jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xiang Chen | Xinyan Liu | Xin Shen | Qiang Zhang
Source: Angewandte Chemie (article, 2021-07-01)
Field: Materials Science — Materials Chemistry

Applying Machine Learning to Rechargeable Batteries: From the Microscale to the Macroscale Abstract Emerging machine learning (ML) methods are widely applied in chemistry and materials science studies and have led to a focus on data‐driven research. This Minireview summarizes the application of ML to rechargeable batteries, from the microscale to the macroscale. Specifically, ML offers a strategy to explore new functionals for density functional theory calculations and new potentials for molecular dynamics simulations, which are expected to significantly enhance the challenging descriptions of interfaces and amorphous structures. ML also possesses a great potential to mine and unveil valuable information from both experimental and theoretical datasets. A quantitative “structure–function” correlation can thus be established, which can be used to predict the ionic conductivity of solids as well as the battery lifespan. ML also exhibits great advantages in strategy optimization, such as fast‐charge procedures. The future combination of multiscale simulations, experiments, and ML is also discussed and the role of humans in data‐driven research is highlighted. Microscale chemistry ; Battery (electricity) ; Nanotechnology ; Computer science ; Function (biology) ; Amorphous solid ; Focus (optics) ; Density functional theory ; Materials science ; Multiscale modeling ; Biochemical engineering ; Chemistry ; Physics ; Computational chemistry ; Engineering ; Mathematics ; Thermodynamics Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Materials and Technologies
Authors: Antonina L. Nazarova | Liqiu Yang | Kuang Liu | Ankit Mishra | Rajiv K. Kalia | Ken‐ichi Nomura | Aiichiro Nakano | Priya Vashishta | Pankaj Rajak
Source: Journal of Chemical Information and Modeling (article, 2021-04-19)
Field: Materials Science — Materials Chemistry

Dielectric Polymer Property Prediction Using Recurrent Neural Networks with Optimizations Despite the growing success of machine learning for predicting structure-property relationships in molecules and materials, such as predicting the dielectric properties of polymers, it is still in its infancy. We report on the effectiveness of solving structure-property relationships for a computer-generated database of dielectric polymers using recurrent neural network (RNN) models. The implementation of a series of optimization strategies was crucial to achieving high learning speeds and sufficient accuracy: (1) binary and nonbinary representations of SMILES (Simplified Molecular Input Line System) fingerprints and (2) backpropagation with affine transformation of the input sequence (ATransformedBP) and resilient backpropagation with initial weight update parameter optimizations (iRPROP<sup>-</sup> optimized). For the investigated database of polymers, the binary SMILES representation was found to be superior to the decimal representation with respect to the training and prediction performance. All developed and optimized Elman-type RNN algorithms outperformed nonoptimized RNN models in the efficient prediction of nonlinear structure-activity relationships. The average relative standard deviation (RSD) remained well below 5%, and the maximum RSD did not exceed 30%. Moreover, we provide a C++ codebase as a testbed for a new generation of open programming languages that target increasingly diverse computer architectures. Dielectric ; Property (philosophy) ; Artificial neural network ; Computer science ; Polymer ; Artificial intelligence ; Materials science ; Composite material ; Optoelectronics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Authors: Hongyu Chen | Shigeru Yamaguchi | Yuya Morita | Hiroyasu Nakao | Xiangning Zhai | Y. Shimizu | Harunobu Mitsunuma | Motomu Kanai
Source: Cell Reports Physical Science (article, 2021-12-01)
Field: Chemistry — Inorganic Chemistry

