representations are made orbital-sensitive by augmenting them with atom-centered orbital charges and Kohn–Sham orbital energies, which are both readily available from baseline calculations on the level of density-functional theory (DFT). We first illustrate the effects of different constructions of the orbital-sensitive representations (OSR) on the prediction of frontier orbital energies of 22K molecules of the QM8 dataset, and show that is is possible to predict the full photoelectron spectrum of molecules within the dataset using a single model with a mean-absolute error below 0.1eV. We further demonstrate that the OSR-based ∆MLQP captures the effects of intra- and intermolecular conformations in application to water monomers and dimers. Finally, we show that the approach can be embedded in multiscale simulation workflows, by studying the solvatochromic shifts of quasiparticle and electron-hole excitation energies of solvated acetone in a setup combining Molecular Dynamics, DFT, the GW approximation and the Bethe–Salpeter Equation. Our findings suggest that the ∆MLQP model allows to predict quasiparticle energies and photoelectron spectra of molecules and clusters with GW accuracy at DFT cost. &lt;/p&gt; &lt;/div&gt; &lt;/div&gt; &lt;/div&gt; Quasiparticle ; Density functional theory ; Physics ; Molecule ; HOMO/LUMO ; Molecular orbital ; Atomic physics ; Chemistry ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Tarak K. Patra | Debjyoti Bhattacharya | Ashwin Bale
Source: Bulletin of the American Physical Society (article, 2021-03-17)
Field: Materials Science — Materials Chemistry

Machine Learning of Phase Transitions and Dynamical Crossovers in Polymers Phase transition ; Polymer ; Phase (matter) ; Materials science ; Statistical physics ; Chemical physics ; Computer science ; Physics ; Condensed matter physics ; Quantum mechanics ; Composite material Machine Learning in Materials Science
Authors: Ke Liu | Nicolas Sadoune | N. Anand Rao | Jonas Greitemann | Lode Pollet
Source: Bulletin of the American Physical Society (article, 2021-03-15)
Field: Materials Science — Materials Chemistry

Revealing the Phase Diagram of Kitaev Materials by Machine Learning Phase diagram ; Phase (matter) ; Computer science ; Materials science ; Statistical physics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystal Structures and Properties
Authors: Michele Gandolfi | Alessandro Rognoni | Chiara Aieta | Riccardo Conte | Michele Ceotto
Source: arXiv (Cornell University) (preprint, 2021-01-11)
Field: Chemistry — Spectroscopy

Machine Learning for Vibrational Spectroscopy via Divide-and-Conquer Semiclassical Initial Value Representation Molecular Dynamics with Application to N-Methylacetamide A machine learning algorithm for partitioning the nuclear vibrational space into subspaces is introduced. The subdivision criterion is based on Liouville's theorem, i.e. best preservation of the unitary of the reduced dimensionality Jacobian determinant within each subspace along a probe full-dimensional classical trajectory. The algorithm is based on the idea of evolutionary selection and it is implemented through a probability graph representation of the vibrational space partitioning. We interface this customized version of genetic algorithms with our divide-and-conquer semiclassical initial value representation method for calculation of molecular power spectra. First, we benchmark the algorithm by calculating the vibrational power spectra of two model systems, for which the exact subspace division is known. Then, we apply it to the calculation of the power spectrum of methane. Exact calculations and full-dimensional semiclassical spectra of this small molecule are available and provide an additional test of the accuracy of the new approach. Finally, the algorithm is applied to the divide-and-conquer semiclassical calculation of the power spectrum of 12-atom trans-N-Methylacetamide. Divide and conquer algorithms ; Semiclassical physics ; Curse of dimensionality ; Algorithm ; Subspace topology ; Computer science ; Mathematics ; Statistical physics ; Physics ; Quantum mechanics ; Artificial intelligence ; Quantum Analytical Chemistry and Chromatography ; Advanced Chemical Physics Studies ; Molecular Spectroscopy and Structure
Authors: Filippo Federici | Yashasvi S. Ranawat
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2021-09-29)
Field: Materials Science — Materials Chemistry

Data for Machine learning for Materials Course at Aalto and UTU Maintained by Kunal Ghosh Course (navigation) ; Computer science ; Artificial intelligence ; Mathematics education ; Psychology ; Engineering Machine Learning in Materials Science
Authors: Yudai Iwamizu | Kota Suzuki | Masaaki Hirayama | Ryoji Kanno
Source: Japan Society of Powder and Powder Metallurgy (article, 2021-03-18)
Field: Materials Science — Materials Chemistry

Material Search of Lithium-Ion Conducting Oxides Using a Prediction of Ionic Conductivity by Chemical Composition Based Machine Learning Ionic conductivity ; Ion ; Lithium (medication) ; Chemical composition ; Composition (language) ; Conductivity ; Ionic bonding ; Materials science ; Inorganic chemistry ; Chemistry ; Machine learning ; Computer science ; Physical chemistry ; Psychology ; Electrolyte ; Electrode ; Organic chemistry Machine Learning in Materials Science