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| 1 | Machine learning in materials informatics:recent applications and prospects显示文摘Propelled partly by the Materials Genome Initiative,and partly by the algorithmic developments and the resounding successes of data-driven efforts in other domains,informatics strategies are beginning to take shape within materials science.These approaches lead to surrogate machine learning models that enable rapid predictions based purely on past data rather than by direct experimentation or by computations/simulations in which fundamental equations are explicitly solved.Data-centric informatics methods are becoming useful to determine material properties that are hard to measure or compute using traditional methods—due to the cost,time or effort involved—but for which reliable data either already exists or can be generated for at least a subset of the critical cases.Predictions are typically interpolative,involving fingerprinting a material numerically first,and then following a mapping(established via a learning algorithm)between the fingerprint and the property of interest.Fingerprints,also referred to as“descriptors”,may be of many types and scales,as dictated by the application domain and needs.Predictions may also be extrapolative—extending into new materials spaces—provided prediction uncertainties are properly taken into account.This article attempts to provide an overview of some of the recent successful data-driven“materials informatics”strategies undertaken in the last decade,with particular emphasis on the fingerprint or descriptor choices.The review also identifies some challenges the community is facing and those that should be overcome in the near future. | Rampi Ramprasad Rohit Batra Ghanshyam Pilania Arun Mannodi-Kanakkithodi Chiho Kim | 2017 | npj Computational Materials2017，，1： | 40 |
| 2 | A universal strategy for the creation of machine learning-based atomistic force fields显示文摘Emerging machine learning(ML)-based approaches provide powerful and novel tools to study a variety of physical and chemical problems.In this contribution,we outline a universal strategy to create ML-based atomistic force fields,which can be used to perform high-fidelity molecular dynamics simulations.This scheme involves(1)preparing a big reference dataset of atomic environments and forces with sufficiently low noise,e.g.,using density functional theory or higher-level methods,(2)utilizing a generalizable class of structural fingerprints for representing atomic environments,(3)optimally selecting diverse and nonredundant training datasets from the reference data,and(4)proposing various learning approaches to predict atomic forces directly(and rapidly)from atomic configurations.From the atomistic forces,accurate potential energies can then be obtained by appropriate integration along a reaction coordinate or along a molecular dynamics trajectory.Based on this strategy,we have created model ML force fields for six elemental bulk solids,including Al,Cu,Ti,W,Si,and C,and show that all of them can reach chemical accuracy.The proposed procedure is general and universal,in that it can potentially be used to generate ML force fields for any material using the same unified workflow with little human intervention.Moreover,the force fields can be systematically improved by adding new training data progressively to represent atomic environments not encountered previously. | Tran Doan Huan Rohit Batra James Chapman Sridevi Krishnan Lihua Chen Rampi Ramprasad | 2017 | npj Computational Materials2017，，1： | 10 |
| 3 | Solving the electronic structure problem with machine learning显示文摘Simulations based on solving the Kohn-Sham(KS)equation of density functional theory(DFT)have become a vital component of modern materials and chemical sciences research and development portfolios.Despite its versatility,routine DFT calculations are usually limited to a few hundred atoms due to the computational bottleneck posed by the KS equation.Here we introduce a machine-learning-based scheme to efficiently assimilate the function of the KS equation,and by-pass it to directly,rapidly,and accurately predict the electronic structure of a material or a molecule,given just its atomic configuration.A new rotationally invariant representation is utilized to map the atomic environment around a grid-point to the electron density and local density of states at that grid-point.This mapping is learned using a neural network trained on previously generated reference DFT results at millions of grid-points.The proposed paradigm allows for the high-fidelity emulation of KS DFT,but orders of magnitude faster than the direct solution.Moreover,the machine learning prediction scheme is strictly linear-scaling with system size. | Anand Chandrasekaran Deepak Kamal Rohit Batra Chiho Kim Lihua Chen Rampi Ramprasad | 2019 | npj Computational Materials2019，，1： | 10 |