Data-driven catalyst optimization for stereodivergent asymmetric synthesis by iridium/boron hybrid catalysis Asymmetric catalysis enabling divergent control of multiple stereocenters remains challenging in synthetic organic chemistry. Although machine-learning-based optimization of molecular catalysis is a rapidly growing research field, the use of such approaches for catalyst design to achieve stereodivergent asymmetric synthesis producing multiple reaction outcomes, such as constitutional selectivity, diastereoselectivity, and enantioselectivity, is unprecedented. Here, we report the straightforward identification of asymmetric two-component iridium/boron hybrid catalyst systems for α-C-allylation of carboxylic acids. Structural optimization of the chiral ligands for iridium catalysts was driven by molecular-field-based regression analysis with a dataset containing overall 32 molecular structures. The catalyst systems enabled selective access to all the possible isomers of chiral carboxylic acids bearing contiguous stereocenters. This chemoselective and stereodivergent, asymmetric catalysis is applicable to late-stage structural modifications of drugs and their derivatives. Stereocenter ; Catalysis ; Iridium ; Enantioselective synthesis ; Combinatorial chemistry ; Chemistry ; Asymmetric hydrogenation ; Selectivity ; Organic chemistry Asymmetric Hydrogenation and Catalysis ; Chemical Synthesis and Analysis ; Advanced Synthetic Organic Chemistry
Authors: Valentín Vassilev-Galindo | Grégory Fonseca | Igor Poltavsky | Alexandre Tkatchenko
Source: The Journal of Chemical Physics (article, 2021-03-03)
Field: Materials Science — Materials Chemistry

Challenges for machine learning force fields in reproducing potential energy surfaces of flexible molecules Dynamics of flexible molecules are often determined by an interplay between local chemical bond fluctuations and conformational changes driven by long-range electrostatics and van der Waals interactions. This interplay between interactions yields complex potential-energy surfaces (PESs) with multiple minima and transition paths between them. In this work, we assess the performance of the state-of-the-art Machine Learning (ML) models, namely, sGDML, SchNet, Gaussian Approximation Potentials/Smooth Overlap of Atomic Positions (GAPs/SOAPs), and Behler-Parrinello neural networks, for reproducing such PESs, while using limited amounts of reference data. As a benchmark, we use the cis to trans thermal relaxation in an azobenzene molecule, where at least three different transition mechanisms should be considered. Although GAP/SOAP, SchNet, and sGDML models can globally achieve a chemical accuracy of 1 kcal mol<sup>-1</sup> with fewer than 1000 training points, predictions greatly depend on the ML method used and on the local region of the PES being sampled. Within a given ML method, large differences can be found between predictions of close-to-equilibrium and transition regions, as well as for different transition mechanisms. We identify key challenges that the ML models face mainly due to the intrinsic limitations of commonly used atom-based descriptors. All in all, our results suggest switching from learning the entire PES within a single model to using multiple local models with optimized descriptors, training sets, and architectures for different parts of the complex PES. Force field (fiction) ; Energy (signal processing) ; Computer science ; Nanotechnology ; Materials science ; Artificial intelligence ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Computational Drug Discovery Methods
Authors: Muhammed Shuaibi | Adeesh Kolluru | Abhishek Das | Aditya Grover | Anuroop Sriram | Zachary W. Ulissi | C. Lawrence Zitnick
Source: arXiv (Cornell University) (preprint, 2021-06-17)
Field: Materials Science — Materials Chemistry

Rotation Invariant Graph Neural Networks using Spin Convolutions Progress towards the energy breakthroughs needed to combat climate change can be significantly accelerated through the efficient simulation of atomic systems. Simulation techniques based on first principles, such as Density Functional Theory (DFT), are limited in their practical use due to their high computational expense. Machine learning approaches have the potential to approximate DFT in a computationally efficient manner, which could dramatically increase the impact of computational simulations on real-world problems. Approximating DFT poses several challenges. These include accurately modeling the subtle changes in the relative positions and angles between atoms, and enforcing constraints such as rotation invariance or energy conservation. We introduce a novel approach to modeling angular information between sets of neighboring atoms in a graph neural network. Rotation invariance is achieved for the network's edge messages through the use of a per-edge local coordinate frame and a novel spin convolution over the remaining degree of freedom. Two model variants are proposed for the applications of structure relaxation and molecular dynamics. State-of-the-art results are demonstrated on the large-scale Open Catalyst 2020 dataset. Comparisons are also performed on the MD17 and QM9 datasets. Computer science ; Invariant (physics) ; Rotation (mathematics) ; Convolution (computer science) ; Density functional theory ; Convolutional neural network ; Relaxation (psychology) ; Artificial neural network ; Statistical physics ; Theoretical computer science ; Algorithm ; Topology (electrical circuits) ; Artificial intelligence ; Physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Topic Modeling ; Advanced Graph Neural Networks
Authors: Erfan Fatehi | Hamidreza Yazdani Sarvestani | Behnam Ashrafi | A.H. Akbarzadeh
Source: Materials & Design (article, 2021-08-20)
Field: Materials Science — Materials Chemistry

