Results 11 to 20 of about 101,057 (252)

Siamese foundation models for crystal structure prediction

Nature Communications
Predicting crystal structures from chemical compositions is a fundamental challenge in materials discovery, complicated by complex 3D geometries that distinguish it from fields like protein folding.
Liming Wu, Wenbing Huang, Rui Jiao, Jianxing Huang, Liwei Liu, Yipeng Zhou, Hao Sun, Yang Liu, Fuchun Sun, Yuxiang Ren, Ji-Rong Wen   +10 more
doaj   +3 more sources

Generative Adversarial Networks for Crystal Structure Prediction [PDF]

ACS Central Science, 2020
Sungwon Kim, Juhwan Noh, Geun Ho Gu, Alan Aspuru-Guzik, Yousung Jung   +4 more
doaj   +2 more sources

Correction to “Generative Adversarial Networks for Crystal Structure Prediction” [PDF]

ACS Central Science, 2022
Sungwon Kim, Juhwan Noh, Geun Ho Gu, Alan Aspuru-Guzik, Yousung Jung   +4 more
doaj   +2 more sources

Theoretical prediction of a crystal structure. [PDF]

Proceedings of the National Academy of Sciences, 1996
The diffusion equation method of global minimization is applied to compute the crystal structure of S6, with no a priori knowledge about the system. The experimental lattice parameters and positions and orientations of the molecules in the unit cell are predicted correctly.
Wawak, Ryszard, Gibson, Kenneth, Józef Adam Liwo, Harold A. Scheraga   +3 more
openaire   +3 more sources

Crystal structure prediction at finite temperatures

npj Computational Materials, 2023
Crystal structure prediction is a central problem of crystallography and materials science, which until mid-2000s was considered intractable. Several methods, based on either energy landscape exploration or, more commonly, global optimization, largely ...
Ivan A. Kruglov, Alexey V. Yanilkin, Yana Propad, Arslan B. Mazitov, Pavel Rachitskii, Artem R. Oganov   +5 more
doaj   +1 more source

Sulfamethoxazole: Molecular docking and crystal structure prediction

Results in Chemistry, 2023
The conformational stability of sulfonamide derivatives was examined in the active site of the Dihydropteroate synthase enzyme. The best-docked conformer was identified via the molecular docking approach.
David Stephen Arputharaj, Meenashi Rajasekaran, P.V. Nidhin   +2 more
doaj   +1 more source

Structure prediction of crystals, surfaces and nanoparticles [PDF]

Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2020
We review the current techniques used in the prediction of crystal structures and their surfaces and of the structures of nanoparticles. The main classes of search algorithm and energy function are summarized, and we discuss the growing role of methods based on machine learning.
Woodley, SM, Day, GM, Catlow, R
openaire   +4 more sources

Crystal Structure Prediction of Energetic Materials [PDF]

Crystal Growth & Design, 2023
The synthesis and experimental testing of energetic materials can be hazardous, but their many industrial and military applications necessitate their constant research and development. We evaluate computational methods for predicting the crystal structures of energetic molecular organic crystals from their molecular structure as a first step in ...
Joseph E. Arnold, Graeme M. Day
openaire   +1 more source

Hybrid algorithm of Bayesian optimization and evolutionary algorithm in crystal structure prediction

Science and Technology of Advanced Materials: Methods, 2022
We propose a highly efficient searching algorithm in crystal structure prediction. The searching algorithm is a hybrid of the evolutionary algorithm and Bayesian optimization. The evolutionary algorithm is widely used in crystal structure prediction, and
Tomoki Yamashita, Hiori Kino, Koji Tsuda, Takashi Miyake, Tamio Oguchi   +4 more
doaj   +1 more source

The seventh blind test of crystal structure prediction: structure generation methods. [PDF]

Acta Crystallogr B Struct Sci Cryst Eng Mater
Hunnisett LM, Nyman J, Francia N, Abraham NS, Adjiman CS, Aitipamula S, Alkhidir T, Almehairbi M, Anelli A, Anstine DM, Anthony JE, Arnold JE, Bahrami F, Bellucci MA, Bhardwaj RM, Bier I, Bis JA, Boese AD, Bowskill DH, Bramley J, Brandenburg JG, Braun DE, Butler PWV, Cadden J, Carino S, Chan EJ, Chang C, Cheng B, Clarke SM, Coles SJ, Cooper RI, Couch R, Cuadrado R, Darden T, Day GM, Dietrich H, Ding Y, DiPasquale A, Dhokale B, van Eijck BP, Elsegood MRJ, Firaha D, Fu W, Fukuzawa K, Glover J, Goto H, Greenwell C, Guo R, Harter J, Helfferich J, Hofmann DWM, Hoja J, Hone J, Hong R, Hutchison G, Ikabata Y, Isayev O, Ishaque O, Jain V, Jin Y, Jing A, Johnson ER, Jones I, Jose KVJ, Kabova EA, Keates A, Kelly PF, Khakimov D, Konstantinopoulos S, Kuleshova LN, Li H, Lin X, List A, Liu C, Liu YM, Liu Z, Liu ZP, Lubach JW, Marom N, Maryewski AA, Matsui H, Mattei A, Mayo RA, Melkumov JW, Mohamed S, Momenzadeh Abardeh Z, Muddana HS, Nakayama N, Nayal KS, Neumann MA, Nikhar R, Obata S, O'Connor D, Oganov AR, Okuwaki K, Otero-de-la-Roza A, Pantelides CC, Parkin S, Pickard CJ, Pilia L, Pivina T, Podeszwa R, Price AJA, Price LS, Price SL, Probert MR, Pulido A, Ramteke GR, Rehman AU, Reutzel-Edens SM, Rogal J, Ross MJ, Rumson AF, Sadiq G, Saeed ZM, Salimi A, Salvalaglio M, Sanders de Almada L, Sasikumar K, Sekharan S, Shang C, Shankland K, Shinohara K, Shi B, Shi X, Skillman AG, Song H, Strasser N, van de Streek J, Sugden IJ, Sun G, Szalewicz K, Tan BI, Tan L, Tarczynski F, Taylor CR, Tkatchenko A, Tom R, Tuckerman ME, Utsumi Y, Vogt-Maranto L, Weatherston J, Wilkinson LJ, Willacy RD, Wojtas L, Woollam GR, Yang Z, Yonemochi E, Yue X, Zeng Q, Zhang Y, Zhou T, Zhou Y, Zubatyuk R, Cole JC.   +154 more
europepmc   +2 more sources