| 4 | Frequency-dependent dielectric constant prediction of polymers using machine learning显示文摘The dielectric constant(ϵ)is a critical parameter utilized in the design of polymeric dielectrics for energy storage capacitors,microelectronic devices,and high-voltage insulations.However,agile discovery of polymer dielectrics with desirableϵremains a challenge,especially for high-energy,high-temperature applications.To aid accelerated polymer dielectrics discovery,we have developed a machine-learning(ML)-based model to instantly and accurately predict the frequency-dependentϵof polymers with the frequency range spanning 15 orders of magnitude.Our model is trained using a dataset of 1210 experimentally measuredϵvalues at different frequencies,an advanced polymer fingerprinting scheme and the Gaussian process regression algorithm. | Lihua Chen Chiho Kim Rohit Batra Jordan P.Lightstone Chao Wu Zongze Li Ajinkya A.Deshmukh Yifei Wang Huan D.Tran Priya Vashishta Gregory A.Sotzing Yang Cao Rampi Ramprasad | 2020 | npj Computational Materials2020，，1： | 2 |
| 5 | The expression of Fhit protein is related Inversely to disease progression on inpa- tients with breast carcinoma 显示文摘 | Gatalica Z Lele SM Rampy BA' | 2000 | Cancer2000，88，6： | 1 |
| 6 | The expression of FHIT protein is related inversely to disease progression in patient with breast carcinoma显示文摘 | GATALICA Z LELE S M RAMPY B A | 2000 | Cancer2000，88，6： | 1 |
| 7 | New polymorphisms in the IL-10 promoter region 显示文摘 | D'Alfonso S Rampi M Rolando V | 2000 | Genes Immun2000，1，3： | 1 |
| 8 | Alkanethiol self-assembled monolayers as the dielectric of capacitors with nanoscale thickness 显示文摘 | Rampi M A Schueller 0 J A Whitesides G M | 1998 | Appl Phys Lett1998，73，： | 1 |
| 9 | The expression of FHIT protein is related inversely to disease progression on inpatients with breast carcinoma显示文摘 | Gatalica Z Lele SM Rampy BA | 2000 | Cancer2000，88，6： | 1 |
| 10 | The expression of Fhit protein is related inversely to disease progression in patients with breast carcinoma显示文摘 | Gatalica Z Lele SM Rampy BA | 2000 | Cancer2000，88，6： | 1 |
| 11 | The expression of Fhit protein is related inversely to disease progression in patient with breast carcinoma显示文摘 | Gatalica Z Ide S M Rampy B A | 2000 | Cancer2000，88，6： | 1 |
| 12 | The expression of fhit protein is related inversely to disease progression in patients with breast carcinoma显示文摘 | Gatalica Z Lele SM Rampy BA | 2000 | Cancer2000，88，6： | 1 |
| 13 | The expression of Fhit protein is related inversely to disease progressionin patients with breast carcinoma显示文摘 | GATALICA Z LELE S M RAMPY B A | 2000 | Cancer2000，88，6： | 1 |
| 14 | The expression of Fhit protein is related inversely to disease progression in patients with breast carcinoma显示文摘 | Gatalicae Z Lele SM Rampy BA | 2000 | Cancer2000，88，6： | 1 |
| 15 | The expression of Fhit protein is related inversely to disease progression in patientswitb breast carcinoma显示文摘 | Gatalica Z Lele SM Rampy BA | 2000 | Cancer2000，88，6： | 1 |
| 16 | Novel expression patterns of PI3K/Akt/mTOR signaling pathway components in colorectal cancer显示文摘 | Johnson SM Gulhati P Rampy BA | | 0，，05： | 1 |
| 17 | The expression of FHIT protein is related inversely to disease progression on inpatients with breast carcinoma 显示文摘 | Gatalica Z Lele SM Rampy BA | 2000 | Cancer2000，88，6： | 1 |
| 18 | Keratinocyte growth factor-2 ac- celerates wound healing in incisional wounds 显示文摘 | Jimenez PA Rampy MA | 1999 | J Surg Res1999，81，： | 1 |
| 19 | The expression of Fhit protein is related inversely to disease progression in patients with breast carcinoma显示文摘 | Gatalica Z Lele SM Rampy BA | 2000 | Cancer2000，88，： | 1 |
| 20 | Novel Expression Patterns of PI3K/Akt/mTOR Signaling Pathway Components in Colorectal Cancer显示文摘 | Sara M. Johnson Pat Gulhati Bill A. Rampy Yimei Han Piotr G. Rychahou Hung Q. Doan Heidi L. Weiss B. Mark Evers | 2010 | Journal of the American College of Surgeons2010，，5： | 1 |
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