Accelerated design of architectured ceramics with tunable thermal resistance via a hybrid machine learning and finite element approach Topologically interlocked architectures can transform brittle ceramics into tougher materials, while making the material design procedure a cumbersome task since modeling the whole architectural design space is not efficient and, to a degree, is not viable. We propose an approach to design architectured ceramics using machine learning (ML), trained by finite element analysis data and together with a self-learning algorithm, to discover high-performance architectured ceramics in thermomechanical environments. First, topologically interlocked panels are parametrically generated. Then, a limited number of designed architectured ceramics subjected to a thermal load is studied. Finally, the multilinear perceptron is employed to train the ML model in order to predict the thermomechanical performance of architectured panels with varied interlocking angles and number of blocks. The developed feed-forward artificial neural network framework can boost the architectured ceramic design efficiency and open up new avenues for controllability of the functionality for various high-temperature applications. This study demonstrates that the architectured ceramic panels with the ML-assisted engineered patterns show improvement up to 30% in frictional energy dissipation and 7% in the sliding distance of the tiles and 80% reduction in the strain energy, leading to a higher safety factor and the structural failure delay compared to the plain ceramics. Materials science ; Ceramic ; Finite element method ; Brittleness ; Mechanical engineering ; Composite material ; Structural engineering ; Engineering Machine Learning in Materials Science ; Advanced ceramic materials synthesis ; Thermal properties of materials
Authors: Saeed Arabha | Zahra Shokri Aghbolagh | Khashayar Ghorbani | S. Milad Hatam-Lee | Ali Rajabpour
Source: Journal of Applied Physics (article, 2021-12-06)
Field: Materials Science — Materials Chemistry

Recent advances in lattice thermal conductivity calculation using machine-learning interatomic potentials The accuracy of the interatomic potential functions employed in molecular dynamics (MD) simulation is one of the most important challenges of this technique. In contrast, the high accuracy ab initio quantum simulation cannot be an alternative to MD due to its high computational cost. In the meantime, the machine learning approach has been able to compromise these two numerical techniques. This work unveils how the MD interatomic potentials have been improved through training over ab initio datasets and are able to well calculate phononic thermal transport of materials. Therefore, this powerful tool allows the quantum computational order accuracy with a timescale in the order of classical computations. Besides, the thermal conductivity of a few 2D and 3D structures, which have been calculated using machine learning interatomic potentials (MLIPs), is presented and compared with experimental and quantum counterparts. Finally, it is discussed that how MLIPs can be developed not only to estimate other properties of pristine materials, such as mechanical properties, but also to predict the properties of defective materials. Interatomic potential ; Thermal conductivity ; Ab initio ; Computation ; Lattice (music) ; Molecular dynamics ; Statistical physics ; Quantum ; Ab initio quantum chemistry methods ; Work (physics) ; Thermal ; Computer science ; Materials science ; Physics ; Algorithm ; Computational chemistry ; Chemistry ; Thermodynamics ; Quantum mechanics ; Molecule Machine Learning in Materials Science ; Thermal properties of materials ; Fuel Cells and Related Materials
Authors: Micah Armstrong | Kai-Wei Lan | Yiwen Guo | Nicola H. Perry
Source: ACS Nano (article, 2021-05-27)
Field: Materials Science — Materials Chemistry

Dislocation-Mediated Conductivity in Oxides: Progress, Challenges, and Opportunities Dislocations in ionic solids are topological extended defects that modulate composition, strain, and charge over multiple length scales. As such, they provide an extra degree of freedom to tailor ionic and electronic transport beyond limits inherent in bulk doping. Heterogeneity of transport paths as well as the ability to dynamically reconfigure structure and properties through multiple stimuli lend dislocations to particular potential applications including memory, switching, non-Ohmic electronics, capacitive charge storage, and single-atom catalysis. However, isolating, understanding, and predicting causes of modified transport behavior remain a challenge. In this Perspective, we first review existing reports of dislocation-modified transport behavior in oxides, as well as synthetic strategies and multiscale characterization routes to uncover processing-structure-property relationships. We outline a vision for future research, suggesting outstanding questions, tasks, and opportunities. Advances in this field will require highly interdisciplinary, convergent computational-experimental approaches, covering orders of magnitude in length scale, and spanning fields from microscopy and machine learning to electro-chemo-mechanics and point defect chemistry to transport-by-design and advanced manufacturing. Nanotechnology ; Materials science ; Dislocation ; Ohmic contact ; Electronics ; Characterization (materials science) ; Engineering physics ; Computer science ; Chemical physics ; Physics ; Electrical engineering ; Engineering Electronic and Structural Properties of Oxides ; Advanced Memory and Neural Computing ; Semiconductor materials and devices
Authors: Yunzheng Wang | Jing Ning | Li Lu | Michel Bosman | Robert E. Simpson
Source: npj Computational Materials (article, 2021-11-11)
Field: Materials Science — Materials Chemistry

A scheme for simulating multi-level phase change photonics materials Abstract Chalcogenide phase change materials (PCMs) have been extensively applied in data storage, and they are now being proposed for high resolution displays, holographic displays, reprogrammable photonics, and all-optical neural networks. These wide-ranging applications all exploit the radical property contrast between the PCMs’ different structural phases, extremely fast switching speed, long-term stability, and low energy consumption. Designing PCM photonic devices requires an accurate model to predict the response of the device during phase transitions. Here, we describe an approach that accurately predicts the microstructure and optical response of phase change materials during laser induced heating. The framework couples the Gillespie Cellular Automata approach for modelling phase transitions with effective medium theory and Fresnel equations. The accuracy of the approach is verified by comparing the PCM’s optical response and microstructure evolution with the results of nanosecond laser switching experiments. We anticipate that this approach to simulating the switching response of PCMs will become an important component for designing and simulating programmable photonics devices. The method is particularly important for predicting the multi-level optical response of PCMs, which is important for all-optical neural networks and PCM-programmable perceptrons. Photonics ; Computer science ; Materials science ; Optical switch ; Chalcogenide ; Ranging ; Electronic engineering ; Optoelectronics ; Telecommunications ; Engineering Phase-change materials and chalcogenides ; Neural Networks and Reservoir Computing ; Advanced Memory and Neural Computing
Authors: Fabian L. Thiemann | P.N. Rowe | Andrea Zen | Erich A. Müller | Angelos Michaelides
Source: Nano Letters (article, 2021-09-14)
Field: Materials Science — Materials Chemistry

Defect-Dependent Corrugation in Graphene Graphene's intrinsically corrugated and wrinkled topology fundamentally influences its electronic, mechanical, and chemical properties. Experimental techniques allow the manipulation of pristine graphene and the controlled production of defects which allows one to control the atomic out-of-plane fluctuations and thus tune graphene's properties. Here, we perform large scale machine learning-driven molecular dynamics simulations to understand the impact of defects on the structure of graphene. We find that defects cause significantly higher corrugation leading to a strongly wrinkled surface. The magnitude of this structural transformation strongly depends on the defect concentration and specific type of defect. Analyzing the atomic neighborhood of the defects reveals that the extent of these morphological changes depends on the preferred geometrical orientation and the interactions between defects. While our work highlights that defects can strongly affect graphene's morphology, it also emphasizes the differences between distinct types by linking the global structure to the local environment of the defects. Graphene ; Materials science ; Atomic units ; Molecular dynamics ; Chemical physics ; Topological defect ; Nanotechnology ; Crystallographic defect ; Work (physics) ; Morphology (biology) ; Transformation (genetics) ; Graphene nanoribbons ; Condensed matter physics ; Chemistry ; Physics ; Computational chemistry Graphene research and applications ; Machine Learning in Materials Science ; Advanced Memory and Neural Computing
Authors: Svetoslav Nikolov | Mitchell A. Wood | Attila Cangi | Jean-Bernard Maillet | Mihai-Cosmin Marinica | Aidan P. Thompson | Michael P. Desjarlais | Julien Tranchida
Source: npj Computational Materials (article, 2021-09-28)
Field: Materials Science — Materials Chemistry

Data-driven magneto-elastic predictions with scalable classical spin-lattice dynamics Abstract A data-driven framework is presented for building magneto-elastic machine-learning interatomic potentials (ML-IAPs) for large-scale spin-lattice dynamics simulations. The magneto-elastic ML-IAPs are constructed by coupling a collective atomic spin model with an ML-IAP. Together they represent a potential energy surface from which the mechanical forces on the atoms and the precession dynamics of the atomic spins are computed. Both the atomic spin model and the ML-IAP are parametrized on data from first-principles calculations. We demonstrate the efficacy of our data-driven framework across magneto-structural phase transitions by generating a magneto-elastic ML-IAP for α -iron. The combined potential energy surface yields excellent agreement with first-principles magneto-elastic calculations and quantitative predictions of diverse materials properties including bulk modulus, magnetization, and specific heat across the ferromagnetic–paramagnetic phase transition. Spins ; Coupling (piping) ; Precession ; Surface (topology) ; Work (physics) ; Interatomic potential ; Dynamics (music) ; Molecular dynamics Machine Learning in Materials Science ; Quantum many-body systems ; Model Reduction and Neural Networks
Authors: Shuochen Zhao | Tianyun Cai | Liangshun Zhang | Weihua Li | Jiaping Lin
Source: ACS Macro Letters (article, 2021-04-27)
Field: Materials Science — Materials Chemistry

Autonomous Construction of Phase Diagrams of Block Copolymers by Theory-Assisted Active Machine Learning Equilibrium phase diagrams serve as blueprints for rational design of nanostructured materials of block copolymers, but their construction is time-consuming and requires profound expertise. Herein, by virtue of the knowledge of self-consistent field theory (SCFT), the active-learning method is developed to autonomously construct the phase diagrams of block copolymers. Without human intervention, the SCFT-assisted active-learning method can rapidly search the undetected phases and efficiently reproduce the complicated phase diagrams of diblock copolymers and multiblock terpolymers via decreasing the number of sampling points to about 20%. It is clearly demonstrated that the combined uncertainty sampling/random selection scheme in the active-learning method shows the outperformance in spite of a small amount of initial data set. This work highlights the promising integration of theoretical modeling with machine learning and represents a crucial step toward rational design of nanostructured materials. Copolymer ; Computer science ; Phase diagram ; Block (permutation group theory) ; Active learning (machine learning) ; Blueprint ; Set (abstract data type) ; Sampling (signal processing) ; Block diagram ; Construct (python library) ; Phase (matter) ; Artificial intelligence ; Theoretical computer science ; Materials science ; Mathematics ; Polymer ; Engineering ; Mechanical engineering ; Programming language ; Chemistry Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Ferroelectric and Negative Capacitance Devices
Authors: Véronique Van Speybroeck | Sander Vandenhaute | Alexander E. J. Hoffman | Sven M. J. Rogge
Source: Trends in Chemistry (article, 2021-05-18)
Field: Chemistry — Inorganic Chemistry

Towards modeling spatiotemporal processes in metal–organic frameworks Metal-organic framework ; Environmental science ; Computer science ; Environmental resource management ; Earth science ; Geology ; Chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography